Výsledky vyhledávání - clasificacion ams::65 numerical analysis::65y computer aspects of numerical algorithms*

Método de inducción de reglas de clasificación oblicuas mediante un algoritmo evolutivo ; Oblique classification rule induction method using an evolutionary algorithm

Autoři: Álvarez Macías, José Luis Mata Vázquez, Jacinto Riquelme Santos, José Cristóbal

Zdroj: Revista Colombiana de Computación; Vol. 3 Núm. 1 (2002): Revista Colombiana de Computación; 7-20

Témata: Innovaciones tecnológicas, Ciencia de los computadores, Desarrollo de tecnología, Ingeniería de sistemas, Investigaciones, Tecnologías de la información y las comunicaciones, TIC´s, Technological innovations, Computer science, Technology development, Systems engineering, Investigations, Information and communication technologies, ICT's, Data mining, Supervised learning, Classification, Evolutionary algorithms, Desarrollo tecnológico, Ciencias de la computación, Tecnologías de la información y la comunicación, Investigación, Minería de datos, Aprendizaje supervisado, Clasificación, Algoritmos evolutivos

Popis souboru: application/pdf

Relation: https://revistas.unab.edu.co/index.php/rcc/article/view/1105/1077; https://revistas.unab.edu.co/index.php/rcc/article/view/1105; [1) D. Ackley. A Connectionist Machine for Genetic Hillclimbing. Kluver Acad. Pub. 19871; (2) C.L. Blake y C.J Merz. fhttp://www.ics.uci.edu/-fmlearn/MLRepository.htmlI. Irvine, CA: University of California, Department of Information and Computer Science. 1998.; Breiman; Friedman; Olshen y Stone. Classification and Regression Trees. Wadsworth International Group. 1984.; M.S. Chen; J. Han y P.S. Yu. Data Mining: An Overview from Database perspective.; K.A. De Jong. An analysis of the behavoir of a class of genetic adaptive systems. PhD thesis, University of Michigan. 1975; K.A. De Jong. Using Genetic Algorithms for Concepts Learning. Machine Learning, 13 161-188, 1993.; L.J. Eshelman y J.D. Schaffer. Real-Coded Genetic Algorithms and Interval Schemata. Foundations of Genetic Algorithms 2. Morgan Kaufmann Pub. 1993. Fayyad; Fayyad; Piatetsky-Shapiro y Smyth. From Data Mining to Knowledge Discovery: An Overview. Advances in Knowledge Discovery and Data Mining, 1-34, 1996.; D.E. Goldberg Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Pub. Company, inc., 1989.; 10) S. Hampson y D. Volper. Linear function neurons: Structure and training. Biological Cybernetics, 53, 203-217, 1986.; 111 J.H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI. 1975. [; 12) C.Z. Janikov. A Knowleged-Intensive Genetic Algorithm for Supervised Learning. Ma-chine Learning, 13 189-228, 1993.; J.R. Koza. Concept Formation and Decision Thee Induction using the Genetic Program-ming Paradimg. Springer-Verlag, 1991.; [14) Z. Michalewicz. Genetics Algorithms + Data Structures = Evolution Programs, Third Edition. Springer-Verlag, 1999.; M. Mitchell. An Introduction to Genetic Algorithms. MIT Press. 1996.; S.K. Murthy; S. Kasif y S. Salzberg. A System for Induction of Obliques Decision Tress. Journal or Artificial Intelligence Research, 2 1-32, 1994.; J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann Pub., 1993.; J.R. Quinlan. Improved use of continuous attributes in C4.5. Journal of Artificial Intel-ligence Research, vol. 4, pp 77-90. 1996.; G. Syswerda. Uniform crossover in Genetic Algorithms. 3th Int. Conference on Genetic Algorithms, 2-9, 1989; P.E. Utgoff y C.E. Brodley. An incremental method for finding multivariate splits for decision trees. 7th International Conference on Machine Learning, 58-65, 1990.; T. Van de Merckt. NFDT: A system that learns flexible concepts based on decision trees for numerical attributes. 9th Int. Workshop on Machine Learning, pp. 322-331, 1992.; T. Van de Merckt. Decision trees in numerical attribute spaces. 13th International Joint Conference on Artificial Intelligence, pp. 1016-1021, 1993.; D. Whitley. A Genetic Algorithm DatoriaL Technical Report CS-93-103. Colorado State University. 1993. A.H. Wright. Genetic Algorithm for Real Parameter Optimization. Morgan Kaufmann Pub., 1991.; http://hdl.handle.net/20.500.12749/9062; instname:Universidad Autónoma de Bucaramanga UNAB; repourl:https://repository.unab.edu.co

Dostupnost: https://hdl.handle.net/20.500.12749/9062

Mecanismo de clasificación de paisajes de optimización basado en muestreo multiescala y aprendizaje automático ; Optimization landscape classification mechanism based on multi-scale sampling and machine learning

Autoři: Rodríguez Hernández, Angie Patricia Melgarejo Rey, Miguel Alberto

Přispěvatelé: Rodríguez Hernández, Angie Patricia Melgarejo Rey, Miguel Alberto

Témata: Análisis de paisajes de optimización, Muestreo multiescala, Aprendizaje automático, Redes neuronales convolucionales, Optimización, Maestría en Ciencias de la Información y las Comunicaciones -- Tesis y Disertaciones Académicas, Arquitectura del paisaje -- Clasificación, Arquitectura del paisaje -- Muestreo, Autoaprendizaje -- Técnicas, Optimization landscape analisys, Multi-scale sampling, Machine learning, Convolutional neural networks, Optimization

Popis souboru: pdf; application/pdf; application/zip

Relation: Ryan Dieter Lang and Andries Petrus Engelbrecht. An Exploratory Landscape Analysis-Based Benchmark Suite. Algorithms 2021, Vol. 14, Page 78, 14(3):78, 2 2021.; Hideo KANEMITSU, Hideaki IMAI, and Masaaki MIYASKOSHI. Definitions and Properties of (Local) Minima and Multimodal Functions using Level Set for Continuous Optimization Problems. IEICE Proceeding Series, 2:94–97, 3 2014.; Wikimedia Commons. Typical CNN architecture, 12 2015.; Hadj Ahmed Bouarara. A survey of computational intelligence algorithms and their applications. Handbook of Research on Soft Computing and Nature-Inspired Algorithms, pages 133–176, 3 2017.; Michel Gendreau and Jean-Yves Potvin. Handbook of Metaheuristics, volume 272 of International Series in Operations Research & Management Science. Springer Interna- tional Publishing, Cham, 2019.; Aaron Courville Heaton Jeff Ian Goodfellow, Yoshua Bengio. Deep learning, volume 19. MIT Press, 2018.; Hossin M and Sulaiman M.N. A Review on Evaluation Metrics for Data Classification Evaluations. International Journal of Data Mining & Knowledge Management Process, 5(2):01–11, 3 2015.; Jacob Cohen. A coefficient of agreement for nominal scales. Educational and Psycho- logical Measurement, 1960; Zhilu Zhang and Mert R. Sabuncu. Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels. Advances in Neural Information Processing Systems, 2018-December:8778–8788, 5 2018.; Jon T. Selvik and Eirik B. Abrahamsen. On the meaning of accuracy and precision in a risk analysis context. http://dx.doi.org/10.1177/1748006X16686897, 231(2):91–100, 1 2017.; minimize(method=Powell) SciPy v1.14.1 Manual.; minimize(method=Nelder-Mead) SciPy v1.14.1 Manual.; M. S. Bazaraa, John J. Jarvis, and Hanif D. Sherali. Linear programming and network flows. 2011.; David H Wolpert and William G Macready. No Free Lunch Theorems for Optimization. Technical Report 1, 1997.; Luis R. Izquierdo, José Manuel Galán Ordax, José I. Santos, and Ricardo Del Ol- mo Martínez. Modelado de sistemas complejos mediante simulación basada en agentes y mediante dinámica de sistemas. Empiria. Revista de metodología de ciencias sociales, 0(16):85, 10 2008.; John R. Rice. The Algorithm Selection Problem. Advances in Computers, 15(C):65– 118, 1 1976.; Ryan Dieter Lang. Landscape analysis-based automated algorithm selection. PhD thesis, Stellenbosch University, 2024.; Thomas Back, David B. Fogel, and Zbigniew Michalewicz. Handbook of Evolutionary Computation. CRC Press, 1 1997.; Kate A. Smith-Miles. Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys, 41(1), 12 2008.; David B. Fogel. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence %7C IEEE eBooks %7C IEEE Xplore. 2006.; Katherine Mary Malan. A Survey of Advances in Landscape Analysis for Optimisation. Algorithms 2021, Vol. 14, Page 40, 14(2):40, 1 2021.; G. M. Ostrovsky, Ye M. Mikhailova, and T. A. Berezhinsky. Optimization of large-scale complex systems. International Journal of Systems Science, 17(8):1121–1132, 2017.; Olaf Mersmann, Bernd Bischl, Heike Trautmann, Mike Preuss, Claus Weihs, and Gün- ter Rudolph. Exploratory landscape analysis. In Genetic and Evolutionary Compu- tation Conference, GECCO’11, pages 829–836, New York, New York, USA, 2011. ACM Press.; Pascal Kerschke and Heike Trautmann. Automated Algorithm Selection on Continuous Black-Box Problems by Combining Exploratory Landscape Analysis and Machine Lear- ning. Evolutionary Computation, 27(1):99–127, 3 2019.; Mario A. Muñoz, Michael Kirley, and Saman K. Halgamuge. Exploratory landscape analysis of continuous space optimization problems using information content. IEEE Transactions on Evolutionary Computation, 19(1):74–87, 2 2015.; Kent C.B. Steer, Andrew Wirth, and Saman K. Halgamuge. Information theoretic classification of problems for metaheuristics. In Lecture Notes in Computer Scien- ce (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 5361 LNAI, pages 319–328, 2008.; V. K. Vassilev, T. C. Fogarty, and J. F. Miller. Information characteristics and the structure of landscapes. Evolutionary computation, 8(1):31–60, 2000.; Mario Andrés Muñoz, Michael Kirley, and Kate Smith-Miles. Analyzing randomness ef- fects on the reliability of exploratory landscape analysis. Natural Computing, 21(2):131– 154, 6 2022.; Bas van Stein, Fu Xing Long, Moritz Frenzel, Peter Krause, Markus Gitterle, and Thomas Bäck. DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis. GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion, pages 515–518, 3 2023.; Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor. An Introduction to Statistical Learning. 2023.; Irene Moser and Marius Gheorghita. Combining search space diagnostics and optimi- sation. 2012 IEEE Congress on Evolutionary Computation, CEC 2012, 2012.; Anja Jankovic and Carola Doerr. Adaptive landscape analysis. GECCO 2019 Com- panion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion, pages 2032–2035, 7 2019.; Ye Tian, Shichen Peng, Xingyi Zhang, Tobias Rodemann, Kay Chen Tan, and Yaochu Jin. A Recommender System for Metaheuristic Algorithms for Continuous Optimi- zation Based on Deep Recurrent Neural Networks. IEEE Transactions on Artificial Intelligence, 1(1):5–18, 8 2020.; William Gilpin. Chaos as an interpretable benchmark for forecasting and data-driven modelling. 10 2021.; Raphael Patrick Prager, Moritz Vinzent Seiler, Heike Trautmann, and Pascal Kersch- ke. Automated Algorithm Selection inăSingle-Objective Continuous Optimization: A Comparative Study ofăDeep Learning andăLandscape Analysis Methods. Lecture No- tes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13398 LNCS:3–17, 2022.; Wenfei Liu, Jingcheng Wei, and Qingmin Meng. Comparisions on KNN, SVM, BP and the CNN for Handwritten Digit Recognition. Proceedings of 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2020, pages 587–590, 8 2020.; Abdurrahim Yilmaz, Ali Anil Demircali, Sena Kocaman, and Huseyin Uvet. Compari- son of Deep Learning and Traditional Machine Learning Techniques for Classification of Pap Smear Images. 9 2020.; Pin Wang, En Fan, and Peng Wang. Comparative analysis of image classification algo- rithms based on traditional machine learning and deep learning. Pattern Recognition Letters, 141:61–67, 1 2021.; Ragav Venkatesan and Baoxin Li. Convolutional Neural Networks in Visual Computing: A Concise Guide. CRC Press, 10 2017.; Stephen Boyd and Lieven Vandenberghe. Convex Optimization. 2004.; Vincent Hénaux, Adrien Goëffon, and Frédéric Saubion. Evolving Fitness Landscapes with Complementary Fitness Functions. Artificial Evolution, pages 110–120, 10 2019.; Peter F. Stadler. Fitness landscapes. Biological Evolution and Statistical Physics, pages 183–204, 10 2002.; Mario A. Muñoz, Michael Kirley, and Saman K. Halgamuge. A meta-learning predic- tion model of algorithm performance for continuous optimization problems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7491 LNCS(PART 1):226–235, 2012.; G. H. Weiss. Aspects and applications of the random walk. Journal of Statistical Physics, 79(1):497–500, 4 1995.; George C. Montgomery, Douglas C.; Runger. Applied Statistics and Probability for Engineers. 2011.; Momin Jamil and Xin-She Yang. A Literature Survey of Benchmark Functions For Global Optimization Problems. International Journal of Mathematical Modelling and Numerical Optimisation, 4(2):150–194, 8 2013.; Feng Zou, Debao Chen, Hui Liu, Siyu Cao, Xuying Ji, and Yan Zhang. A survey of fitness landscape analysis for optimization. Neurocomputing, 503:129–139, 9 2022.; Jeffrey Horn and David E. Goldberg. Genetic Algorithm Difficulty and the Modality of Fitness Landscapes. 3:243–269, 1 1995.; Erik Pitzer, Michael Affenzeller, and Andreas Beham. A closer look down the basins of attraction. 2010 UK Workshop on Computational Intelligence, UKCI 2010, 2010.; Christian Hanster and Pascal Kerschke. Flaccogui: Exploratory landscape analysis for everyone. In GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 1215–1222. Association for Computing Machinery, Inc, 7 2017.; Pascal Kerschke, Mike Preuss, Carlos Hernández, Oliver Schütze, Jian Qiao Sun, Chris- tian Grimme, Günter Rudolph, Bernd Bischl, and Heike Trautmann. Cell mapping techniques for exploratory landscape analysis. Advances in Intelligent Systems and Computing, 288:115–131, 2014.; Katherine M. Malan and Andries P. Engelbrecht. Quantifying ruggedness of continuous landscapes using entropy. In IEEE Congress on Evolutionary Computation, CEC 2009, pages 1440–1447, 2009.; Alroomi Website - Unconstrained.; Index AMPGO 0.1.0 documentation.; Micheal F. Shlesinger, George M. Zaslavsky, and Uriel Frisch. Lévy Flights and Related Topics in Physics. 450, 1995.; Jean Philippe Bouchaud and Antoine Georges. Anomalous diffusion in disordered media: Statistical mechanisms, models and physical applications. Physics Reports, 195(4-5):127–293, 11 1990.; Masato S. Abe. Functional advantages of Lévy walks emerging near a critical point. Proceedings of the National Academy of Sciences of the United States of America, 117(39):24336–24344, 9 2020.; Rosario N. Mantegna and H. Eugene Stanley. Stochastic Process with Ultraslow Convergence to a Gaussian: The Truncated Lévy Flight. Physical Review Letters, 73(22):2946–2949, 1994.; G. M. Viswanathan, Sergey V. Buldyrev, Shlomo Havlin, M. G.E. Da Luz, E. P. Raposo, and H. Eugene Stanley. Optimizing the success of random searches. Nature 1999 401:6756, 401(6756):911–914, 10 1999.; Ralf Metzler and Joseph Klafter. The restaurant at the end of the random walk: Recent developments in the description of anomalous transport by fractional dynamics. Journal of Physics A, 2004.; Albert-László Barabási and Réka Albert. Emergence of Scaling in Random Networks. Mat. Res. Soc. Symp. Proc, 74:677, 1995.; J. A. Nelder and R. Mead. A Simplex Method for Function Minimization. The Com- puter Journal, 7(4):308–313, 1 1965.; M. J. D. Powell. An efficient method for finding the minimum of a function of several variables without calculating derivatives. The Computer Journal, 7(2):155–162, 1 1964.; S Kirkpatrick; C D Gelatt, and; M P Vecchi. Optimization by Simulated Annealing. New Series, 220(4598):671–680, 1983.; Zewen Li, Fan Liu, Wenjie Yang, Shouheng Peng, and Jun Zhou. A Survey of Convo- lutional Neural Networks: Analysis, Applications, and Prospects. IEEE Transactions on Neural Networks and Learning Systems, 33(12):6999–7019, 12 2022.; D. H. Hubel and T. N. Wiesel. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology, 160(1):106, 1 1962.; Douglas M. Hawkins. The Problem of Overfitting. Journal of Chemical Information and Computer Sciences, 44(1):1–12, 1 2004.; Kunihiko Fukushima. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4):193–202, 4 1980.; Siddharth Sharma, Simone Sharma, and Anidhya Athaiya. Activation Functions in Neural Networks. International Journal of Engineering Applied Sciences and Techno- logy, 04(12):310–316, 5 2020.; Diederik P. Kingma and Jimmy Lei Ba. Adam: A Method for Stochastic Optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 12 2014.; Houda Bichri, Adil Chergui, and Mustapha Hain. Image Classification with Transfer Learning Using a Custom Dataset: Comparative Study. Procedia Computer Science, 220:48–54, 1 2023.; Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. MobileNets: Efficient Convo- lutional Neural Networks for Mobile Vision Applications. arXiv, 4 2017.; Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang Chieh Chen. MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pa- ges 4510–4520, 1 2018.; Davide Chicco and Giuseppe Jurman. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1):1–13, 1 2020.; C. Ferri, J. Hernández-Orallo, and R. Modroiu. An experimental comparison of per- formance measures for classification. Pattern Recognition Letters, 30(1):27–38, 1 2009.; Sylvain Arlot and Alain Celisse. A survey of cross-validation procedures for model selection. https://doi.org/10.1214/09-SS054, 4(none):40–79, 1 2010.; Rafael C. Gonzalez and Richard E. Woods. Digital Image Processing, Global Edition. Person Education, pages 19–44, 2018.; Karl Pearson. III. Contributions to the mathematical theory of evolution. Philosophical Transactions of the Royal Society of London. (A.), 185:71–110, 12 1894.; Nguyen Xuan Vinh, Julien Epps, and James Bailey. Information theoretic measures for clusterings comparison: Is a correction for chance necessary? ACM International Conference Proceeding Series, 382, 2009.; Zhou Wang, Alan Conrad Bovik, Hamid Rahim Sheikh, and Eero P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600–612, 4 2004.; Chris Thornton, Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown. Auto- WEKA: Combined Selection and Hyperparameter Optimization of Classification Al- gorithms. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Part F128815:847–855, 8 2012.; Frank Hutter, Holger Hoos, and Kevin Leyton-Brown. An Efficient Approach for Assessing Hyperparameter Importance, 1 2014.; James Bergstra, James Bergstra@umontreal Ca, and Yoshua Bengio@umontreal Ca. Random Search for Hyper-Parameter Optimization Yoshua Bengio. Journal of Machi- ne Learning Research, 13:281–305, 2012.; Daniel Horn and Bernd Bischl. Multi-objective parameter configuration of machine learning algorithms using model-based optimization. 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, 2 2017.; Renan Netto, Sheiny Fabre, Tiago Augusto Fontana, Vinicius Livramento, Laer- cio L. Pilla, Laleh Behjat, and Jose Luis Guntzel. Algorithm Selection Framework for Legalization Using Deep Convolutional Neural Networks and Transfer Learning. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 41(5):1481–1494, 5 2022.; Itai Dagan, Roman Vainshtein, Gilad Katz, and Lior Rokach. Automated algorithm selection using meta-learning and pre-trained deep convolution neural networks. In- formation Fusion, 105:102210, 5 2024.; S; Xu, W; Liu, C; Wu, J Li, Siyi Xu, Wenwen Liu, Chengpei Wu, and Junli Li. CNN-HT: A Two-Stage Algorithm Selection Framework. Entropy 2024, Vol. 26, Page 262, 26(3):262, 3 2024.; Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, Dinani Amorim, and Amo- rim Fernández-Delgado. Do we Need Hundreds of Classifiers to Solve Real World Classification Problems? Journal of Machine Learning Research, 15:3133–3181, 2014.; Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, and Ilya Sutskever. Deep Double Descent: Where Bigger Models and More Data Hurt. 8th International Conference on Learning Representations, ICLR 2020, 12 2019.; Yaxin Li, Jing Liang, Kunjie Yu, Ke Chen, Yinan Guo, Caitong Yue, and Leiyu Zhang. Adaptive local landscape feature vector for problem classification and algorithm selec- tion. Applied Soft Computing, 131:109751, 12 2022.; Moritz Vinzent Seiler, Pascal Kerschke, and Heike Trautmann. Deep-ELA: Deep Ex- ploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems. 1 2024.; Yaxin Li, Jing Liang, Kunjie Yu, Caitong Yue, and Yingjie Zhang. Keenness for characterizing continuous optimization problems and predicting differential evolution algorithm performance. Complex and Intelligent Systems, 9(5):5251–5266, 10 2023.; Kangjing Li, Saber Elsayed, Ruhul Sarker, and Daryl Essam. Multiple landscape measure-based approach for dynamic optimization problems. Swarm and Evolutionary Computation, 88:101578, 7 2024.; Gaper Petelin, Gjorgjina Cenikj, and Tome Eftimov. TinyTLA: Topological landscape analysis for optimization problem classification in a limited sample setting. Swarm and Evolutionary Computation, 84:101448, 2 2024.; Vojtech Uher and Pavel Kromer. Impact of Different Discrete Sampling Strategies on Fitness Landscape Analysis Based on Histograms. ACM International Conference Proceeding Series, 9, 12 2023.; Jakob Bossek. Smoof: Single-and Multi-Objective Optimization Test Functions. The R Journal, 9, 2017.; GitHub - ciren/benchmarks: A collection of n-dimensional functions.; Zhen Zhang, Kuo Yang, Jinwu Qian, and Lunwei Zhang. Real-time surface EMG pattern recognition for hand gestures based on an artificial neural network. Sensors (Switzerland), 19(14), 7 2019.; Yi Zhao, Tongfeng Weng, and Defeng Huang. Lévy walk in complex networks: An effi- cient way of mobility. Physica A: Statistical Mechanics and its Applications, 396:212– 223, 2 2014.; Xin She Yang and Suash Deb. Multiobjective cuckoo search for design optimization. Computers & Operations Research, 40(6):1616–1624, 6 2013.; Hemanth. levy(n,m,beta), 2019.; Jorge; Nocedal and Stephen J. Wrigh. Numerical Optimization. Springer Series in Operations Research and Financial Engineering. Springer New York, 2006.; Saket Gupta, Narendra Kumar, Laxmi Srivastava, Hasmat Malik, Alberto Pliego Ma- rugán, and Fausto Pedro García Márquez. A Hybrid JayaPowells Pattern Search Algo- rithm for Multi-Objective Optimal Power Flow Incorporating Distributed Generation. Energies 2021, Vol. 14, Page 2831, 14(10):2831, 5 2021.; M. D. Buhmann. Michael J.D. Powells work in approximation theory and optimisation. Journal of Approximation Theory, 238:3–25, 2 2019.; Marco A. Luersen and Rodolphe Le Riche. Globalized NelderMead method for engi- neering optimization. Computers & Structures, 82(23-26):2251–2260, 9 2004.; Nitish Shirish Keskar, Jorge Nocedal, Ping Tak Peter Tang, Dheevatsa Mudigere, and Mikhail Smelyanskiy. On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings, 9 2016.; Mariana Medina Muñoz. Método para la síntesis de paisajes de optimización bidimen- sionales basado en redes adversarias generativas, 2024.; Juan Pablo López Guevara. Método de síntesis de funciones de prueba para optimiza- dores basado en autocodificadores variacionales y regresión simbólica, 2024.; Miguel Melgarejo, Mariana Medina, Juan Lopez, and Angie Rodriguez. Optimization test function synthesis with generative adversarial networks and adaptive neuro-fuzzy systems. Information Sciences, 686:121371, 1 2024.; https://hdl.handle.net/11349/94020

Dostupnost: https://hdl.handle.net/11349/94020

Desarrollo de un sistema de clasificación de sustancias basado en un arreglo de sensores tipo lengua electrónica ; Development of a system for classifying substances based on an electronic tongue-type sensor array

Autoři: Leon Medina, Jersson Xavier Tibaduiza Burgos, Diego Alexander Anaya Vejar, Maribel a další

Přispěvatelé: Leon Medina, Jersson Xavier Tibaduiza Burgos, Diego Alexander Anaya Vejar, Maribel a další

Témata: 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería, Lengua electrónica, Extracción de características, Reducción de dimensionalidad, Selección de características, Aprendizaje de máquina, Clasificación, Sustancias líquidas, Arreglo de sensores, Voltametría, Amperometría, Validación cruzada, Manifold learning, Electronic tongue, Feature extraction, Dimensionality reduction, Feature selection, Machine learning, Classification, Sensor array, Voltammetry, Amperometry, Cross validation

Popis souboru: 241 páginas; application/pdf

Relation: “Harvey d (2010) analytical chemistry 2.0." https://chem.libretexts.org/Bookshelves/Analytical_Chemistry/Supplemental_Modules_(Analytical_Chemistry)/Instrumental_Analysis/Cyclic_Voltammetry. Accessed: 2021-01-24.; T. Nasir, Electrochemical sensors of environmental pollutants based on carbon electrodes modified by ordered mesoporous silica. PhD thesis, Université de Lorraine, 2018.; J. X. Leon-Medina, W. A. Pineda-Muñoz, and D. A. T. Burgos, “Joint distribution adaptation for drift correction in electronic nose type sensor arrays," IEEE Access, vol. 8, pp. 134413-134421, 2020.; L. Wang, Q. Niu, Y. Hui, and H. Jin, “Discrimination of rice with different pretreatment methods by using a voltammetric electronic tongue," Sensors (Switzerland), vol. 15, no. 7, pp. 17767-17785, 2015.; M. Sliwinska, P. Wisniewska, T. Dymerski, J. Namiesnik, and W. Wardencki, “Food analysis using artificial senses," Journal of Agricultural and Food Chemistry, vol. 62, no. 7, pp. 1423-1448, 2014.; H. Jiang, M. Zhang, B. Bhandari, and B. Adhikari, “Application of electronic tongue for fresh foods quality evaluation: A review," Food Reviews International, vol. 34, no. 8, pp. 746-769, 2018.; C. Costa, C. Taiti, M. C. Strano, G. Morone, F. Antonucci, S. Mancuso, S. Claps, F. Pallottino, L. Sepe, N. Bazihizina, and P. Menesatti, Multivariate Approaches to Electronic Nose and PTR-TOF-MS Technologies in Agro-Food Products. 2016.; D. Ha, Q. Sun, K. Su, H. Wan, H. Li, N. Xu, F. Sun, L. Zhuang, N. Hu, and P. Wang, “Recent achievements in electronic tongue and bioelectronic tongue as taste sensors," vol. 207, pp. 1136-1146, 2015.; M. Esteban, C. Ariño, and J. M. Díaz-Cruz, “Chemometrics in electroanalytical chemistry," Crit. Rev. Anal. Chem., vol. 36, pp. 295-313, 2006.; P. Oliveri, M. C. Casolino, and M. Forina, Chemometric Brains for Artificial Tongues, vol. 61. Elsevier Inc., 1 ed., 2010.; M. del Valle, “Materials for electronic tongues: Smart sensor combining different materials and chemometric tools," in Materials for Chemical Sensing, pp. 227-265, Springer, 2017.; L. Zhang and F. C. Tian, “A new kernel discriminant analysis framework for electronic nose recognition," Analytica Chimica Acta, vol. 816, pp. 8-17, 2014.; L. Zhang, X. Wang, G. B. Huang, T. Liu, and X. Tan, “Taste Recognition in E-Tongue Using Local Discriminant Preservation Projection," IEEE Trans. Cybern., vol. PP, pp. 1-14, 2018.; L. Zhang, F. Tian, and D. Zhang, E-Nose Algorithms and Challenges. Springer Singapore, 2018.; J. X. Leon-Medina, L. J. Cardenas-Flechas, and D. A. Tibaduiza, “A data-driven methodology for the classification of different liquids in artificial taste recognition applications with a pulse voltammetric electronic tongue," International Journal of Distributed Sensor Networks, vol. 15, no. 10, p. ., 2019.; M. del Valle, “Electronic tongues employing electrochemical sensors," Electroanalysis, vol. 22, pp. 1539-1555, jul 2010.; J. X. Leon-Medina, M. A. Vejar, and D. A. Tibaduiza, “Signal Processing and Pattern Recognition in Electronic Tongues: A Review," in Pattern Recognition Applications in Engineering (D. A. T. Burgos, M. A. Vejar, and F. Pozo, eds.), pp. 84-108, Hershey, PA, USA: IGI Global, 2020.; S. Y. Tian, S. P. Deng, and Z. X. Chen, “Multifrequency large amplitude pulse voltammetry: A novel electrochemical method for electronic tongue," Sensors and Actuators, B: Chemical, vol. 123, no. 2, pp. 1049-1056, 2007.; Z. Wei, J. Wang, and W. Jin, “Evaluation of varieties of set yogurts and their physical properties using a voltammetric electronic tongue based on various potential waveforms," Sensors and Actuators B: Chemical, vol. 177, pp. 684-694, 2013.; P. Ivarsson, S. Holmin, N.-E. Höjer, C. Krantz-Rülcker, and F. Winquist, “Discrimination of tea by means of a voltammetric electronic tongue and different applied waveforms," Sensors and Actuators B: Chemical, vol. 76, no. 1-3, pp. 449-454, 2001.; Z. Wei, J. Wang, and L. Ye, “Classification and prediction of rice wines with different marked ages by using a voltammetric electronic tongue," Biosensors and bioelectronics, vol. 26, no. 12, pp. 4767-4773, 2011.; M. Palit, B. Tudu, P. K. Dutta, A. Dutta, A. Jana, J. K. Roy, N. Bhattacharyya, R. Bandyopadhyay, and A. Chatterjee, “Classification of black tea taste and correlation with tea taster's mark using voltammetric electronic tongue," IEEE Transactions on Instrumentation and Measurement, vol. 59, no. 8, pp. 2230-2239, 2009.; Z. Wei and J. Wang, “Classification of monofloral honeys by voltammetric electronic tongue with chemometrics method," Electrochimica acta, vol. 56, no. 13, pp. 4907-4915, 2011.; A. Gutes, F. Cespedes, M. Del Valle, D. Louthander, C. Krantz-Rülcker, and F. Winquist, “A ow injection voltammetric electronic tongue applied to paper mill industrial waters," Sensors and Actuators B: Chemical, vol. 115, no. 1, pp. 390-395, 2006.; T. Liu, Y. Chen, D. Li, T. Yang, and J. Cao, “Electronic tongue recognition with feature specificity enhancement," Sensors, vol. 20, no. 3, p. 772, 2020.; T. Liu, Y. Chen, D. Li, and M. Wu, “An Active Feature Selection Strategy for DWT in Artificial Taste," Journal of Sensors, vol. 2018, 2018.; X. Wang and K. K. Paliwal, “Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition," Pattern recognition, vol. 36, no. 10, pp. 2429-2439, 2003.; L. F. Villamil Cubillos, “Implementación y validación de algoritmos de selección de características en el proceso de clasificación de líquidos con arreglos de sensores tipo lengua electrónica," tech. rep., Trabajo de grado. Programa de Ingeniería mecatrónica. Universidad Nacional de Colombia sede Bogotá, 2020.; J. X. Leon-Medina, M. Anaya, F. Pozo, and D. Tibaduiza, “Nonlinear feature extraction through manifold learning in an electronic tongue classification task," Sensors, vol. 20, no. 17, 2020.; I. Levner, “Feature selection and nearest centroid classification for protein mass spectrometry," BMC bioinformatics, vol. 6, no. 1, p. 68, 2005.; J. Yan, X. Guo, S. Duan, P. Jia, L. Wang, C. Peng, and S. Zhang, “Electronic nose feature extraction methods: A review," Sensors, vol. 15, no. 11, pp. 27804-27831, 2015.; G. Sugihara, R. May, H. Ye, C.-h. Hsieh, E. Deyle, M. Fogarty, and S. Munch, “Detecting causality in complex ecosystems," science, vol. 338, no. 6106, pp. 496-500, 2012.; Y. Huang, G. Kou, and Y. Peng, “Nonlinear manifold learning for early warnings in financial markets," European Journal of Operational Research, vol. 258, no. 2, pp. 692-702, 2017.; D. Lunga, S. Prasad, M. M. Crawford, and O. Ersoy, “Manifold-learning-based feature extraction for classification of hyperspectral data: A review of advances in manifold learning," IEEE Signal Processing Magazine, vol. 31, no. 1, pp. 55-66, 2013.; K. Yildiz, A. Y. Çamurcu, and B. Dogan, “Comparison of dimension reduction techniques on high dimensional datasets.," Int. Arab J. Inf. Technol., vol. 15, no. 2, pp. 256-262, 2018.; J. X. Leon, W. A. P. MuÑOz, M. Anaya, J. Vitola, and D. A. Tibaduiza, “Structural damage classification using machine learning algorithms and performance measures," Structural Health Monitoring 2019, 2019.; D. Agis and F. Pozo, “A frequency-based approach for the detection and classification of structural changes using t-sne," Sensors, vol. 19, no. 23, p. 5097, 2019.; V. D. Silva and J. B. Tenenbaum, “Global versus local methods in nonlinear dimensionality reduction," in Advances in neural information processing systems, pp. 721-728, 2003.; F. Plastria, S. De Bruyne, and E. Carrizosa, “Dimensionality reduction for classification, comparison of techniques and dimension choice," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5139 LNAI, pp. 411-418, 2008.; P. Jia, T. Huang, L. Wang, S. Duan, J. Yan, and L. Wang, “A novel pre-processing technique for original feature matrix of electronic nose based on supervised locality preserving projections," Sensors, vol. 16, no. 7, p. 1019, 2016.; P. Zhu, J. Du, B. Xu, and M. Lu, “Modified unsupervised discriminant projection with an electronic nose for the rapid determination of chinese mitten crab freshness," Analytical Methods, vol. 9, no. 11, pp. 1806-1815, 2017.; L. Ding, Z. Guo, S. Pan, and P. Zhu, “Manifold learning for dimension reduction of electronic nose data," in 2017 International Conference on Control, Automation and Information Sciences (ICCAIS), pp. 169-174, IEEE, 2017.; P. Zhu, Y. Zhang, and L. Ding, “Rapid freshness prediction of crab based on a portable electronic nose system," International Journal of Computer Applications in Technology, vol. 61, no. 4, pp. 241-246, 2019.; J. Leon-Medina, M. Anaya, F. Pozo, and D. Tibaduiza, “Application of manifold learning algorithms to improve the classification performance of an electronic nose," in 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, IEEE, 2020.; R. Zhi, L. Zhao, B. Shi, and Y. Jin, “New dimensionality reduction model (manifold learning) coupled with electronic tongue for green tea grade identification," European Food Research and Technology, vol. 239, no. 1, pp. 157-167, 2014.; M. Liu, M. Wang, J. Wang, and D. Li, “Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar," Sensors and Actuators, B: Chemical, vol. 177, pp. 970-980, 2013.; J. M. Gutiérrez, Z. Haddi, A. Amari, B. Bouchikhi, A. Mimendia, X. Cetó, and M. del Valle, “Hybrid electronic tongue based on multisensor data fusion for discrimination of beers," Sensors and Actuators B: Chemical, vol. 177, pp. 989-996, 2013.; J. X. Leon-Medina, M. A. Vejar, and D. A. Tibaduiza, “Signal processing and pattern recognition in electronic tongues: A review," in Pattern Recognition Applications in Engineering, pp. 84-108, IGI Global, 2020.; S. Zhang, R. He, J. Zhang, Z. Zhou, X. Cheng, G. Huang, J. Zhang, et al., “A convolutional neural network based auto features extraction method for tea classification with electronic tongue," Applied Sciences, vol. 9, no. 12, p. 2518, 2019.; J. X. Leon-Medina, L. J. Cardenas-Flechas, and D. A. Tibaduiza, “A data-driven methodology for the classification of different liquids in artificial taste recognition applications with a pulse voltammetric electronic tongue," International Journal of Distributed Sensor Networks, vol. 15, no. 10, p. 1550147719881601, 2019.; Q. Shi, T. Guo, T. Yin, Z. Wang, C. Li, X. Sun, Y. Guo, and W. Yuan, “Classification of pericarpium citri reticulatae of different ages by using a voltammetric electronic tongue system," International Journal Of Electrochemical Science, vol. 13, no. 12, pp. 11359-11374, 2018.; M. Palit, B. Tudu, N. Bhattacharyya, A. Dutta, P. K. Dutta, A. Jana, R. Bandyopadhyay, and A. Chatterjee, “Comparison of multivariate preprocessing techniques as applied to electronic tongue based pattern classification for black tea," Analytica Chimica Acta, vol. 675, no. 1, pp. 8-15, 2010.; Y. Vlasov, A. Legin, A. Rudnitskaya, C. Di Natale, and A. D'amico, “Nonspecific sensor arrays (.electronic tongue") for chemical analysis of liquids (IUPAC Technical Report)," Pure and Applied Chemistry, vol. 77, no. 11, pp. 1965-1983, 2005.; A. Bratov, N. Abramova, and A. Ipatov, “Recent trends in potentiometric sensor arrays-a review," Anal. Chim. Acta, vol. 678, pp. 149-159, 2010.; Z. Wei, Y. Yang, J. Wang, W. Zhang, and Q. Ren, “The measurement principles, working parameters and configurations of voltammetric electronic tongues and its applications for foodstuff analysis," J. Food Eng., vol. 217, pp. 75-92, 2018.; C. G. Zoski, Handbook of electrochemistry. Elsevier, 2006.; “Real academia de ingeniería (2021) electroanálisis." http://diccionario.raing.es/es/lema/electroan%C3%A1lisis. Accessed: 2021-04-12.; D. J. Munch, M. Wasko, E. Flynt, S. C. Wendelken, J. Scifres, J. R. Mario, M. Hunt, D. Gregg, T. Schaeffer, M. Clarage, et al., “Validation and peer review of us environmental protection agency chemical methods of analysis," in Forum on Environmental Measurements, US Environmental Protection Agency, Washington, DC, Citeseer, 2005.; A. Wilken, V. Kraft, S. Girod, M. Winter, and S. Nowak, “A fluoride-selective electrode (fse) for the quantification of fluoride in lithium-ion battery (lib) electrolytes," Analytical methods, vol. 8, no. 38, pp. 6932-6940, 2016.; V. Pravdová, M. Pravda, and G. Guilbault, “Role of chemometrics for electrochemical sensors," Analytical letters, vol. 35, no. 15, pp. 2389-2419, 2002.; M. Holmberg, M. Eriksson, C. Krantz-Rülcker, T. Artursson, F. Winquist, A. Lloyd-Spetz, and I. Lundström, “2nd workshop of the second network on artificial olfactory sensing (nose ii)," Sensors and Actuators B: Chemical, vol. 101, no. 1-2, pp. 213-223, 2004.; I. Campos Sánchez, Sensores electroquímicos tipo lengua electrónica voltamétrica aplicados al control medioambiental ya la industria alimentaria. PhD thesis, Universitat Politècnica de Valencia, 2013.; M. del Valle, “Electronic tongues employing electrochemical sensors," Electroanalysis, vol. 22, no. 14, pp. 1539-1555, 2010.; M. del Valle, “Potentiometric electronic tongues applied in ion multidetermination," Comprehensive Analytical Chemistry, vol. 49, pp. 721-753, 2007.; J. Wang, Study of Electrode Reactions and Interfacial Properties, ch. 2, pp. 29-66. Analytical Electrochemistry John Wiley Sons, Ltd, 2006.; M. M. Cuenca Quicazan, Desarrollo de una herramienta instrumental de gusto artificial aplicable a bebidas alcohólicas a base de miel de abejas. PhD thesis, Universidad Nacional de Colombia sede Bogotá, 2014.; Q. Ouyang, Y. Yang, J. Wu, Z. Liu, X. Chen, C. Dong, Q. Chen, Z. Zhang, and Z. Guo, “Rapid sensing of total theaflavins content in black tea using a portable electronic tongue system coupled to efficient variables selection algorithms," Journal of Food Composition and Analysis, vol. 75, pp. 43-48, 2019.; Q. Ouyang, Y. Yang, J. Wu, Q. Chen, Z. Guo, and H. Li, “Measurement of total free amino acids content in black tea using electronic tongue technology coupled with chemometrics," LWT, vol. 118, p. 108768, 2020.; E. M. Fuentes Perez, Aplicación de la lengua electrónica voltamétrica a alimentos líquidos. PhD thesis, Universitat Politècnica de Valencia, 2017.; E. Baldeon Chamorro, Desarrollo de la técnica de lengua electrónica voltamétrica para la determinación de la capacidad antioxidante total de extractos de plantas y frutas peruanas. PhD thesis, Universitat Politècnica de Valencia, 2015.; M. A. Fillol, Diseño de un sistema de lengua electrónica basado en técnicas electroquímicas voltamétricas y su aplicación en el ámbito agroalimentario. PhD thesis, Universitat Politècnica de Valencia, 2011.; F. Winquist, P. Wide, and I. Lundström, “An electronic tongue based on voltammetry," Anal. Chim. Acta, vol. 357, pp. 21-31, 1997.; Z. Wei, J. Wang, and W. Jin, “Evaluation of varieties of set yogurts and their physical properties using a voltammetric electronic tongue based on various potential waveforms," Sens. Actuators, B, vol. 177, pp. 684-694, 2013.; C. A. Nunes, V. O. Alvarenga, A. de Souza Sant'Ana, J. S. Santos, and D. Granato, “The use of statistical software in food science and technology: Advantages, limitations and misuses," Food Research International, vol. 75, pp. 270-280, 2015.; P. C. Jurs, G. A. Bakken, and H. E. McClelland, “Computational methods for the analysis of chemical sensor array data from volatile analytes," Chemical Reviews, vol. 100, no. 7, pp. 2649-2678, 2000.; R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification. John Wiley & Sons, 2012.; D. A. Tibaduiza, L. E. Mujica, and J. Rodellar, “Damage classification in structural health monitoring using principal component analysis and self-organizing maps," Structural Control and Health Monitoring, vol. 20, no. 10, pp. 1303-1316, 2013.; M. Anaya, D. A. Tibaduiza, and F. Pozo, “A bioinspired methodology based on an artificial immune system for damage detection in structural health monitoring," Shock and Vibration, vol. 501, p. 648097, 2015.; J. A. Westerhuis, T. Kourti, and J. F. MacGregor, “Comparing alternative approaches for multivariate statistical analysis of batch process data," Journal of Chemometrics, vol. 13, no. 3-4, pp. 397-413, 1999.; F. Pozo, Y. Vidal, and Ó. Salgado, “Wind turbine condition monitoring strategy through multiway PCA and multivariate inference," Energies, vol. 11, no. 4, pp. 1-19, 2018.; M. Anaya, D. A. Tibaduiza, and F. Pozo, “Detection and classification of structural changes using artificial immune systems and fuzzy clustering," International Journal of Bio-Inspired Computation, vol. 9, no. 1, pp. 35-52, 2017.; D. Agis, D. A. Tibaduiza, and F. Pozo, “Vibration-based detection and classification of structural changes using principal component analysis and-distributed stochastic neighbor embedding," Structural Control and Health Monitoring, vol. 27, no. 6, p. e2533, 2020.; J. M. Gutiérrez, L. Moreno-Barón, F. Céspedes, R. Munoz, and M. del Valle, “Resolution of heavy metal mixtures from highly overlapped asv voltammograms employing a wavelet neural network," Electroanal, vol. 21, no. 3-5, pp. 445-451, 2009.; Z. Haddi, H. Alami, N. El Bari, M. Tounsi, H. Barhoumi, A. Maaref, N. Jaffrezic-Renault, and B. Bouchikhi, “Electronic nose and tongue combination for improved classification of Moroccan virgin olive oil profiles," Food Research International, vol. 54, no. 2, pp. 1488-1498, 2013.; K. Thongpull, Advancing the automated design of integrated intelligent multi-sensory systems with self-X properties. PhD thesis, Technischen Universität Kaiserslautern, 2016.; S. Ayesha, M. K. Hanif, and R. Talib, “Overview and comparative study of dimensionality reduction techniques for high dimensional data," Information Fusion, vol. 59, pp. 44-58, 2020.; S. Wold, K. Esbensen, and P. Geladi, “Principal component analysis," Chemometrics and intelligent laboratory systems, vol. 2, no. 1-3, pp. 37-52, 1987.; H. Abdi and L. J. Williams, “Principal component analysis," Wiley interdisciplinary reviews: computational statistics, vol. 2, no. 4, pp. 433-459, 2010.; R. Bro and A. K. Smilde, “Principal component analysis," Analytical Methods, vol. 6, no. 9, pp. 2812-2831, 2014.; D.-A. Tibaduiza, M.-A. Torres-Arredondo, L. Mujica, J. Rodellar, and C.-P. Fritzen, “A study of two unsupervised data driven statistical methodologies for detecting and classifying damages in structural health monitoring," Mechanical Systems and Signal Processing, vol. 41, no. 1-2, pp. 467-484, 2013.; M. Anaya, D. A. Tibaduiza, and F. Pozo, “Detection and classification of structural changes using artificial immune systems and fuzzy clustering," International Journal of Bio-Inspired Computation, vol. 9, no. 1, p. 35, 2017.; D. Tibaduiza, L. Mujica, and J. Rodellar, “Damage classification in structural health monitoring using principal component analysis and self-organizing maps," Structural Control and Health Monitoring, vol. 20, no. 10, pp. 1303-1316, 2013.; M. Anaya Vejar, Design and validation of structural health monitoring system based on bio-inspired algorithms. PhD thesis, Universitat Politècnica de Catalunya, 2016.; B. M. Wise, N. B. Gallagher, R. Bro, J. M. Shaver, W. Windig, and R. S. Koch, “PLS Toolbox 3.5 for use with Matlab. ," Manson, WA: Eigenvector Research Inc, 2005.; M. Anaya, D. Tibaduiza, and F. Pozo, “Data driven methodology based on artificial immune systems for damage detection," in 7th European Workshop on Structural Health Monitoring. La Cité, Nantes, France, July 8-11, 2014.; D. A. Tibaduiza Burgos, Design and validation of a structural health monitoring system for aeronautical structures. PhD thesis, Universitat Politècnica de Catalunya, 2013.; J. Vitola, F. Tibadui, D. Tibaduiza, and M. Anaya, “A sensor data fusion system based on k-nearest neighbor pattern classification for structural health monitoring applications," Sensors, vol. 17, p. 417, 2017.; Y. Ma and Y. Fu, Manifold learning theory and applications. CRC press, 2011.; J. B. Tenenbaum, V. De Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction," science, vol. 290, no. 5500, pp. 2319-2323, 2000.; L. Van der Maaten, “An introduction to dimensionality reduction using matlab," Report, vol. 1201, no. 07-07, p. 62, 2007.; J. A. Lee and M. Verleysen, Nonlinear dimensionality reduction. Springer Science & Business Media, 2007.; “Gabriel peyré (2011) manifold learning with isomap." http://www.numerical-tours.com/matlab/shapes_7_isomap/. Accessed: 2021-04-12.; G. Peyré, “The numerical tours of signal processing-advanced computational signal and image processing," IEEE Computing in Science and Engineering, vol. 13, no. 4, pp. 94-97, 2011.; M. Belkin and P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation," Neural computation, vol. 15, no. 6, pp. 1373-1396, 2003.; Á. Fernández Pascual, “Advanced methods for dimensionality reduction and clustering: Laplacian eigenmaps and spectral clustering," Master's thesis, Universidad Autónoma de Madrid, 2010.; S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding," science, vol. 290, no. 5500, pp. 2323-2326, 2000.; Y. Ni, J. Chai, Y.Wang, and W. Fang, “A fast radio map construction method merging self-adaptive local linear embedding (lle) and graph-based label propagation in wlan fingerprint localization systems," Sensors, vol. 20, no. 3, p. 767, 2020.; Z. Zhang and J. Wang, “Mlle: Modified locally linear embedding using multiple weights," in Advances in neural information processing systems, pp. 1593-1600, 2007.; D. L. Donoho and C. Grimes, “Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data," Proceedings of the National Academy of Sciences, vol. 100, no. 10, pp. 5591-5596, 2003.; L. Van Der Maaten, E. Postma, and J. Van den Herik, “Dimensionality reduction: a comparative," J Mach Learn Res, vol. 10, no. 66-71, p. 13, 2009.; Z. Zhang and H. Zha, “Principal manifolds and nonlinear dimensionality reduction via tangent space alignment," SIAM journal on scienti_c computing, vol. 26, no. 1, pp. 313-338, 2004.; L. v. d. Maaten and G. Hinton, “Visualizing data using t-sne," Journal of machine learning research, vol. 9, no. Nov, pp. 2579-2605, 2008.; G. E. Hinton and S. T. Roweis, “Stochastic neighbor embedding," in Advances in neural information processing systems, pp. 857-864, 2003.; M. Husnain, M. M. S. Missen, S. Mumtaz, M. M. Luqman, M. Coustaty, and J.-M. Ogier, “Visualization of high-dimensional data by pairwise fusion matrices using t-sne," Symmetry, vol. 11, no. 1, p. 107, 2019.; R. E. Sha_er, S. L. Rose-Pehrsson, and R. A. McGill, “A comparison study of chemical sensor array pattern recognition algorithms," Analytica Chimica Acta, vol. 384, no. 3, pp. 305-317, 1999.; M. Weso ly and P. Ciosek, “Comparison of various data analysis techniques applied for the classification of pharmaceutical samples by electronic tongue," Sensors and Actuators B: Chemical, vol. 267, pp. 570-580, 2018.; S. Dhanabal and S. Chandramathi, “A review of various k-nearest neighbor query processing techniques," International Journal of Computer Applications, vol. 31, no. 7, pp. 14-22, 2011.; X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, S. Y. Philip, et al., “Top 10 algorithms in data mining," Knowledge and information systems, vol. 14, no. 1, pp. 1-37, 2008.; R. A. Fisher, “The use of multiple measurements in taxonomic problems," Annals of eugenics, vol. 7, no. 2, pp. 179-188, 1936.; MathWorks:, Statistics and Machine Learning Toolbox for Matlab; ,. 2015.; F. J. H. . O. R. A. Breiman, L., Classification and Regression Trees. Belmont, California.: Wadsworth,, 1984.; L. Breiman, “Random forests," Machine Learning, vol. 45, pp. 5-32, 2001.; T. K. Ho, “The random subspace method for constructing decision forests," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, pp. 832-844, Aug. 1998.; C. Cortes and V. Vapnik, “Support-vector networks," Machine learning, vol. 20, no. 3, pp. 273-297, 1995.; C. W. Hsu and C. J. Lin, “A comparison of methods for multiclass support vector machines," IEEE Trans. Neural Netw., vol. 13, pp. 415-425, Mar. 2002.; M. Liu, M. Wang, J. Wang, and D. Li, “Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and chinese vinegar," Sens. Actuators, B, vol. 177, pp. 970-980, 2013.; T.-T. Wong, “Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation," Pattern Recognition, vol. 48, no. 9, pp. 2839-2846, 2015.; D. Ballabio, F. Grisoni, and R. Todeschini, “Multivariate comparison of classification performance measures," Chemometrics and Intelligent Laboratory Systems, vol. 174, no. February, pp. 33-44, 2018.; K. Varmuza and P. Filzmoser, Introduction to Multivariate Statistical Analysis in Chemometrics. Boca Raton, FL: CRC press, 2009.; L. F. Villamil-Cubillos, J. X. Leon-Medina, M. Anaya, and D. A. Tibaduiza, “Evaluation of feature selection techniques in a multifrequency large amplitude pulse voltametric electronic tongue," Engineering Proceedings, vol. 2, no. 1, 2020.; J. X. Leon-Medina, M. Anaya, and D. A. Tibaduiza, “Locally linear embedding as nonlinear feature extraction to discriminate liquids with a cyclic voltammetric electronic tongue," in CSAC2021: 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry, (Basel), pp. 1-6, MDPI, 2021.; E. G. Mendez-Lopez, J. X. Leon-Medina, and D. A. Tibaduiza, “Development of a pattern recognition tool for the classification of electronic tongue signals using machine learning," in CSAC2021: 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry, (Basel), pp. 1-6, MDPI, 2021.; J. X. Leon-Medina, M. Anaya, and D. A. Tibaduiza, “T-distributed stochastic neighbor embedding to improve the discrimination of yogurt using a multistep amperometry electronic tongue," ECS Meeting Abstracts, vol. MA2021-01, pp. 2061-2061, may 2021.; N. R. Stradiotto, H. Yamanaka, and M. V. B. Zanoni, “Electrochemical sensors: a powerful tool in analytical chemistry," Journal of the Brazilian Chemical Society, vol. 14, no. 2, pp. 159-173, 2003.; J. Bobacka, A. Ivaska, and A. Lewenstam, “Potentiometric ion sensors," Chemical reviews, vol. 108, no. 2, pp. 329-351, 2008.; J. Wang, Electrochemical Sensors, ch. 6, pp. 201-243. John Wiley Sons, Ltd, 2006.; B. Uslu and S. A. Ozkan, “Solid electrodes in electroanalytical chemistry: present applications and prospects for high throughput screening of drug compounds," Combinatorial chemistry & high throughput screening, vol. 10, no. 7, pp. 495-513, 2007.; B. Uslu and S. A. Ozkan, “Electroanalytical application of carbon based electrodes to the pharmaceuticals," Analytical Letters, vol. 40, no. 5, pp. 817-853, 2007.; P. Kissinger and W. R. Heineman, Laboratory Techniques in Electroanalytical Chemistry, revised and expanded. CRC press, 2018.; J. Wang, “Stripping analysis at bismuth electrodes: a review," Electroanalysis: An International Journal Devoted to Fundamental and Practical Aspects of Electroanalysis, vol. 17, no. 15-16, pp. 1341-1346, 2005.; D. F. Nieto Arboleda, “Desarrollo de un sistema de adquisición de datos provenientes de sensores tipo lengua electrónica para aplicaciones en industria alimenticia," tech. rep., Trabajo de grado. Programa de Ingeniería electrónica. Universidad Nacional de Colombia sede Bogotá, 2020.; F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.; K. Johnsson, Structures in High-Dimensional Data: Intrinsic Dimension and Cluster Analysis. PhD thesis, Lund University, 2016.; G. Kraemer, M. Reichstein, and M. D. Mahecha, “dimred and coranking-unifying dimensionality reduction in r," R Journal, vol. 10, no. 1, pp. 342-358, 2018.; “Robrecht cannoodt and wouter saelens (2020) dyndimred: Dimensionality reduction methods in a common format." https://cran.microsoft.com/snapshot/2020-04-20/web/packages/dyndimred/index.html. Accessed: 2021-04-12.; K. You, “Rdimtools: An r package for dimension reduction and intrinsic dimension estimation," arXiv preprint arXiv:2005.11107, 2020.; I. Capera, J. Calder_on, O. Ramos, and O. Restrepo, “Inuencia del voltaje sobre las características reológicas del sabajón," Publicaciones e Investigación, vol. 1, pp. 15-35, 2007.; C. A. Fuenmayor, A. C. Díaz-Moreno, C. M. Zuluaga-Domínguez, and M. C. Quicazán, “Honey of colombian stingless bees: Nutritional characteristics and physicochemical quality indicators," in Pot-Honey, pp. 383-394, Springer, 2013.; A. Naila, S. H. Flint, A. Z. Sulaiman, A. Ajit, and Z. Weeds, “Classical and novel approaches to the analysis of honey and detection of adulterants," Food Control, vol. 90, pp. 152-165, 2018.; C. Maione, F. Barbosa, and R. M. Barbosa, “Predicting the botanical and geographical origin of honey with multivariate data analysis and machine learning techniques: A review," Computers and Electronics in Agriculture, vol. 157, no. January, pp. 436-446, 2019.; M. Bougrini, K. Tahri, T. Saidi, N. El Alami El Hassani, B. Bouchikhi, and N. El Bari, “Classification of Honey According to Geographical and Botanical Origins and Detection of Its Adulteration Using Voltammetric Electronic Tongue," Food Analytical Methods, vol. 9, no. 8, pp. 2161-2173, 2016.; J. Cai, X. Wu, L. Yuan, E. Han, L. Zhou, and A. Zhou, “Determination of Chinese Angelica honey adulterated with rice syrup by an electrochemical sensor and chemometrics, "Analytical Methods, vol. 5, no. 9, pp. 2324-2328, 2013.; Z. Gan, Y. Yang, J. Li, X.Wen, M. Zhu, Y. Jiang, and Y. Ni, “Using sensor and spectral analysis to classify botanical origin and determine adulteration of raw honey," Journal of Food Engineering, vol. 178, pp. 151-158, 2016.; M. Juan-Borrás, J. Soto, L. Gil-Sánchez, A. Pascual-Maté, and I. Escriche, “Antioxidant activity and physico-chemical parameters for the differentiation of honey using a potentiometric electronic tongue," Journal of the Science of Food and Agriculture, vol. 97, no. 7, pp. 2215-2222, 2016.; M. Oroian, S. Paduret, and S. Ropciuc, “Honey adulteration detection: voltammetric etongue versus official methods for physicochemical parameter determination," Journal of the Science of Food and Agriculture, vol. 98, no. 11, pp. 4304-4311, 2018.; M. Oroian and S. Ropciuc, “Romanian honey authentication using voltammetric electronic tongue. correlation of voltammetric data with physico-chemical parameters and phenolic compounds," Computers and electronics in agriculture, vol. 157, pp. 371-379, 2019.; D. Pauliuc, F. Dranca, and M. Oroian, “Raspberry, rape, thyme, sunower and mint honeys authentication using voltammetric tongue," Sensors, vol. 20, no. 9, p. 2565, 2020.; K. Tiwari, B. Tudu, R. Bandyopadhyay, and A. Chatterjee, “Identification of monofloral honey using voltammetric electronic tongue," Journal of Food Engineering, vol. 117, no. 2, pp. 205-210, 2013.; L. Sobrino-Gregorio, R. Bataller, J. Soto, and I. Escriche, “Monitoring honey adulteration with sugar syrups using an automatic pulse voltammetric electronic tongue," Food Control, vol. 91, pp. 254-260, 2018.; A. C. Veloso, M. E. Sousa, L. Estevinho, L. G. Dias, and A. M. Peres, “Honey evaluation using electronic tongues: An overview," Chemosensors, vol. 6, no. 3, pp. 1-25, 2018.; J. W. Gardner and P. N. Bartlett, “A brief history of electronic noses," Sensors and Actuators: B. Chemical, vol. 18, no. 1-3, pp. 210-211, 1994.; M. Holmberg, F. Winquist, I. Lundström, F. Davide, C. DiNatale, and A. D'Amico, “Drift counteraction for an electronic nose," Sens. Actuators, B, vol. 36, no. 1-3, pp. 528-535, 1996.; M. Zuppa, C. Distante, P. Siciliano, and K. C. Persaud, “Drift counteraction with multiple self-organising maps for an electronic nose," Sens. Actuators, B, vol. 98, no. 2-3, pp. 305-317, 2004.; A. Rudnitskaya, “Calibration Update and Drift Correction for Electronic Noses and Tongues," Frontiers in Chemistry, vol. 6, no. September, 2018.; G. Korotcenkov and B. Cho, “Instability of metal oxide-based conductometric gas sensors and approaches to stability improvement (short survey)," Sens. Actuators, B, vol. 156, no. 2, pp. 527-538, 2011.; S. Di Carlo and M. Falasconi, “Drift correction methods for gas chemical sensors in artificial olfaction systems: techniques and challenges," in Advances in Chemical Sensors, IntechOpen, 2012.; A. Vergara, S. Vembu, T. Ayhan, M. A. Ryan, M. L. Homer, and R. Huerta, “Chemical gas sensor drift compensation using classifier ensembles," Sensors and Actuators, B: Chemical, vol. 166-167, pp. 320-329, 2012.; J. Fonollosa, I. Rodríguez-Luján, and R. Huerta, “Chemical gas sensor array dataset," Data in brief, vol. 3, pp. 85-89, 2015.; L. Zhang, D. Zhang, X. Yin, and Y. Liu, “A novel semi-supervised learning approach in artificial olfaction for e-nose application," IEEE Sensors J., vol. 16, no. 12, pp. 4919- 4931, 2016.; A. ur Rehman and A. Bermak, “Swarm intelligence and similarity measures for memory efficient electronic nose system," IEEE Sensors J., vol. 18, no. 6, pp. 2471-2482, 2018.; T. Liu, D. Li, J. Chen, Y. Chen, T. Yang, and J. Cao, “Gas-sensor drift counteraction with adaptive active learning for an electronic nose," Sensors, vol. 18, no. 11, p. 4028, 2018.; K. Yan, L. Kou, and D. Zhang, “Learning domain-invariant subspace using domain features and independence maximization," IEEE transactions on cybernetics, vol. 48, no. 1, pp. 288-299, 2017.; B. Liu, X. Zeng, F. Tian, S. Zhang, and L. Zhao, “Domain transfer broad learning system for long-term drift compensation in electronic nose systems," IEEE Access, vol. 7, pp. 143947-143959, 2019.; A. Grover and B. Lall, “A novel method for removing baseline drifts in multivariate chemical sensor," IEEE Transactions on Instrumentation and Measurement, 2020.; T. Liu, Y. Chen, D. Li, T. Yang, J. Cao, and M. Wu, “Drift compensation for an electronic nose by adaptive subspace learning," IEEE Sensors Journal, vol. 20, no. 1, pp. 337-347, 2019.; S. J. Pan and Q. Yang, “A survey on transfer learning," IEEE Trans Knowl Data Eng, vol. 22, no. 10, pp. 1345-1359, 2009.; A. Arnold, R. Nallapati, and W. W. Cohen, “A comparative study of methods for transductive transfer learning.," in ICDM Workshops, pp. 77-82, 2007.; M. Long, J. Wang, G. Ding, J. Sun, and P. S. Yu, “Transfer feature learning with joint distribution adaptation," in Proceedings of the IEEE international conference on computer vision, pp. 2200-2207, 2013.; A. Gretton, K. Borgwardt, M. J. Rasch, B. Scholkopf, and A. J. Smola, “A kernel method for the two-sample problem," arXiv preprint arXiv:0805.2368, 2008.; H. Abdi and L. J. Williams, “Principal component analysis," Wiley Interdiscip Rev Comput Stat, vol. 2, no. 4, pp. 433-459, 2010.; S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, “Domain adaptation via transfer component analysis," IEEE Trans. Neural Netw., vol. 22, no. 2, pp. 199-210, 2010.; T.-M. Huang, V. Kecman, and I. Kopriva, Kernel based algorithms for mining huge data sets, vol. 1. Springer, 2006.; G. Camps-Valls, “Kernel spectral angle mapper," Electron. Lett., vol. 52, no. 14, pp. 1218-1220, 2016.; B. Gong, Y. Shi, F. Sha, and K. Grauman, “Geodesic ow kernel for unsupervised domain adaptation," in 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2066-2073, IEEE, 2012.; Q. Sun, R. Chattopadhyay, S. Panchanathan, and J. Ye, “A two-stage weighting framework for multi-source domain adaptation," in Advances in neural information processing systems, pp. 505-513, 2011.; I. Rodriguez-Lujan, J. Fonollosa, A. Vergara, M. Homer, and R. Huerta, “On the calibration of sensor arrays for pattern recognition using the minimal number of experiments," Chemometrics and Intelligent Laboratory Systems, vol. 130, pp. 123-134, 2014.; E. G. Méndez López, “Desarrollo de una interfaz gráfica de usuario gui para el procesamiento de señales obtenidas con arreglos de sensores tipo lengua electrónica," tech. rep., Trabajo de grado. Programa de Ingeniería eléctrica. Universidad Nacional de Colombia sede Bogotá, 2021.; https://repositorio.unal.edu.co/handle/unal/79962; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

Dostupnost: https://repositorio.unal.edu.co/handle/unal/79962
https://repositorio.unal.edu.co/

Desarrollo de un análisis comparativo de desempeño computacional entre Nvidia Jetson Nano y el Raspberry Pi CM4, en el contexto de clasificación de frutas mediante algoritmos basados en Máquinas de Soporte Vectorial (SVM) y Redes Neuronales Convolucionales (CNN) ; Development of a comparative performance analysis between the Nvidia Jetson Nano and Raspberry Pi CM4 in the context of fruit classification using Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms

Autoři: Ruiz Mundaca, Edgar Jahir Ortiz Rojas, Manuel Enrique Becerra Felipe, Jose Luis

Přispěvatelé: Ruiz Mundaca, Edgar Jahir Ortiz Rojas, Manuel Enrique Becerra Felipe, Jose Luis

Zdroj: Universidad Peruana de Ciencias Aplicadas (UPC) ; Repositorio Académico - UPC

Témata: Single Board Computer, Nvidia Jetson Nano, Raspberry Pi CM4, Redes neuronales computacionales, Máquinas de soporte vectorial, Clasificación de imágenes, Convolutional Neural Networks, Support Vector Machines, Image Classification, https://purl.org/pe-repo/ocde/ford#2.03.01, https://purl.org/pe-repo/ocde/ford#2.00.00

Popis souboru: application/pdf; application/epub; application/msword

Relation: http://hdl.handle.net/10757/683333; 000000012196144X

Dostupnost: http://hdl.handle.net/10757/683333

Mejorar el modelo de estimación de riesgo de deserción de los estudiantes de pregrado de la Universidad Autónoma de Bucaramanga empleando herramientas business intelligence soportadas en software libre ; IMPROVE THE RISK ESTIMATION MODEL OF UNDERGRADUATE STUDENTS OF THE UNIVERSITY AUTÓNOMA DE BUCARAMANGA USING BUSINESS INTELLIGENCE TOOLS SUPPORTED IN FREE SOFTWARE

Autoři: Ropero Silva, Miguel Eduardo Cuello De Avila, Silvio Rafael Cuello De Avila, Silvio Rafael 0g-Gf7AAAAAJ

Přispěvatelé: Ropero Silva, Miguel Eduardo Cuello De Avila, Silvio Rafael Cuello De Avila, Silvio Rafael 0g-Gf7AAAAAJ

Témata: Universidad Autónoma de Bucaramanga UNAB, Systems Engineering, Free software, Data Mining, Computer program, Algorithms, University dropout, Technical aspects, Investigations, Analysis, Academic desertion, Decision trees, Classification techniques, Ingeniería de sistemas, Software libre, Programa para computador, Algoritmos, Deserción universitaria, Aspectos técnicos, Investigaciones, Análisis, Deserción académica, Minería de datos, Arboles de decisión, Técnicas de clasificación, Algoritmo J48, Weka

Geografické téma: Bucaramanga (Colombia), Bucaramanga (Santander, Colombia)

Popis souboru: application/pdf; application/octet-stream

Relation: Ropero Silva, Miguel Eduardo (2018). Mejorar el modelo de estimación de riesgo de deserción de los estudiantes de pregrado de la Universidad Autónoma de Bucaramanga empleando herramientas bussines intelligence soportadas en software libre. Bucaramanga (Colombia) : Universidad Autónoma de Bucaramanga UNAB, Universitat Oberta de Catalunya UOC; Amaya, Y. y Barrientos, E. y Heredia, D. (2015). Student dropout predictive model using data mining techniques. IEEE Latin America Transactions, vol. (13).; Azoumana, K. (2013). Análisis de la deserción estudiantil en la Universidad Simón Bolívar, facultad Ingeniería de Sistemas, con técnicas de minería de datos. Pensamiento Americano, 41-5; Bouckaert, R. (2010). WEKA---Experiences with a java open-source project. The Journal of Machine Learning Research, vol. (11), pp. 2533-2541. Recuperado de http://dl.acm.org/citation.cfm?id=1953016; Cabena, P. (1998). Discovering Data mining From Concept To Implementation. Estados Unidos: Prentice Hall; Castellanos Guarín, L. (2015). Incorporación de Elementos de Inteligencia de Negocios al Análisis de Deserción Estudiantil de la universidad Autónoma de Bucaramanga (Tesis de Maestría). Universidad Autónoma de Bucaramanga, Colombia.; Chapman, P. y Clinton, J. y Kerber, R. y Khabaza, T. y Reinartz, T. y Shearer, C. y Wirth, R. (2000). CRISP-DM 1.0. Recuperado de ftp://ftp.software.ibm.com/software/analytics/spss/support/Modeler/Documentation/14/UserManual/CRISP-DM.pdf; Demšar, J. (2016). Orange: Data mining toolbox in python. The Journal of Machine Learning Research, vol. (14), pp. 2349-2353. Recuperado de http://dl.acm.org/citation.cfm?id=2567736&CFID=789665709&CFTOKEN=93305719; Departamento Nacional de Planeación. (2016). Visión Colombia II Centenario. Recuperado de http://www.mineducacion.gov.co/cvn/1665/article-95980.html; Ekkachai, N. Jatsada, S. Nittaya, K. (2012). Classification Model Induction For Student Recruiting, Latest Advances In Educational Technologies. Recuperado de http://www.wseas.us/e-library/conferences/2012/Singapore/EDUC/EDUC-18.pdf; Fayyad, U. y Piatetsky-Shapiro, G. y Smyth, P. (1996). From data mining to knowledge discovery: an overview. AI Magazine, vol. (17), pp. 37-54. Recuperado de https://www.aaai.org/ojs/index.php/aimagazine/article/viewFile/1230/1131; Han, J. y Kamber, M. y Pei, J. (2001). Data mining: Concepts and techniques. Amsterdam: Morgan Kaufmann Publishers.; Hernández Orallo, J. y Ramírez Quintana, M. y Ferri Ramírez, C. (2004). Introducción a la Minería de Datos. Pearson Educación; Howson, C. (2007). Successful business intelligence: Secrets to making bi a killer App. Estados Unidos: McGraw-Hill Education; International Educational Data Mining Society. (s.f.). Educational Data Mining. Recuperado el 10 de abril de 2016 de http://educationaldatamining.org; Kumar, S. y Pal, S. (2012). Data mining: A prediction for performance improvement of engineering students using classification. World of Computer Science and Information Technology Journal, vol. (2), pp. 51-56.; The University Of Waikato (s.f.). Weka 3 - data mining with open source machine learning software in java. Recuperado de http://www.cs.waikato.ac.nz/ml/weka/; Ministerio de Educación Nacional. (2010). Deserción estudiantil en la educación superior colombiana. Recuperado el 15 abril de 2016 de http://www.mineducacion.gov.co/sistemasdeinformacion/1735/articles-254702_libro_desercion.pdf; Ministerio de Educación Nacional. (2015a). Estrategias Para la Permanencia en Educación Superior: Experiencias Significativas. Recuperado de http://www.colombiaaprende.edu.co/html/micrositios/1752/articles-350844_pdf.pdf.; Ministerio de Educación Nacional (2015b). Guía para la implementación del modelo de gestión de permanencia y graduación estudiantil en instituciones de educación superior. Recuperado de http://www.colombiaaprende.edu.co/html/micrositios/1752/articles-355193_guia_.pdf; Ministerio de Educación Nacional (2016). Estadísticas de deserción y graduación 2015. Recuperado de http://www.colombiaaprende.edu.co/html/ micrositios/1752/articles-350629_estadisticas_pdf2015.pdf; Moine, J. y Haedo, A. y Gordillo, S. (2001). Estudio comparativo de metodologías para minería de datos. XIII Workshop de Investigadores en Ciencias de la Computación. Recuperado de http://sedici.unlp.edu.ar/handle/10915/20034; Muenchen, B. (2017). The Popularity of Data Science Software. Recuperado de http://r4stats.com/articles/popularity; Oracle Help Center (2016). Data Warehousing and Business Intelligence. Recuperado de https://docs.oracle.com/cd/B28359_01/datamine.111/ b28129/regress.htm; Parr Rud, O. (2000). Data mining cookbook: Modeling data for marketing, risk, and customer relationship management. United States: Wiley, John & Sons.; Rangra, K. (2014). Comparative study of data mining tools. International Journal of Advanced Research in Computer Science and Software Engineering, vol. (04), pp. 6; Remco, R. Eibe, F. (2016). Weka Manual for Version 3-8-1. Recuperado de http://www.cs.waikato.ac.nz/ml/weka/documentation.html; Rohanizadeh, S. y Moghadam, M. (2010). A proposed data mining methodology and its application to industrial procedures. Journal of Industrial Engineering, vol. (4), pp. 37-50; Sauter, V. (2011). Decision support systems for business intelligence. New Jersey, Estados Unidos: United Kingdom: Wiley-Blackwell.; Society for Learning Analytics Research – SoLAR. (2014). About SOLAR. Recuperado el 10 de abril de 2016 de http://educationaldatamining.org/; Statistical Analysis System - SAS Institute Inc (2003). Data Mining Using SAS Enterprise Miner: A Case Study Approach. Recuperado de http://support.sas.com/documentation/onlinedoc/miner/casestudy_59123.pdf; Timaran Pereira, R. (2009). Una lectura sobre deserción universitaria en estudiantes de pregrado desde la perspectiva de la Minería de Datos. Recuperado el 20 de mayo de 2016 de http://www.redalyc.org/html/1053/105317327011/; Turban, E. y Sharda, R. y Denle, D. y King, D. (2013). Business intelligence: A managerial perspective on Analytics. Boston, Estados Unidos: Prentice Hall; Universidad Autónoma de Bucaramanga (2016a). Acerca de la UNAB. Recuperado de http://unab.edu.co/nosotros/acerca-de; Universidad Autónoma de Bucaramanga (2016b). Bienestar Universitario. Recuperado de http://unab.edu.co/nosotros/bienestar-universitario; University of Ljubljana (2016). License Orange. Recuperado de http://orange.biolab.si/license/; Valenzuela, J. Flores, M. (2014). Fundamentos de Investigación Educativa. Ciudad de México, México: Editorial digital del tecnológico de Monterrey.; Vercellis, C. (2011). Business intelligence: Data mining and optimization for decision making. United States: Wiley, John & Sons.; Witten, I. y Frank, E. y Hall, M. (2005). Data mining: Practical machine learning tools and techniques. San Francisco, CA: Morgan Kaufmann Publishers.; http://hdl.handle.net/20.500.12749/3439; reponame:Repositorio Institucional UNAB

Dostupnost: https://hdl.handle.net/20.500.12749/3439

33° Simposio Internacional de estadística 2024 : ciencia de datos

Přispěvatelé: Universidad Nacional de Colombia. Sede Bogotá. Facultad de Ciencias. Departamento de Estadística

Témata: 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores, APRENDIZAJE AUTOMATICO (INTELIGENCIA ARTIFICIAL), PROCESAMIENTO ELECTRONICO DE DATOS-TECNICAS ESTRUCTURADAS, ANALISIS NUMERICO-PROCESAMIENTO DE DATOS-CONGRESOS,CONFERENCIAS,ETC, Machine learning, Electronic data processing - Structured techniques, Numerical analysis - data processing congresses, Bootstrap, Regresión, Series Temporales, Modelos Bayesianos, Modelos de Ecuaciones Estructurales, Minería de Datos, Modelos de Regresión, Algoritmos de Clasificación, Redes Neuronales, Modelos de Espacio de Estado, Modelos AutoRegresivos, Inferencia Estadística, Modelos Mixtos, Distribuciones de Probabilidad, Muestreo Óptimo

Geografické téma: 30 de julio al 02 de agosto de 2024

Time: 30 de julio al 02 de agosto de 2024

Popis souboru: 533 páginas; application/pdf

Relation: CONSEJO NACIONAL DE ACREDITACIÓN (CNA)., Lineamientos para la acreditación de programas académicos de pregrado. 2013. Consultado: 19 de agosto de 2024. Disponible en: www.mineducacion.gov.co/1621/articles-342684_recurso_1.pdf.; MINISTERIO DE EDUCACIÓN NACIONAL DE COLOMBIA., Sistema de Prevención y Análisis de la Deserción en las Instituciones de Educación Superior (SPADIES). 2024. Consultado: 19 de agosto de 2024. Disponible en: www.spadies.mineducacion.gov.co.; F. PEDREGOSA, G. VAROQUAUX, A. GRAMFORT, V. MICHEL, B. THIRION, O. GRISEL, M. BLONDEL, P. PRETTENHOFER, R. WEISS, V. DUBOURG, J. VANDERPLAS, A. PASSOS, D. COURNAPEAU, M. BRUCHER, M. PERROT, AND E. DUCHESNAY., Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12:2825–2830, 2011.; GUIDO VAN ROSSUM AND FRED L. DRAKE JR., Python tutorial. Centrum voorWiskunde en Informatica Amsterdam, The Netherlands, 1995.; [5] DANIEL JURAFSKY AND JAMES H. MARTIN., Speech and Language Processing. Stanford University, 2023.; JAMES BERGSTRA AND YOSHUA BENGIO., Random search for hyper-parameter optimization. Journal of Machine Learning Research 13(2):281–305, 2012.; DAVIDE CHICCO AND GIUSEPPE JURMAN., The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21:1–13, 2020. Springer.; DAVID M. W. POWERS., Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. CoRR abs/2010.16061, 2020. Disponible en: https://arxiv.org/abs/2010.16061.; CORTES, C. AND VAPNIK, V., Support Vector Networks. Machine Learning 20:273–297, 1995.; SEBASTIAN RASCHKA, YUXI (HAYDEN) LIU, AND VAHID MIRJALILI., Machine Learning with PyTorch and Scikit-Learn. Packt Publishing, Birmingham, UK, 2022.; LEIF E. PETERSON., K-nearest neighbor. Scholarpedia 4(2):1883, 2009.; MAHESH PAL., Random forest classifier for remote sensing classification. International Journal of Remote Sensing 26(1):217–222, 2005. Taylor & Francis.; DAVID E. RUMELHART, GEOFFREY E. HINTON, AND RONALD J. WILLIAMS., Learning representations by back-propagating errors. Nature 323:533–536, 1986. Disponible en: https://api.semanticscholar.org/CorpusID:205001834.; ZACHARY CHASE LIPTON, CHARLES ELKAN, AND BALAKRISHNAN NARAYANASWAMY., Thresholding Classifiers to Maximize F1 Score. 2014. Disponible en: https: //arxiv.org/abs/1402.1892.; NITESH V. CHAWLA, KEVIN W. BOWYER, LAWRENCE O. HALL, AND W. PHILIP KEGELMEYER., SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16:321–357, 2002.; CAROLINA GUZMÁN RUIZ, DIANA DURÁN MURIEL, JORGE FRANCO GALLEGO, ELKIN CASTAÑO VÉLEZ, SANTIAGO GALLÓN GÓMEZ, KAROLL GÓMEZ PORTILLA, AND JOHANNA VÁSQUEZ VELÁSQUEZ., Deserción estudiantil en la educación superior colombiana. 2009. Disponible en: https://www.mineducacion.gov.co/ sistemasdeinformacion/1735/articles-254702_libro_desercion.pdf.; AURÉLIEN GÉRON., Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. O’Reilly Media, Sebastopol, CA, 2017.; MARINA SOKOLOVA AND GUY LAPALME., A systematic analysis of performance measures for classification tasks. Information Processing Management 45(4):427–437, 2009. DOI: https://doi.org/10.1016/j.ipm.2009.03.002.; PLOTLY TECHNOLOGIES INC., Dash: A Python framework for building analytical web applications. Versión 2.0.0, 2024. Disponible en: https://dash.plotly.com.; G. E. BATTESE, R. M. HARTER, AND W. A. FULLER. , An error-components model for prediction of county crop areas using survey and satellite data. Journal of the American Statistical Association 83(401):28–36, 1988.; DANE., Encuesta multipropósito (em), 2017.; D. A. HARVILLE., Decomposition of prediction error. Journal of the American Statistical Association, 80(389):132–138, 1985.; D. A. HARVILLE., [that blup is a good thing: The estimation of random effects: Comment. Statistical Science, 6(1):35–39, 1991.; J. A. HILDEN-MINTON., Multilevel diagnostics for mixed and hierarchical linear models. 1996.; J. C. PINHEIRO AND D. M. BATES., Linear mixed-effects models: basic concepts and examples. Mixed-effects models in S and S-Plus, pages 3–56, 2000.; J. N. RAO AND I. MOLINA., Small area estimation. John Wiley Sons, 2015.; VAN DER LEEDEN, RIEN AND BUSING, FMTA AND MEIJER, ERIK, Bootstrap methods for two-level models. Multilevel conference, 1997; G. VERBEKE AND E. LESAFFRE., The effect of misspecifying the random-effects distribution in linear mixed models for longitudinal data. Computational Statistics Data Analysis, 23(4):541–556, 1997.; BAFFES, J. & GARDNER, B., The transmission of world commodity prices to domestic markets under policy reforms in developing countries, The Journal of Policy Reform, 6(3), 159–180. https://doi.org/10.1080/0951274032000175770, 2003.; BUSTAMANTE MARTÍNEZ, A., AMARU, E., LISTA, G., & CARLOS GÓMEZ FLÓREZ, L., ETL Processes modeling techniques: an alternatives review and its application in a BI solution development project, Scientia et Technica Año XVIII, 18(1), 2013.; DANE, Sistema de Información de Precios y Abastecimiento del Sector Agropecuario Componente Abastecimiento de Alimentos - SIPSA, 2024. https://microdatos.dane.gov.co/catalog/697.; DOGUCU, M. & ÇETINKAYA-RUNDEL, M., Web Scraping in the Statistics and Data Science Curriculum: Challenges and Opportunities, Journal of Statistics Education. https://doi.org/10.1080/10691898.2020.1787116, 2020.; LOZANO FORERO, S., CAMILO, J., & CONTRERAS, Proposal for a SIPSA index and its relation to food inflation for the Colombian case: empirical evidence, 2019. https://repository.libertadores.edu.co/server/api/core/bitstreams/1777bf32-e5cd-4769-b9c5- 82bec172d4e8/content.; EKHOLM, ANDERS Y SEPPO LAAKSONEN, Weighting via response modeling in the Finnish Household Budget Survey., Journal of Official Statistics 7.3, págs. 325-337, 1991.; IANNACCHIONE, VINCENT G, JENNIFER G MILNE Y RALPH E FOLSOM, Response probability weight adjustments using logistic regression., Proceedings of the Survey Research Methods Section, American Statistical Association. Vol. 20, 1991.; RACINE, JEFF Y QI LI, Nonparametric estimation of regression functions with both categorical and continuous data., Journal of Econometrics 119.1, págs. 99-130, 2004.; DA SILVA, DAMIÃO N Y JEAN D OPSOMER, A kernel smoothing method of adjusting for unit non-response in sample surveys., Canadian Journal of Statistics 34.4, págs. 563-579, 2006.; KIM, JAE KWANG Y JAY J KIM, Nonresponse weighting adjustment using estimated response probability., Canadian Journal of Statistics 35.4, págs. 501-514, 2007.; DA SILVA, DAMIÃO N Y JEAN D OPSOMER, Nonparametric propensity weighting for survey nonresponse through local polynomial regression., Survey Methodology 35.2, págs. 165-176, 2009.; HAZIZA, DAVID Y ÉRIC LESAGE, A discussion of weighting procedures for unit nonresponse., Journal of Official Statistics 32.1, págs. 129-145, 2016.; FU, RAN, Weighting adjustments in surveys., Tesis doctoral, Colorado State University, 2017.; LITTLE, RODERICK JA Y DONALD B RUBIN, Statistical analysis with missing data. John Wiley & Sons, 2019.; E. L. LEHMANN AND G. CASELLA., Theory of point estimation. Springer Science & Business Media, 2006.; S. WRIGHT., The distribution of gene frequencies in populations. Proceedings of the National Academy of Sciences, 23(6):307–320, 1937.; EPA (2023) Agencia de Protección Ambiental de Estados Unidos. (Octubre de 2023). Por qué la calidad del aire de los interiores es importante para las escuelas.; Bedoya Sanmiguel, Ó. G., Álvarez Vanegas, A., Cadena Gaitán, C., Espinosa Valderrama, M., Pachón Quinche, J. E., Zapata Sánchez, C. E. (s. f.). Efectos de contaminación atmosférica en la salud.; Giorgino, T. (2009). Computing and Visualizing Dynamic Time warping alignments INR: TheDTWPackage. Journal of Statistical Software.; Honaker, James, and Gary King. "What to Do about Missing Values in Time-Series Cross- Section Data.American Journal of Political Science; Van Buuren, Stef. Flexible Imputation of Missing Data, second edition. 2018.; NACIONES UNIDAS., Secretary-General Calls For Transformation In Men’s Attitudes To End All Forms Of Violence Against Women. 2003. Disponible en: https://press.un. org/en/2003/sgsm9030.doc.htm.; NACIONES UNIDAS., Hechos y cifras: Poner fin a la violencia contra las mujeres. 2023. Disponible en: https://www.unwomen.org/es/what-we-do/ ending-violence-against-women/facts-and-figures.; NACIONES UNIDAS., Declaration on the Elimination of Violence against Women. 1999. Disponible en: https://www.ohchr.org/es/instruments-mechanisms/ instruments/declaration-elimination-violence-against-women.; ENRIQUE GRACIA, ANTONIO LÓPEZ-QUÍLEZ, MIRIAM MARCO, MARISOL LILA., Neighborhood characteristics and violence behind closed doors: The spatial overlap of child maltreatment and intimate partner violence. PLoS One, 13(6) , 2018. Public Library of Science San Francisco, CA USA.; PAUL RODRIGUES, MYLÈNE FERNET, MARIE-MARTHE COUSINEAU, MATHIEU PHILIBERT., Associations between small-area sociodemographic characteristics and intimate partner violence in Montréal, Québec. Journal of Public Health Research, 12(4):22799036231208326, 2023. SAGE Publications Sage UK: London, England.; DEBORAH M. CAPALDI, NAOMI B. KNOBLE, JOANN WU SHORTT, HYOUN K. KIM., A systematic review of risk factors for intimate partner violence. Partner Abuse, 3(2):231, 2012. NIH Public Access.; ISAAC ESTEBAN CAMARGO FREILE, KAREN CECILIA FLÓREZ LOZANO, CARLOS ALBERTO SARMIENTO CRESPO, CAROLINA MERCEDES VECCHIO CAMARGO, SANDRA MILENA RODRÍGUEZ ACOSTA, VICTOR FLOREZ-GARCIA, EDGAR NAVARRO LECHUGA., Risk of violence from a current or former partner: Associated factors and classification in a nationwide study in Colombia. PloS One, 17(12) , 2022. Public Library of Science San Francisco, CA USA.; OPS., Violencia contra la mujer. 2023. Disponible en: https://www.paho.org/es/ temas/violencia-contra-mujer.; PROCURADURÍA., Preocupante radiografía de violencia contra la mujer advierte Procuraduría. 2023. Disponible en: https://www.procuraduria.gov.co/Pages/ preocupante-radiografia-violencia-contra-mujer-advierte-procuraduria. aspxr.; PROCURADURÍA., 3 mujeres cada hora, 128 al día y 47 mil en el 2022, fueron víctimas de violencia intrafamiliar: Procuraduría. 2022. Disponible en: https://www.procuraduria.gov.co/Pages/ 3-mujeres-cada-hora-128-al-dia-y-47-mil-en-2022-fueron-victimas-de-violencia-intrafamiliar-aspx.; OMS., Violence against women prevalence estimates, 2018: WHO Region of the Americas. 2021. Organización Mundial de la Salud. Disponible en: https://www.who.int/ publications/i/item/WHO-SRH-21.11.; NATALIA ECHEVERRI CALERO, YANITHZA MEDINA BARRETO., Protocolo de valoración del riesgo de violencia mortal contra mujeres por parte de su pareja o expareja. Medicina Legal, 2014.; OPS., Comprender y abordar la violencia contra las mujeres. Panorama general. 2013.; SARAH BOTT, ALESSANDRA GUEDES, ANA P. RUIZ-CELIS, JENNIFER ADAMS MENDOZA., La violencia por parte de la pareja íntima en las Américas: una revisión sistemática y reanálisis de las estimaciones nacionales de prevalencia. Revista Panamericana de Salud Pública, 45 , 2021. SciELO Public Health.; LORI HEISE., What works to prevent partner violence? An evidence overview. 2011. STRIVE Research Consortium, London School of Hygiene and Tropical Medicine.; LORI L. HEISE., Violence against women: An integrated, ecological framework. Violence Against Women, 4(3):262–290, 1998. SAGE Periodicals Press Thousand Oaks.; LORI HEISE., Ending violence against women. Issues in World Health, 11:1–44, 1999.; PAUL J. BRANTINGHAM, PATRICIA L. BRANTINGHAM (EDS.)., Environmental criminology. Sage Publications, Beverly Hills [Calif.], 1981. Sage Focus Editions, 39. ISBN 978-0-8039-1678-4.; NEREA NÚÑEZ, OSCAR VIDAL-CASANELLA, SONIA SENTELLAS, JAVIER SAURINA, OSCAR NÚÑEZ., Characterization, Classification and Authentication of Turmeric and Curry Samples by Targeted LC-HRMS Polyphenolic and Curcuminoid Profiling and Chemometrics. Molecules, 25(12):2942, 2020. DOI: https://doi.org/10. 3390/molecules25122942. Disponible en: https://www.mdpi.com/1420-3049/25/ 12/2942.; SIMONE LOLLI., Machine Learning Techniques for Vertical Lidar-Based Detection, Characterization, and Classification of Aerosols and Clouds: A Comprehensive Survey. Remote Sensing, 15(17):4318, 2023. DOI: https://doi.org/10.3390/rs15174318. Disponible en: https://www.mdpi.com/2072-4292/15/17/4318.; IGNACIO RODRÍGUEZ-RODRÍGUEZ, JOSÉ-VÍCTOR RODRÍGUEZ, DOMINGO-JAVIER PARDO-QUILES, PURIFICACIÓN HERAS-GONZÁLEZ, IOANNIS CHATZIGIANNAKIS., Modeling and Forecasting Gender-Based Violence through Machine Learning Techniques. Applied Sciences, 10(22):8244, 2020. DOI: https://doi.org/10.3390/app10228244. Disponible en: https://www.mdpi.com/2076-3417/10/22/8244.; CAROLINA STEFONI., Panorama de la migración internacional en América del Sur. 2018. ISSN: 1680-9009. Disponible en: https://repositorio.cepal.org/server/ api/core/bitstreams/a6d972e4-f2cb-4eff-8328-611a329cf56a/content.; DORA AGUIRRE., Problemática específica de las mujeres jóvenes inmigrantes, víctimas de violencia de género. REVISTA DE ESTUDIOS DE JUVENTUD, 86:101–119, 2019. Disponible en: https://www.injuve.es/sites/default/files/RJ86-08.pdf.; WENDY KLIEWER, NIKOLA ZAHARAKIS., Community violence exposure, coping, and problematic alcohol and drug use among urban, female caregivers: A prospective study. Personality and Individual Differences, 55(4):361–366, 2013. DOI:10.1016/j.paid. 2013.03.020. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/ S0191886913001402.; CLAUDIA IVETHE JAENCORTÉS, SOFÍA RIVERA ARAGÓN, ELGA FILIPA AMORIN DE CASTRO, LEONOR RIVERA RIVERA., Violencia de Pareja en Mujeres: Prevalencia y Factores Asociados. Acta de Investigación Psicológica, 5(3):2224–2239, 2015. DOI:10.1016/ S2007-4719(16)30012-6. Disponible en: https://www.revista-psicologia.unam. mx/revista_aip/index.php/aip/article/view/82.; DORA BLITCHTEIN-WINICKI, ESPERANZA REYES-SOLARI., Factores asociados a violencia física reciente de pareja hacia la mujer en el Perú, 2004-2007. Revista Peruana de Medicina Experimental y Salud Pública, 29(1):35–43, 2012. DOI:10.1590/S1726-46342012000100006. Disponible en: http://www.scielosp.org/ scielo.php?script=sci_arttext&pid=S1726-46342012000100006&lng=es&nrm= iso&tlng=es.; Profamilia. Encuesta Nacional de Demografía y Salud Tomo 1. 2015. ISBN 978-958- 5401-09-9. Available at: https://profamilia.org.co/docs/ENDS%20%20TOMO%20I. pdf.; Profamilia. Encuesta Nacional de Demografía y Salud Tomo 2. 2015. ISBN 978-958- 5401-09-9. Available at: https://profamilia.org.co/wp-content/uploads/2019/ 05/ENDS-2015-TOMO-II.pdf.; Departamento Administrativo Nacional de Estadística. Censo Nacional de Población y Vivienda - CNPV- 2018. 2018. Available at: https://www.dane. gov.co/index.php/estadisticas-por-tema/demografia-y-poblacion/ proyecciones-de-poblacion.; Instituto Nacional de Medicina Legal y Ciencias Forenses. Forensis 2022, Datos para la vida. 2023. ISSN 2145-0250.; Torres Munguía, Juan Armando. A model-based boosting approach to risk factors for physical intimate partner violence against women and girls in Mexico. Journal of Computational Social Science, 2024. ISSN 2432-2717, 2432-2725. Available at: https://link. springer.com/10.1007/s42001-024-00292-5. DOI:10.1007/s42001-024-00292-5.; Conti, Gabriella, Frühwirth-Schnatter, Sylvia, Heckman, James J., and Piatek, Rémi. Bayesian Exploratory Factor Analysis. Journal of Econometrics, 2014, vol. 183, no. 1, pp. 31–57. ISSN 0304-4076. DOI:10.1016/j.jeconom.2014.06.008.; Gelman, Andrew. Bayesian data analysis. Boca Raton, Fla: Chapman & Hall/CRC, 2004. 2nd ed. ISBN 978-1-58488-388-3.; Travaini, Guido Vittorio, Pacchioni, Federico, Bellumore, Silvia, Bosia, Marta, and De Micco, Francesco. Machine Learning and Criminal Justice: A Systematic Review of Advanced Methodology for Recidivism Risk Prediction. International Journal of Environmental Research and Public Health, vol. 19, no. 17, 2022, pp. 10594. ISSN 1660-4601. DOI:10.3390/ijerph191710594. Available at: https://www.mdpi.com/1660-4601/19/ 17/10594.; Colombia. Ley 1257 de 2008. 2008. Available at: https://www.funcionpublica.gov. co/eva/gestornormativo/norma.php?i=34054.; Colombia. Política Pública Nacional de Equidad de Género para las Mujeres. 2013. Available at: http://www.equidadmujer.gov.co/ejes/Paginas/ politica-publica-de-equidad-de-genero.aspx.; Colombia. Ley 1719 de 2014. 2014. Available at: https://www.funcionpublica.gov. co/eva/gestornormativo/norma.php?i=57716#:~:text=La%20presente%20ley% 20tiene%20por,asociada%20al%20conflicto%20armado%20interno.; Colombia. Política Nacional de Equidad de Género (2015-2030). 2015.; Silva, Luis, Benavides, Alina, and Vidal, Carmen. ANÁLISIS ESPACIAL DE LA MORTALIDAD EN ÁREAS GEOGRÁFICAS PEQUEÑAS. EL ENFOQUE BAYESIANO. Revista Cubana de Salud Pública, vol. 29, no. 4, 2003. Available at: http://scielo.sld.cu/ pdf/rcsp/v29n4/spu04403.pdf.; Pinto-Muñoz, Cristian-Camilo, Zuñiga-Samboni, Jhon-Alex, and Ordoñez-Erazo, Hugo- Armando. Machine Learning Applied to Gender Violence: A Systematic Mapping Study. Revista Facultad de Ingeniería, vol. 32, no. 64, 2023, pp. e15944. ISSN 2357-5328, 0121- 1129. DOI:10.19053/01211129.v32.n64.2023.15944. Available at: https://revistas. uptc.edu.co/index.php/ingenieria/article/view/15944.; Roa Avella, Marcela Del Pilar, Sanabria-Moyano, Jesús Eduardo, and Dinas-Hurtado, Katherin. Herramientas de predicción de violencia basada en género y feminicidio mediante la Inteligencia Artificial. Revista Jurídica Mario Alario D´Filippo, vol. 15, no. 30, 2023, pp. 360–390. ISSN 2256-2796, 2145-6054. DOI:10.32997/2256-2796-vol.15- num.30-2023-4254. Available at: https://revistas.unicartagena.edu.co/index. php/marioalariodfilippo/article/view/4254.; España. VIOGEN. 2007. Available at: https://www.interior.gob.es/opencms/es/ servicios-al-ciudadano/violencia-contra-la-mujer/sistema-viogen/.; Elliott, Paul. Spatial Epidemiology: Methods and Applications. Oxford: Oxford University Press, 2006. Reprint. ISBN 978-0-19-851532-6.; Fleiss, J.L. Statistical Methods for Rates and Proportions. Second Edition. Wiley, John and Sons, Incorporated, New York, N.Y., 1981. Available at: https://books.google. com.co/books?id=I79_MgAACAAJ.; Besag, Julian, York, Jeremy, and Mollie, Annie. Bayesian Image Restoration, with Two Applications in Spatial Statistics. Annals of the Institute of Statistical Mathematics, vol. 43, no. 1, 1991, pp. 1–20. ISSN 0020-3157, 1572-9052. DOI:10.1007/BF00116466. Available at: http://link.springer.com/10.1007/BF00116466; BOX, G., JENKINS, G. Y REINSEL, G., Time series analysis forecasting and control, 4th Ed., Wiley y Sons Inc., 2008.; DRITSAKI, C., Forecast of SARIMA models: An application to unemployment rates of Greece., American Journal of Applied Mathematics and Statistics, 4(5), 136-148, 2016.; DRITSAKIS, N., Y KLAZOGLOU, P., Forecasting unemployment rates in USA using Box- Jenkins methodology., International Journal of Economics and Financial Issues, 8(1), 9, 2018.; GIL, V. D., Pronóstico de la demanda mensual de electricidad con series de tiempo, Revista EIA, 13(26), 111-120, 2016.; HANKE, J. Y WICHERN, D., Pronósticos en los Negocios, 3rd edition, Ed. Pearson, 2010.; HYNDMAN, R.J., Y ATHANASOPOULOS, G., Forecasting: principles and practice, 9na edición, OTexts: Melbourne, Australia. OTexts.com/fpp3, 2021.; ROTHMAN, P., Forecasting Asymmetric Unemployment Rates., Review of Economics and Statistics, 80(1), 164–168, 1998.; TAYLOR E. Y CONTO, R., Comportamiento de la UVR en el largo plazo, Revista Electrónica de Comunicaciones y Trabajos de ASEPUMA, 24(1), 21-34, 2023.; SHINY , Shiny for R Gallery.Available at: https://shiny.posit.co/r/gallery/ 2016; CASELLA, G., & BERGER, R. L.], Statistical Inference. Duxbury Press.2002; FREEDMAN, D. A. Statistical Models: Theory and Practice. Cambridge University Press. 2009; BERNARDO, J.M. AND SMITH, A.F.M., Bayesian Theory,Wiley Series in Probability and Statistics, Wiley, 2009.; GELMAN, A. AND HILL, J., Bayesian Data Analysis, CRC Press, 3rd edition, Boca Raton, FL, 2014.; VANPAEMEL, W., Prior sensitivity in theory testing: An apologia for the Bayes factor, Journal of Mathematical Psychology, vol. 54, N.6, pp. 491-498, 2010.; KASS, R.E. AND RAFTERY, A.E., Bayes Factors, Journal of the American Statistical Association, vol. 90, N.430, pp. 773-795, 1995.; HALPERN, D. (2016). Halpern Critical Thinking Assessment: Test Label HCTA, Versión 51. Mödling, Austria: Schuhfried GmbH.; KLINE, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications; RAYKOV, T., & AMP; MARCOULIDES, G. A. (2012). A first course in structural equation modeling. routledge; LILIANA BLANCO-CASTAÑEDA AND VISWANATHAN ARUNACHALAM., Applied Stochastic Modeling. Springer Nature, 2023.; ELIAS PARDO CAMPO., Estadística Descriptiva Multivariada. Universidad Nacional de Colombia, 2023.; LING WANG, XI-YUAN XIAO, AND JIAN-YAO MENG., Prediction of Air Pollution Based on FCM-HMM Multi-model. University of Science and Technology Beijing, 2016.; JASPER KIRKBY., Cloudy climate change: How clouds affect Earth’s temperature [Vídeo]. 2015. Recuperado de: https://ed.ted.com/lessons/ cloudy-climate-change-how-clouds-affect-earth-s-temperature-jasper-kirkby/ digdeeper.; J. F. CORTÉS AND S. GRANDAS MEDINA., Distrito anunció medidas para hacer más efectivas las restricciones de agua. 2024. Recuperado de: https://bogota.gov.co/mi-ciudad/habitat/ nuevas-medidas-por-racionamiento-de-agua-en-bogota-multas-y-sanciones.; W. I. GALLO APONTE AND A. SANABRIA RODELO., Evaluación de Impacto Ambiental y ganadería extensiva en Colombia. En Lecturas sobre Derecho de Tierras. Tomo III, 375– 406. Universidad Externado de Colombia, 2019.; J. P. HUGHES, P. GUTTORP, AND S. P. CHARLES., A non-homogeneous hidden Markov model for precipitation occurrence. University of Washington, Seattle, USA and Commonwealth Scientific and Industrial Research Organisation, Wembley, Australia, 1998.; Departamento Administrativo Nacional de Estadística, Producto Interno Bruto (PIB) nacional trimestral, 2023, https://www.dane. gov.co/index.php/estadisticas-por-tema/cuentas-nacionales/ cuentas-nacionales-trimestrales/pib-informacion-tecnica; Raquel Prado, Marco Ferreira, Mike West, Time Series, Modeling, Computation and Inference, 2021.; Nelfi Gonzáles, Notas de Clase Estadística III, 2013.; Mike West, Jeff Harrison, Bayesian Forecasting and Dynamic Models, 1997.; Blanco-Castañeda, L., & Arunachalam, V. (2023). Applied Stochastic Modeling. Springer.; Red de Monitoreo de Calidad del Aire de Bogotá. Reporte de estaciones.; MURRAY, J. D., Mathematical Biology, Vol. 1 y 2, 2nd ed., Springer, 2002.; OKUBO, A. Y LEVIN, S. A., Diffusion and Ecological Problems, Modern Perspectives. Interdisciplinary and Applied Mathematics, Springer, 2001; HYNDMAN, R. J. Y ATHANASOPOULOS, G., Forecasting: Principles and Practice (3rd ed), Monash University, Australia.; ZANETTA, F. Y ALLEN, S., Scoringrules: a python library for probabilistic forecast evaluation, 2024, https://github.com/frazane/scoringrules; AMAT RODRIGO, J., Y ESCOBAR ORTIZ, J., skforecast (Version 0.12.1) [Computer software], 2024, https://doi.org/10.5281/zenodo.8382788, https://github. com/JoaquinAmatRodrigo/skforecast; GNEITING, T., BALABDAOUI, F. Y RAFTERY, A. E., Probabilistic forecasts, calibration and sharpness, J. R. Statist. Soc. B, 2007; THORARINSDOTTIR, T. L. Y SCHUHEN, N., Verification: assessment of calibration and accuracy, Norwegian Computing Center, 2017.; GNEITING, T. Y RAFTERY, A. E., Strictly Proper Scoring Rules, Prediction, and Estimation, American Statistical Association, 2007; CASAS-CORDERO VALENCIA, C., ENCINA, J. AND LAHIRI, P., Poverty Mapping for the Chilean Comunas, In M. Pratesi (Ed.), Analysis of Poverty Data by Small Area Estimation, New York: Wiley, 2016.; ISABEL MOLINA, Desagregación de datos en encuestas de hogares: metodologías de estimación en áreas pequeñas. Series Estudios Estadísticos, No 97, (LC/TS.2018/82/Rev.1), Santiago, Comisión Económica para América Latina y el Caribe, (CEPAL), 2019; FAY, R.E. Y HERRIOT, R.A., Desagregación de datos en encuestas de hogares: metodologías de estimación en áreas pequeñas. Estimation of Income for Small Places: An Application of James-Stein Procedures to Census Data, Journal of the American Statistical Association, 74, 269–277., 1979.; INSTITUTO COLOMBIANO PARA LA EVALUACIÓN DE LA EDUCACIÓN - ICFES, Informe Nacional de los Resultados de las Pruebas Saber 3°, 5°, 7° y 9° - 2022. https://www.icfes.gov.co/web/guest/informe-nacional-2022, 2022; M. SUTTON, A., W. ALMQUIST, Z, Small Area Estimation for Demographic Analysis. IntechOpen. doi:10.5772/intechopen.1001842, 2023; DEPARTAMENTO ADMINISTRATIVO NACIONAL DE ESTADÍSTICA - DANE, Desagregación de datos en encuestas de hogares: metodologías de estimación en áreas pequeñas. Boletín técnico - Inseguridad alimentaria a partir de la escala FIES - 2023. https://www.dane.gov.co/files/operaciones/FIES/bol-FIES-2023.pdf, 2024; BUITRAGO-REYES, L. A., GAMBOA, O., HERNÁNDEZ, J., Age, period and cohort effects on mortality from cervical cancer in Colombia between 1985 and 2014, Colombia Médica, Vol. 53, N°1, e2074873–e2074873, 2022.; INTERNATIONAL AGENCY FOR RESEACH ON CANCER, New Global Cancer Data: Globocan, IARC; 2020. Cited 2022.Available from: https://gco.iarc.fr/today; AHMEDIN, J., BRAY, F., CENTER, M., FERLAY, J., WARD, E., Global cancer statistics., Cancer J Clinicians, Vol. 61, N°1, 69-90, 2011.; HOLFORD, T., Understanding the effects of age, period, and cohort on incidence and mortality rates, Ann Rev Publ Health, Vol. 12, N°1, 425-457, 1991; CARSTENSEN, B., Age-period-cohort models for the Lexis diagram., Statistics Med., Vol. 26, N°15, 3018-3045, 2007.; Rodríguez-Yzquierdo, G. A., Basso-de-Figuera, C. A., Díaz-Reyes, G., & León-Pacheco, R. I. (2020). Riego deficitario controlado su efecto sobre la nutrición, productividad y calidad de fruta en maracuyá. Agronomía Mesoamericana, 31(2), 405–418.; García, Y., Rivero, J., & Brito, J. (2012). Evaluación del efecto de dos sistemas de riego sobre el desarrollo vegetativo del cultivo de la parchita maracuyá (Passiflora edulis var. flavicarpa Deneger) en el Valle de Quibor, Venezuela. Irriga, 17, 264–273. https:// doi.org/10.15809/irriga.2012v17n2p264; Franco, C., Mejía, N., Pedersen, S. M., & Gislum, R. (2022). Potential Impact of Learning Management Zones for Site-Specific N Fertilisation: A Case Study for Wheat Crops. Nitrogen, 3(2), 387–403. https://doi.org/10.3390/nitrogen3020025; Boydell, B., & McBratney, A. B. (2002). Identifying potential withinfield management zones from cotton-yield estimates. Precision Agriculture, 3(1), 9–23.; Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. Plenum Press.; Xu, R., & Wunsch, D. C. (2009). Clustering. John Wiley & Sons.; Córdoba, M., Paccioretti, P. A., Giannini Kurina, F., Bruno, C. I., & Balzarini, M. G. (2019). Guía para el análisis de datos espaciales en agricultura (Guide for the analysis of spatial data in agriculture). Retrieved June 19, 2024, from https://ri.conicet.gov. ar/handle/11336/128391; Warrick, A. W., & Nielsen, D. R. (1980). Spatial variability of physical properties in the field. In D. Hillel (Ed.), Applications of soil physics (pp. 319–344). Academic Press.; Sanches, G. M., Magalhães, P. S. G., & Franco, H. C. J. (2019). Site-specific assessment of spatial and temporal variability of sugarcane yield related to soil attributes. Geoderma, 334, 90–98.; Sun, W., Whelan, B. M., Minasny, B., & McBratney, A. B. (2012). Evaluation of a local regression kriging approach for mapping apparent electrical conductivity of soil (ECa) at high resolution. Journal of Plant Nutrition and Soil Science, 175(2), 212–220.; Yang, W., Li, M., Zheng, L., & Sun, H. (2014). Evaluation Model of Winter Wheat Yield Based on Soil Properties. In International Conference on Computer and Computing Technologies in Agriculture (pp. 638–645). Springer International Publishing.; Denora, M., Fiorentini, M., Zenobi, S., Deligios, P. A., Orsini, R., Ledda, L., & Perniola, M. (2022). Validation of rapid and low-cost approach for the delineation of zone management based on machine learning algorithms. Agronomy, 12(1), 183. https: //doi.org/10.3390/agronomy12010183; Moral, F. J., Rebollo, F. J., & Serrano, J. M. (2020). Delineating site-specific management zones on pasture soil using a probabilistic and objective model and geostatistical techniques. Precision Agriculture, 21(3), 620–636.; Pilesjö, P., Thylén, L., & Persson, A. (2005). Topographical data for delineation of agricultural management zones. In Precision Agriculture ‘05 (pp. 819–826).Wageningen Academic. https://doi.org/10.3920/978-90-8686-549-9_100; Serrano, J., Shahidian, S., Paixão, L., Marques da Silva, J., & Moral, F. (2022). Management zones in pastures based on soil apparent electrical conductivity and altitude: NDVI, soil and biomass sampling validation. Agronomy, 12(4), 778. https://doi.org/10. 3390/agronomy12040778; Faleiro, F.G., Junqueira, N.T.V., de Jesús, O.N., Cenci, S.A., Machado, C.F., Rosa, R.C.C., Costa, A.M., Junqueira, K.P., Junghans, T.G. (2020). Maracuyá: Passiflora edulis Sims. In Rodríguez A., Faleiro FG, Parra M., Costa AM (Eds.), Pasifloras - especies cultivadas en el mundo (pp. 15-29). ProImpress-Brasilia, Brazil and Cepass, Neiva, Colombia; AGRONET (2022). Red de información y comunicación del sector agropecuario de Colombia. Retrieved from https://www.agronet.gov.co/estadistica/Paginas/home. aspx?cod=1; MINISTERIO DE AGRICULTURA Y DESARROLLO RURAL, MADR (2021). Cadena de Pasifloras. Indicadores e instrumentos. Retrieved from https://sioc.minagricultura.gov.co/Pasifloras/Documentos/2020-06-30% 20Cifras%20Sectoriales.pdf; Kravchenko, A.N., Bullock, D.G. (2002). Spatial variability of soybean quality data as a function of field topography: I. Spatial data analysis. Crop Science, 42(3), 804–815. 10.2135/cropsci2002.8040; Dray, S., Saïd, S., Débias, F. (2008). Spatial ordination of vegetation data using a generalization of Wartenberg’s multivariate spatial correlation. Journal of Vegetation Science, 19(1), 45-56. 10.3170/2007-8-18312; Moral, F. J., Rebollo, F. J., Serrano, J. M. (2020). Delineating site-specific management zones on pasture soil using a probabilistic and objective model and geostatistical techniques. Precision Agriculture, 21(3), 620-636; Córdoba, M., Balzarini, M., Bruno, C., Costa, J. L. (2012). Identificación de zonas de manejo sitio-específico a partir de la combinación de variables de suelo. Ciencia y Tecnología Agropecuaria, 13(1), 47-54.; Gili, A., Álvarez, C., Bagnato, R., Noellemeyer, E. (2017). Comparison of three methods for delineating management zones for site-specific crop management. Computers and Electronics in Agriculture, 139, 213-223; de Oliveira, J. F., Mayi, S., Marchão, R. L., Corazza, E. J., Hurtado, S. C., Malaquias, J. V., Guimarães, M. D. F. (2019). Spatial variability of the physical quality of soil from management zones. Precision Agriculture, 20, 1251-1273.; Behera, S. K., Mathur, R. K., Shukla, A. K., Suresh, K., Prakash, C. (2018). Spatial variability of soil properties and delineation of soil management zones of oil palm plantations grown in a hot and humid tropical region of southern India. Catena, 165, 251-259.; Varón-Ramírez, V. M., Camacho-Tamayo, J. H., González-Nivia, J. (2018). Management zones determination based on physical properties of the soil. Ciência e Agrotecnologia, 42(3), 248-260.; Esteves, C., Fangueiro, D., Braga, R. P., Martins, M., Botelho, M., Ribeiro, H. (2022). Assessing the Contribution of ECa and NDVI in the Delineation of Management Zones in a Vineyard. Agronomy, 12(6), 1331; Liu, H., Whiting, M. L., Ustin, S. L., Zarco-Tejada, P. J., Huffman, T., Zhang, X. (2018). Maximizing the relationship of yield to site-specific management zones with objectoriented segmentation of hyperspectral images. Precision Agriculture, 19, 348-364.; Scudiero, E., Teatini, P., Manoli, G., Braga, F., Skaggs, T. H., Morari, F. (2018).Workflow to establish time-specific zones in precision agriculture by spatiotemporal integration of plant and soil sensing data. Agronomy, 8(11), 253.; Vasques, G. D. M., Rodrigues, H. M., de Oliveira, R. P., Hernani, L. C., Tavares, S. D. L. (2022). Management zones from proximal soil sensors capture within-field soil property and terrain variations. Precision Agriculture; Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., R. Core Team (2014). nlme: Linear and Nonlinear Mixed Effects Models (R package version 3.1e118). Retrieved from http:// CRAN.R-project.org/package=nlme; Song MA, Reisinger SA, Freudenheim JL, Brasky TM, Mathé EA, McElroy JP, Nickerson QA, Weng DY, Wewers MD, Shields PG. Effects of Electronic Cigarette Constituents on the Human Lung: A Pilot Clinical Trial. Cancer Prev Res (Phila). 2020 Feb;13(2):145-152. doi:10.1158/1940-6207.CAPR-19-0400. Epub 2019 Oct 16. PMID: 31619441; PMCID: PMC7007320.; Park HR, O’Sullivan M, Vallarino J, Shumyatcher M, Himes BE, Park JA, Christiani DC, Allen J, Lu Q. Transcriptomic response of primary human airway epithelial cells to flavoring chemicals in electronic cigarettes. Sci Rep. 2019 Feb 1;9(1):1400. doi:10.1038/s41598-018-37913-9. PMID: 30710127; PMCID: PMC6358614; Zhang H, Rostamim MR, Leopold PL, Mezey JG, O’Beirne SL, Strulovici-Barel Y, Crystal RG. Reply to Sharma and Zeki: Does Vaping Increase Susceptibility to COVID-19? Am J Respir Crit Care Med. 2020 Oct 1;202(7):1056-1057. doi:10.1164/rccm.202006-2351LE. PMID: 32749854; PMCID: PMC7528781.; Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43(7), e47.; Love, M.I., Huber, W., Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biology 15(12):550 (2014); Andersen, A., Reimer, R., Dawes, K., Becker, A., Hutchens, N., Miller, S., . . . Philibert, R. (2022). DNA methylation differentiates smoking from vaping and non-combustible tobacco use. Epigenetics, 17(2), 178–190. https://doi.org/10.1080/15592294.2021.1890875; Richmond, R.C., Sillero-Rejon, C., Khouja, J.N. et al. Investigating the DNA methylation profile of e-cigarette use. Clin Epigenet 13, 183 (2021). https://doi.org/10.1186/s13148-021-01174-7; Wilson, R., Wahl, S., Pfeiffer, L. et al. The dynamics of smoking-related disturbed methylation: a two time-point study of methylation change in smokers, non-smokers and former smokers. BMC Genomics 18, 805 (2017). https://doi.org/10.1186/s12864-017-4198-0; Min-Ae Song; Jo L. Freudenheim; Theodore M. Brasky; et al. Biomarkers of Exposure and Effect in the Lungs of Smokers, Nonsmokers, and Electronic Cigarette Users. American Association for Cancer Research. Cancer Epidemiol Biomarkers Prev (2020) 29 (2): 443–451. https://doi.org/10.1158/1055-9965.EPI-19-1245; Jason K. Freels, William Q. Meeker, and Luis A. Escobar, SMRD: Statistical Methods for Reliability Data, 2020.; Luis A. Escobar, Francis G. Pascual,William Q. Meeker, Statistical Methods for Reliability Data, 2021.; Jason K. Freels, William Q. Meeker, and Luis A. Escobar, SMRD: Statistical Methods for Reliability Data, 2020; D’AMATO, G., BAENA-CAGNANI, C., CECCHI, L., ANNESI-MAESANO, I., NUNES, C.,ANSOTEGUI, I., D’AMATO, M., LICCARDI, G., SOFIA, M., CANONICA, W., (2013) Climate change, air pollution and extreme events leading to increasing prevalence of allergic respiratory diseases. 8, 1-9.; NATIONAL HEART, LUNG AND BLOOD INSTITUTE, Pneumonia Recovery, 2022. Recuperado de https://www.nhlbi.nih.gov/health/pneumonia/recover; RÍOS-GUTIÉRREZ A, ARUNACHALAM V. , (2023) Modelización, análisis y estimación en un modelo epidemiológico con perturbaciones aleatorias: una aplicación a las infecciones respiratorias agudas en Bogotá. Memorias del 32 Simposio Internacional de Estadística, Ibagué (Tolima), Colombia https://sites.google.com/unal.edu.co/ simposioestadistica/inicio/memorias; RÍOS-GUTIÉRREZ A, TORRES S, ARUNACHALAM V. , (2023) An updated estimation approach for SEIR models with stochastic perturbations: Application to COVID-19 data in Bogotá. PLOS ONE 18(8): e0285624. https://doi.org/10.1371/journal.pone. 0285624; Bundesliga. (2024). Clasificación Bundesliga 2023-2024. Recuperado de https://www.bundesliga.com/es/bundesliga/clasificacion.; Johnson, D. E. (1999). Métodos Multivariados Aplicados al Análisis de Datos. Ediciones Paraninfo. ISBN 9789687529905.; Sumpter, D. (2016). Soccermatics: Mathematical Adventures in the Beautiful Game. Bloomsbury Publishing.; Jackson, J. E. (2003). A User’s Guide to Principal Components. Wiley- Interscience. ISBN 978-0471471349; BLANCO, Liliana; ARUNACHALAM, Viswanathan; DHARMARAJA, Selvamuthu: Introduction to Probability and Stochastic Processes with Applications. John Wiley & Sons, 2012.; DIEKER, Ton: Simulation of Fractional Brownian Motion, Masters Thesis, Department of Mathematical Sciences, University of Twente, 2004.; FRIEDRICH, Jan; GALLON, Sebastian; PUMIR, Alain; GRAUER, Rainer: Stochastic Interpolation of Sparsely Sampled Time Series via Multipoint Fractional Brownian Bridges. In: Physical Review Letters 125 (2020), No. 17, p. 170602; KRANSTAUBER, Bart; KAYS, Roland; LAPOINT, Scott D.;WIKELSKI, Martin; SAFI, Kamran: A Dynamic Brownian Bridge Movement Model to Estimate Utilization Distributions for Heterogeneous Animal Movement. In: Journal of Animal Ecology 81 (2012), No. 4, p. 738–746.; YUAN, Chenggui; MAO, Xuerong: Convergence of the Euler–Maruyama Method for Stochastic Differential Equations with Markovian Switching. In: Mathematics and Computers in Simulation 64 (2004), No. 2, p. 223–235.; CHEN, Y.; GEORGIOU, T.: Stochastic Bridges of Linear Systems. In: IEEE Transactions on Automatic Control 61 (2015), No. 2, p. 526–531.; WANG, S.; RAMKRISHNA, D.; NARASIMHAN, V.: Exact Sampling of Polymer Conformations Using Brownian Bridges. In: The Journal of Chemical Physics 153 (2020), No. 3, p. 034901.; KRANSTAUBER, B.: Modelling Animal Movement as Brownian Bridges with Covariates. In: Movement Ecology 7 (2019), No. 1, p. 1–10.; CAPSTICK, S. B., WHITMARSH, L. AND POORTINGA, W., Public engagement with climate change: Comparing different public engagement interventions for their impact on public attitudes and behaviour, Global Environmental Change, vol. 72, p. 102431, 2022. DOI:10.1016/j.gloenvcha.2022.102431; CORPORACIONES AUTÓNOMAS REGIONALES, GUÍA METODOLÓGICA PARA EL CÁLCULO DE LA HUELLA DE CARBONO CORPORATIVA A NIVEL SECTORIAL, 2013, https://www.car.gov.co/uploads/files/5ade1b0319769.pdf; GILLINGHAM, K. AND STOCK, J. H. The cost of reducing greenhouse gas emissions, Journal of Economic Perspectives, vol. 32, no. 4, pp. 53-72, 2018.; GOIB Govern de les Illes Balears. GUÍA PRÁCTICA PARA EL CÁLCULO DE EMISIONES DE GASES DE EFECTO INVERNADERO (GEI)). CAIB, 2011, https://www.caib. es/sacmicrofront/archivopub.do?ctrl=MCRST234ZI97531&id=97531; SCHEAFFER, R. L., MENDENHALL, W. Y OTT, L. Elementos de Muestreo. 3a edición. Grupo Editorial Iberoamericana, 1987.; THOMPSON, S. K. Sampling, John Wiley & Sons, New York, 1992.; SANTAMARÍA S, TELENCHANA E, BERNARD B, HIDALGO S, BEATE B, CÓRDOVA M, NARVÁEZ D, Registro de erupciones ocurridas en los Andes del Norte durante el Holoceno: Nuevos resultados obtenidos en la turbera de Potrerillos, Complejo Volcánico Chiles-Cerro Negro, Revista Politécnica, vol. 39, no. 2, pp. 1–9, Jul. 2017. [Online]. Available: http://scielo.senescyt.gob.ec/scielo.php?script= sci_arttext&pid=S1390-01292017000200007; INSTITUTO GEOFÍSICO DE LA ESCUELA POLITÉCNICA NACIONAL, OBSERVATORIO VULCANOLÓGICO Y SISMOLÓGICO DE PASTO DEL SERVICIO GEOLÓGICO COLOMBIANO, Informe Especial Complejo Volcánico Chiles-Cerro Negro – 2019 - N° 1 “Actualización del estado de actividad” vol. 1, pp. 1–9, Aug. 2019. [Online]. Available: https://www2.sgc.gov.co/Noticias/boletinesDocumentos/ INFORME%20ESPECIAL%20CHILES-CERRO%20NEGRO%20-%202019%20-%20N1.pdf; SPARKS R, ASPINALL W, CROSWELLER H, HINCKS T, Risk and uncertainty assessment of volcanic hazards , Risk and Uncertainty Assessment for Natural Hazards, vol. 9781107006195, Cambridge University Press, 2011, pp. 364–397. doi:10.1017/CBO9781139047562.012.; SHERIDAN M, PATRA A, DALBEY K, HUBBARD B, Probabilistic digital hazard maps for avalanches and massive pyroclastic flows using TITAN2D, Special Paper of the Geological Society of America, vol. 464, no. 14, pp. 1–12, 2010, doi:10.1130/2010.2464(14).; BOX,G.E.P AND WILSON, K.B., On the experimental attainment of optimum conditions, Journal of the Royal Statistical Society 13,1-45, 1951.; DE ZAN, A. T., Principios de metodología de superficie de respuesta para modelos logísticos. Universitat Politécnica de Catalunya. Barcelona, 2006.; FAMOYE, F. AND SINGH, K. P., Zero-inflated generalized Poisson regression model with an application to domestic violence data, Journal of Data Science, 4 (1), 117-130, 2006.; HALL, D. B., AND BERENHAUT, K. S., Score tests for heterogeneity and overdispersion in zero-inflated Poisson and Binomial regression models, Canadian Journal of Statistics, 30(3), 415-430, 2002; LI, C. S., (2012). Score tests for semiparametric zero-inflated Poisson models, International Journal of Statistics and Probability, 1 (2), 1-7, 2012.; MELO, O. O., LÓPEZ, L. A. AND MELO, S. E., Diseño de experimentos: métodos y aplicaciones. Universidad Nacional de Colombia. Facultad de Ciencias, 2020.; RIDOUT,M.,HINDE J. AND DEMETRIO, C., A score test for testing a zero inflated Poisson regression model against zero inflated negative binomial alternatives, Biometrics 57, 219- 223, 2001; XIANG, L., LEE, A. H., YAU, K. K. AND MCLACHLAN, G. J., A score test for overdispersion in zero-inflated Poisson mixed regression model, Statistics in Medicine, 26 (7), 1608, 2007.; J. O. RAMSAY, B. W. SILVERMAN, Functional Data Analysis, Springer, 2005, New York, ISBN 978-0-387-40080-8, https://doi.org/10.1007/b98888; GHIGLIETTI, ANDREA AND IEVA, FRANCESCA AND PAGANONI, ANNA, Statistical inference for stochastic processes: Two-sample hypothesis tests Journal of Statistical Planning and Inference. 180. 49-68. 10.1016/j.jspi.2016.08.004, 2017.; IAN L. DRYDEN, ALEXEY KOLOYDENKO, DIWEI ZHOU, Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging The Annals of Applied Statistics, Ann. Appl. Stat. 3(3), 1102-1123, (September 2009).; FEBRERO-BANDE M, OVIEDO DE LA FUENTE M, Statistical Computing in Functional Data Analysis: The R Package fda.usc Journal of Statistical Software, 51(4), 1–28. https://www.jstatsoft.org/v51/i04/, 2012; PEDREGOSA, F., VAROQUAUX, GA.EL, GRAMFORT, A., MICHEL, V., THIRION, B., GRISEL, O., . . . OTHERS, Scikit-learn: Machine learning in Python Journal of Machine Learning Research, 12(Oct), 2825–2830, 2011; [Chang et al. (2023)] CHANG, W., CHENG, J., ALLAIRE, J.J., SIEVERT, C., SCHLOERKE, B., XIE, Y., ALLEN, J., MCPHERSON, J., DIPERT, A., BORGES, B. shiny: Web Application Framework for R. R package version 1.7.5. Available online: https://CRAN. R-project.org/package=shiny (accessed 2023).; [Martin et al. (2011)] MARTIN, A.D., QUINN, K.M., PARK, J.H. MCMCpack: Markov Chain Monte Carlo in R. Journal of Statistical Software 2011, 42, 1–22. 10.18637/jss.v042.i09.; [Gelman et al. (2013)] GELMAN, A., CARLIN, J.B., STERN, H.S., DUNSON, D.B., VEHTARI, A., RUBIN, D.B. Bayesian Data Analysis, Third Edition. Chapman & Hall/CRC Texts in Statistical Science. Taylor & Francis: Boca Raton, FL, 2013. ISBN 9781439840955. Available online: https://books.google.com.co/books?id=ZXL6AQAAQBAJ (accessed 2013).; SCHLATTMANN, P., Medical applications of finite mixture models, Vol. 1, Springer, 2009.; DO, C. B. Y BATZOGLOU, S., What is the expectation maximization algorithm?, Nature biotechnology, 26(8), 897-899., 2008.; TRANG, N. V., CHOISY, M., NAKAGOMI, T., CHINH, N. T. M., DOAN, Y. H., YAMASHIRO, T. Y ANH, D. D., Determination of cut-off cycle threshold values in routine RT–PCR assays to assist differential diagnosis of norovirus in children hospitalized for acute gastroenteritis., Epidemiology & Infection, 143(15), 3292-3299., 2015.; OAKES, D., Direct calculation of the information matrix via the EM algorithm., R Statist Soc B 61:479–482., 1999; RESOLUCIÓN 268 DE 2020 [INSTITUTO COLOMBIANO PARA LA EVALUACIÓN DE LA EDUCACIÓN]., Por la cual se reglamentan las metodologías para la generación de resultados de los exámenes de Estado y se dictan otras disposiciones., 5 de junio de 2020.; KE, G., MENG, Q., FINLEY, T., WANG, T., CHEN, W., MA, W. Y LIU, T.-Y., Lightgbm: A highly efficient gradient boosting decision tree., Advances in Neural Information Processing Systems, 30, 3146–3154, 2017.; LI, Z. S., YAO, X., LIU, Z. G. Y ZHANG, J. C., Feature selection algorithm based on LightGBM., Journal of Northeastern University (Natural Science), 42(12), 1688, 2021.; SYAFEI, R. M., Y EFRILIANDA, D. A., Machine Learning Model Using Extreme Gradient Boosting (XGBoost) Feature Importance and Light Gradient Boosting Machine (LightGBM) to Improve Accurate Prediction of Bankruptcy., Recursive Journal of Informatics, 1(2), 64-72, 2023.; INSTITUTO COLOMBIANO PARA LA EVALUACIÓN DE LA EDUCACIÓN., ¿Cómo se construye el Índice de Nivel Socioeconómico (INSE) en el contexto de las pruebas Saber?., Saber al Detalle Edición 4, 2019; SAURABH, H. AND NOURIVANDI, T. , Time to retire F1-binary score for action unit detection, Vol.182, pag.(111-117), Pattern Recognition Letters, 2024.; TAGHREED H. ALMUTAIRI AND SUNDAY O. OLATUNJI, The utilization of AI in healthcare to predict no-shows for dental appointments: A case study conducted in Saudi Arabia., Vol.(46), Pag.101472, Informatics in Medicine Unlocked, 2024.; MOHAMMADI, IMAN AND WU, HUANMEI AND TURKCAN, AYTEN AND TOSCOS, TAMMY AND DOEBBELING, BRADLEY N, Data analytics and modeling for appointment no-show in community health centers, Vol.(9), Pag.(2150132718811692), Journal of primary care & community health, 2018.; ARNOLD, B.C., GÓMEZ, H. AND SALINAS, H. (2009). On multiple contraint skewed model. Scandinavian Journal of Statistics 43(3), 279–293.; BIRNBAUM, Z.W. AND SAUNDERS, S.C. (1969). A new family of life distributions Journal of Applied Probability 6(2), 319–327.; BOLFARINE, H., MARTÍNEZ-FLÓREZ, G., SALINAS, H. (2018). Bimodal symmetricasymmetric power-normal families. Communications in Statistics - Theory and Methods 47(2), 259–276.; ELAL-OLIVERO, D. (2010). Alpha-skew-normal distribution. Proyecciones Journal of Mathematics 29(3), 224–240.; ELAL-OLIVERO, D., GÓMEZ, H.W., AND QUINTANA, F.A. (2009). Bayesian modeling using a class of bimodal skew-elliptical distributions. Journal of Statistical Planning and Inference 139(4), 1484–1492; ELAL-OLIVERO, D., OLIVARES-PACHECO, J., GÓMEZ, H., BOLFARINE, H. (2009). A new class of non negative distributions generated by symmetric distributions. Communications in Statistics - Theory and Methods 38(7), 993–1008.; GÓMEZ, H.W., ELAL-OLIVERO, D., SALINAS, H.S., AND BOLFARINE, H. (2011). Bimodal extension based on the skew-normal distribution with application to pollen data. Environmetrics 22(1), 50-62.; KIM, H.J. (2005). On a class of two-piece skew-normal distributions. Statistics 39(6), 537– 553.; MARTÍNEZ-FLÓREZ, G.; MARTÍNEZ, E.; TOVAR-FALÓN, R.; GÓMEZ, H.W. (2022) A family of bimodal distributions generated by distributions with positive support. Journal of Applied Statistics 14, 3614-3637.; MARTÍNEZ-FLÓREZ, G., TOVAR-FALÓN, R., GÓMEZ. H.W. (2020). Bivariate powerskew- elliptical distribution. Symmetry 12(8), 1327–1347.; DOORNIK, J.A.; HANSEN, H. An Omnibus Test for Univariate and Multivariate Normality. Oxford Bulletin of Economics and Statistics 2008, 70, 927-939.; DURRANS, S.R. Distributions of fractional order statistics in hydrology. Water Resour. Res. 1992, 28, 1649–1655; GUPTA, R.C.; GUPTA, R.D. Generalized skew normal model. Test. 2004, 13, 501–524.; HENZE, N.; ZIRKLER, B. A Class of Invariant Consistent Tests for Multivariate Normality. Communications in Statistics - Theory and Methods 1990, 19, 3595–3617; LEHMANN, E.L. The power of rank Tests. Ann. Math. Statist. 1953, 24, 23–243.; MARTÍNEZ-FLÓREZ, G., BOLFARINE, H. AND GÓMEZ, H. W. Skew-normal Alpha-Power Model. Statistics. 2014, 48, 1414-1428; MARTÍNEZ-FLÓREZ, G.; ARNOLD, B.C.; BOLFARINE,H.; GÓMEZ, H.W. The multivariate alphapower model. Journal of Statistical Planning and Inference 2013, 143, 1244–1255; MARTÍNEZ-FLÓREZ, G.; TOVAR-FALÓN, R.; GÓMEZ, H.W. Bivariate Power-Skew-Elliptical Distribution. Symmetry 2020, 12, 1327.; PEWSEY, A.; GÓMEZ, H.W.; BOLFARINE, H. Likelihood-based inference for power distributions. Test 2012, 21, [Royston (1983)] ROYSTON, J.P. Some Techniques for Assessing Multivarate Normality Based on the Shapiro- Wilk W. Journal of the Royal Statistical Society, Series C 1983, 32, 121–133.; Cragg, J. Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica 1971, 39, 829–844.; Díaz-García, J.A.; Leiva-Sánchez, V. A new family of life distributions based on the elliptically contoured distributions. Journal of Statistical Planning and Inference 2005, 128, 445–457.; Kumaraswamy, P. A generalized probability density function for double-bounded random processes. Journal of Hydrology 1980, 46, 79–88.; Lemonte, A.J. Multivariate Birnbaum-Saunders regression model. Journal of Statistical Computation and Simulation 2013, 46, 2244–2257.; Martínez-Flórez, G.; Bolfarine, H.; Gómez, H.W. An alpha-power extension for the Birnbaum-Saunders distribution. Statistics 2014, 48, 896–912.; Martínez-Flórez, G.; Tovar-Falón, R.; Barrera-Causil, C. Inflated Unit-Birnbaum-Saunders Distribution. Mathematics 2022, 10, 667.; Mazucheli, J.; Menezes, A. F. B.; Dey, S. The unit-Birnbaum-Saunders distribution with applications. Chilean Journal of Statistics 2018, 9, 47–57.; Ospina, R.; Ferrari, S.L.P. Inflated beta distribution. Statistical Papers 2010, 51, 111–126.; Vilca-Labra, F.; Leiva-Sánchez, V. A new fatigue life model based on the family of skewelliptical distributions. Communications in Statistics - Theory and Methods 2006, 35, 229– 244.; ARELLANO-VALLE, R.B.; AZZALINI, A. The centred parametrization for the multivariate skew-normal distribution. Journal of Multivariate Analysis 2008, 99, 1362–1382.; ARELLANO-VALLE, R.B.; CASTRO, L.M.; GONZÁLEZ-FARIAS, G.; MUÑÓZGAJARDO K.A. Student-t censored regression model: properties and inference. Statistical Methods and Applications 2012, 21, 453–473.; AZZALINI, A.; CAPPELLO, D.; KOTZ, S. Log-skew-normal and log-skew-t distributions as models for family income data. Journal of Incone Distribution 2002, 11, 12–20.; CHAI, H.; BAILEY, K. Use of log-normal distribution in analysis of continuous data with a discrete component at zero. Statistics in Medicine 2008, 27, 3643–3655.; JOB, J.; HALSEY, N.; BOULOS, R.; HOLT, E.; FARREL, D.; ALBRECHT, P.; BRUTUS, J.; ADRIEN, M.; ANDRE, J.; CHAN, E.; KISSINGER, P.; BOULOS, C. The cite soleil/JHU project team. Successful immunization of infants at 6 months of age with high dose Edmonston-Zagreb measles vaccine. Pediatric Infectious Disease Journal 1991, 30, 303–311.; MARTÍNEZ-FLÓREZ, G.; VERGARA-CARDOZO, S.; TOVAR-FALÓN, R. A Class of Exponentiated Regression Model for Non Negative Censored Data with an Application to Antibody Response to Vaccine. Symmetry 2021, 13, 1419.; MOULTON, L.; HALSEY, N. A mixture model with detection limits for regression analyses of antibody response to vaccine. Biometrics 1995, 51, 1550–1578.; MOULTON, L.; HALSEY, N. A mixed gamma model for regression analyses of quantitative assay data. Vaccine 1996, 14, 1154–1158.; OWEN, D.B. Tables for computing bi-variate normal probabilities. Annals Mathematical Statistics 1956, 27,1075–1090.; TOBIN, J. Estimation of relationships for limited dependent variables. Econometrica 1958, 26, 24–36.; Plain English AI. GoogLeNet (InceptionV1) with TensorFlow. Recovered from https://ai.plainenglish.io/googlenet-inceptionv1-with-tensorflow-9e7f3a161e87. Online. 2021.; Arrieta, M.E. Material de clase Análisis de regresión. Semana 17. Online. 2023.; Géron, A. “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow”. O’REILLY 2 (2019).; Haolun S., Yuping Y., Liangliang W., Da M., Mirza F. B., Jian P. y Jiguo C. Two- Dimensional Functional Principal Component Analysis for Image Feature Extraction. Recovered from https://doi.org/10.1080/10618600.2022.2035738. Online. 2022.; Navoneel Chakrabarty. Brain MRI Images for Brain Tumor Detection. Recovered from https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection. Online. 2019.; Zelada, C. Evaluación de modelos de clasificación. Recuperado de https://rpubs.com/chzelada/275494. Online. 2017.; Jospin, L. V., Laga, H., Boussaid, F., Buntine, W., & Bennamoun, M. (2022). Hands-on Bayesian neural networks—A tutorial for deep learning users. IEEE Computational Intelligence Magazine, 17(2), 29-48.; Kingma, D. P., Salimans, T., & Welling, M. (2015). Variational dropout and the local reparameterization trick. Advances in neural information processing systems, 28.; Manuel Febrero-Bande & Wenceslao González-Manteiga, 2013. "Generalized additive models for functional data,"TEST: An Official Journal f the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 278-292, June.; Breast Ultrasound Images Dataset. Recovered from https://www.kaggle.com/datasets/aryashah2k/breast-ultrasound-images-dataset; A. Fernández, Regresión logística en R,.Ander Fernández. [En línea]. Disponible: https://anderfernandez.com/blog/regresion-logistica-r/. [Accedido: 14-jun-2024].; Capítulo 4: Análisis Discriminante, Çlassification: Chapter 4,ÇDR, 2021. [En línea]. Disponible: https://cdr-book.github.io/cap-discriminante.html. [Accedido: 14-jun-2024].; MINISTERIO DE SALUD Y PROTECCIÓN SOCIAL, Mortalidad en Colombia, período 2020- 2021: Medición de la Mortalidad por todas las causas y Covid-19. (2022); DÁVILA, D. & PARDO, A., Violencia y accidentes mortales: análisis de la mortalidad por causas externas en Colombia y México, 1998-2015. (2019); MARTÍNEZ, S, Cuatro ensayos sobre la mortalidad por causas externas en Colombia. (2020); Bao, Yukun; Xiong, Tao; Hu, Zhongyi: Multi-step-ahead time series prediction using multiple-output support vector regression. En: Neurocomputing 129 (2014), p. 482–493; Cortes, C., Vapnik, V. (1995) Support-vector network.; Carmona, E. (2016). Tutorial sobre Máquinas de Vector de Soporte (SVM). Dpto. de Inteligencia ArtifiCial, ETS de Ingeniería Informática, Universidad Nacional de Educación a Distancia (UNED).; Smola, Alex J.; Schölkopf, Bernhard: A tutorial on support vector regression. En: Statistics and computing 14 (2004), p. 199–222; Salinas-Rodríguez A., Pérez-Núñez R., & Ávila-Burgos L. (2006). Modelos de regresión para variables expresadas como una proporción continua. Salud pública de México, 48, 395-404. , 48(5), 395-404.; Kieschnick, R., & McCullough, B. D. (2003). Regression analysis of variates observed on (0, 1): percentages, proportions and fractions. Statistical modelling, 3(3), 193-213.; Ghiassi, M., Saidane, H., and Zimbra, D. (2005). A dynamic artificial neural network model for forecasting time series events.; Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model; Khashei, M., and Bijari, M. (2011). A new hybrid methodology for forecasting time series: Application of the combined artificial neural networks and ARIMA models.; Pai, P. F., and Lin, C. S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting.; Gao, S., and Li, S. (2008). Forecasting the US unemployment rate with a new hybrid method; Assaad, M., Bone, R., and Cardot, H. (2008). A hybrid approach for time series forecasting: ARIMA, neural networks and support vector machines; LI, H., AND ZUO, H., AND SU, Y., AND XU, J, AND YIN, Y., Study on segmented distribution for reliability evaluation, Chin J Aeronaut, 2016, 30(1), 310-329; MARTÍNEZ-FLÓREZ, G., AND MORENO-ARENAS, G., AND VERGARA-CARDOSO, S., Properties and Inference for Proportional Hazard Models., Colombian Journal of Statistics, 2013, 36, 95-114.; MAZUCHELI, J., AND COELHO-BARROS, E.A., AND ACHCAR, J.A., Inferences for the change-point of the exponentiatedWeibull hazard function., REVSTAT, 2012, 10(3), 309- 322.; FRIEDMAN, M., Piecewise exponential models for survival data with covariates., Ann Statist, 1982, 10, 101-113.; GÓMEZ, Y. M., AND GALLARDO, D. I., AND ARNOLD, B. C., The power piecewise exponential model., Journal of Statistical Computation and Simulation, 2018, 88(5), 825- 840.; S. M. R. Hasan and A. Dhakal, .Obfuscated Malware Detection: Investigating Real-world Scenarios through Memory Analysis," Proc. 2023 5th International Conference on Telecommunication and Photonics (ICTP), vol. 1, pp. 1–9, 2023. doi:10.1109/ICTP60248.2023.10490701.7.; Canadian Institute for Cybersecurity, ÇIC-MalMem-2022,"2022. [Online]. Disponible: https://www.unb.ca/cic/datasets/malmem-2022.html; H. El Merabet and A. Hajraoui, .A survey of malware detection techniques based on machine learning,"International Journal of Advanced Computer Science and Applications (IJACSA), vol. 10, no. 1, pp. 31-40, 2019. doi:10.14569/IJACSA.2019.0100148.; J. Friedman, T. Hastie, and R. Tibshirani, Regularization Paths for Generalized Linear Models via Coordinate Descent,"Journal of Statistical Software, vol. 33, no. 1, pp. 1-22, 2010. doi:10.18637/jss.v033.i01.; FERRATY, F. Y VIEU, P., Nonparametric functional data analysis: theory and practice., Springer Science & Business Media, 2006; GIRALDO, R., Modelos de Regresión en Aprendizaje Estadístico., Universidad Nacional de Colombia, 2018.; HOFF, P. D., A first course in Bayesian statistical methods., Springer, 2009.; RAMSAY, J. Y SILVERMAN, B. W., Functional Data Analysis, Springer-Verlag, 2005.; DEPARTAMENTO ADMINISTRATIVO NACIONAL DE ESTADÍSTICA, Fuente: Departamento Administrativo Nacional de Estadística, accessed July 31, 2024. Available at: http://microdatos. dane.gov.co/www.dane.gov.co.; DEPARTAMENTO ADMINISTRATIVO NACIONAL DE ESTADÍSTICA, Proyecciones y retroproyecciones de población departamental para el periodo 2020-2050 con base en el CNPV 2018 por área, sexo y edad, 2020. Available at: https://www.dane.gov.co/index.php/ estadisticas-por-tema/demografia-y-poblacion/proyecciones-de-poblacion.; GREENWELL, B. M., MCCARTHY, A. J., BOEHMKE, B. C., AND LIU, D., Residuals and Diagnostics for Binary and Ordinal Regression Models: An Introduction to the SURE Package, The R Journal, Vol. 10, No. 1, 2018. Available at: https://journal.r-project.org/articles/ RJ-2018-004/RJ-2018-004.pdf.; MAYO CLINIC, Drug Addiction (Substance Use Disorder) - Symptoms and Causes, October 4, 2022. Accessed July 31, 2024. Available at: https://www.mayoclinic.org/diseases-conditions/ drug-addiction/symptoms-causes/syc-20365112; TULU, S. K., AND KESKIS, W., Assessment of Causes, Prevalence and Consequences of Alcohol and Drug Abuse among Mekelle University, CSSL 2nd Year Students, American Journal of Applied Psychology, Vol. 3, No. 3, pp. 47–56, 2015. Available at: https://doi.org/10.12691/ ajap-3-3-1.; VANEGAS, LUIS HERNANDO, RONDÓN, LUZ MARINA, AND PAULA, GILBERTO A., glmtoolbox: Set of Tools to Data Analysis using Generalized Linear Models, R package version 0.1.11, 2024. Available at: https://CRAN.R-project.org/package=glmtoolbox.; HE, HUA, ZHANG, HUI, YE, PENG, AND TANG, WAN, A test of inflated zeros for Poisson regression models, Statistical Methods in Medical Research, Vol. 28, pp. 1157–1169, 2019.; AGRESTI, ALAN, Categorical Data Analysis, 3rd edition, John Wiley & Sons, 2012. Hoboken, NJ.; GREENE, WILLIAM H, Econometric Analysis, 5th edition, International edition, Prentice Hall, 2002. New Jersey.; STAUDEMEYER, RALF C. AND ROTHSTEIN MORRIS, ERIC, Understanding LSTM – a tutorial into Long Short-Term Memory Recurrent Neural Networks, arXiv:1909.09586, 2019. https://arxiv.org/abs/1909.09586; G, PRITHIVRAJ AND KUMARI, ALKA, Identification and Classification of Exoplanets Using Machine Learning Techniques, arXiv:2305.09596, 2023. https://arxiv.org/ abs/2305.09596; FISCHER, DEBRA A. AND VALENTI, JEFF, The Planet-Metallicity Correlation, 622(2), 1102, The Astrophysical Journal, 2005. https://dx.doi.org/10.1086/428383; BELHE, MRS. SAYALI, CHORDIYA, DISHA, AND KADU, MS. SWATI, Searching Exoplanets using Artificial Intelligence, International Journal of Technology Engineering Arts Mathematics Science, 2023.; ZHAO, BENDONG, LU, HUANZHANG, CHEN, SHANGFENG, LIU, JUNLIANG, AND WU, DONGYA, Convolutional neural networks for time series classification, Journal of Systems Engineering and Electronics, 2017.; LISSAUER, JACK J., BATALHA, NATALIE M., AND BORUCKI, WILLIAM J., Exoplanet Science From Kepler, arXiv:2311.04981, 2023.; WOLSZCZAN, ALEKSANDER AND FRAIL, DAVID A., A planetary system around the millisecond pulsar PSR1257 + 12, 355, 145–147, Nature, 1992. https://doi.org/10.1038/ 355145a0; MORGAN, W. W. AND KEENAN, P. C., Spectral Classification, 11, 29–50, Annual Review of Astronomy and Astrophysics, 1973. https://doi.org/10.1146/annurev.aa. 11.090173.000333; BRYAN LIM, SERCAN O. ARIK, NICOLAS LOEFF, TOMAS PFISTER, Temporal Fusion Transformers for interpretable multi-horizon time series forecasting, International Journal of Forecasting, Volume 37, Issue 4, 2021, Pages 1748-1764, ISSN 0169-2070, https:// www.sciencedirect.com/science/article/pii/S0169207021000637.; BERNER, JULIUS, PHILIPP GROHS, GITTA KUTYNIOK, PHILIPP PETERSEN, The Modern Mathematics of Deep Learning, CoRR, Vol. abs/2105.04026, 2021, https://arxiv.org/ abs/2105.04026.; SALIMANS, TIM, DIEDERIK P. KINGMA, Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks, CoRR, Vol. abs/1602.07868, 2016, http://arxiv.org/abs/1602.07868; YU, JIAHUI, YUANZHONG XU, JING YU KOH, THANG LUONG, GUNJAN BAID, ZIRUI WANG, VIJAY VASUDEVAN, ALEXANDER KU, YINFEI YANG, BURCU KARAGOL AYAN, BEN HUTCHINSON, WEI HAN, ZARANA PAREKH, XIN LI, HAN ZHANG, JASON BALDRIDGE, YONGHUI WU, Scaling Autoregressive Models for Content-Rich Textto- Image Generation, arXiv, 2022, https://arxiv.org/abs/2206.10789.; BURKOV, ANDRIY, The Hundred-Page Machine Learning Book, Andriy Burkov, 2019, ISBN 9781999579517, https://books.google.com.co/books?id=0jbxwQEACAAJ.; ALVAR, J., VÉLEZ, I. D., BERN, C., HERRERO, M., DESJEUX, P., CANO, J., . TEAM, W. H. O. L. C. , Leishmaniasis worldwide and global estimates of its incidence, PLoS one, 7(5), e35671, 2012; BARROSO, La presencia de ADN de Leishmania braziliensis en la mucosa nasal de pacientes con leishmaniasis cutánea y la búsqueda de posibles patrones clínicos e inmunológicos de progresión de la enfermedad: un estudio transversal. Fronteras en microbiología celular y de infecciones, 11, 744163, 2021.; CALAO, Y BULA , Prevalencia de leishmaniasis cutánea en el departamento de Córdoba, Colombia 2016– 2020. Trabajo de grado de especialista en Epidemiología, Fundación Universitaria del Área Andina, 2021.; DEL ROSAL, T., BAQUERO, F. Y GARCÍA, M., Leishmaniasis cutánea. Revista Pediatría de Atención Primaria. Vol. XII(46), 2010.; INSTITUTO NACIONAL DE SALUD (INS)., Boletín Epidemiológico: Leishmaniasis cutánea en Colombia 2018-2022. Obtenido de https://www.ins.gov.co, 2023.; ORGANIZACIÓN PANAMERICANA DE LA SALUD., Leishmaniasis: informe epidemiológico de las Américas. Núm. 12, diciembre del 2023.; SALAZAR MORA, J. D. , Modelo predictivo para la ocurrencia de leishmaniasis cutánea en Colombia, a partir de variables ambientales y socioeconómicas. Doctoral dissertation, Universidad Nacional de Colombia, 2021; WORLD HEALTH ORGANIZATION, Leishmaniasis. Obtenido de https://www.who.int/news-room/fact-sheets/detail/leishmaniasis, 2020.; GERENCIA DE OPERACIÓN BANCARIA Y APOYO TRANSACCIONAL, Guía Operativa de Gestión Efectivo Banco Agrario de Colombia, 2022.; GUTIÉRREZ, H. , Cartas de control Bayesianas para atributos y eltamaño de subgrupo grande en la carta p , Revista Colombiana de Estadística, 2006.; OFICINA DE AUDITORÍA INTERNA - BAC, Reporte de monitoreo de gestión del efectivo cupo asignado a cada oficina Banco Agrario de Colombia, 2021.; VARGAS, J.A., Control Estadístico de Calidad 2da ed., 2017.; DAVIS, C.,Statistical Methods for the Analysis of Repeated Measurements, Springer, 2002; DIGGLE, P., HEAGERTY, P., LIANG ,K. AND ZEGER,S.,Analysis of Longitudinal Data,Oxford University Press, 2002.; JIMÉNEZ-RODRÍGUEZ, J.,El Efecto Mateo: Un Concepto Psicológico, Papeles del Psicólogo 30(2), 145-154, 2009.; MERTON, R.,The Matthew Effect in Science, Science 159, 56-63, 1968.; Alcaide, M. (2015). Modelo de regresión binomial negativa. Universidad de Sevilla. Facultad de Matemáticas; Anselin, L. (2001). Spatial econometrics. A companion to theoretical econometrics, ed. Baltagi, Oxford: Basil Blackwell; pp. 310-330; Chasco, C. (2004). Modelos de heterogeneidad espacial. Universidad Autónoma de Madrid.; Espin, J., González, M. (2020).Acerca de la covid-19 como un importante problema para la salud pública y la humanidad. En el I Congreso Virtual de Ciencias Básicas Biomédicas de Granma; Ruiz, M. (2020). Mapeo de enfermedades con enfoques bayesianos y evaluación del riesgo relativo para la salud pública en Colombia. Universidad Distrital Francisco José de Caldas. Bogotá, Colombia; AKPINAR, M., Y NEJAT, Y, Year ahead demand forecast of city natural gas using seasonal Time Series Methods, Energies, 9(9), 727, 2016.; HWANG, S., Time Series Models for Forecasting Construction Costs Using Time Series Indexes., J. Constr. Eng. Manage. 137: 656-662, 2011.; BREIMAN, L., FRIEDMAN, J. H., OLSHEN, R. A. AND STONE, C. J., Classification and Regression Trees, Chapman and Hall/CRC, 1984.; DÍAZ, L. G. Y MORALES, M. A., Análisis Estadístico de Datos Categóricos, Universidad Nacional de Colombia, 2018; HASTIE, T., TIBSHIRANI, R. AND FRIEDMAN, J., The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer, 2001; HOSMER JR., D. W. AND LEMESHOW, S., Applied Logistic Regression, 2nd Edition, John Wiley & Sons, 2000.; JAMES, G., WITTEN, D. HASTIE, T. AND TIBSHIRANI, R., An Introduction to Statistical Learning With Applications in R, Springer, 2013; RAMSAY, J. O., SILVERMAN, B. W. , Functional Data analysis., Springer, 1997.; JAMES O. RAMSAY, GILES HOOKER SPENCER GRAVES, Functional Data Analysis with R and MATLAB (Use R!)., Springer, 2009.; WANG, J., CHIOU, J., MÜLLER, H., Functional Data analysis. Annual Review of Statistics and Its Application, 3(1), 257–295. https://doi.org/10.1146/annurev-statistics-041715- 033624, 2016.; NU. CEPAL. CELADE., Observatorio Demográfico de América Latina y el Caribe 2022. Tendencias de la población de América Latina y el Caribe: efectos demográficos de la pandemia de COVID-19. https://repositorio.cepal.org/entities/publication/9433bf26-70ac- 46c8-be5d-b7e4dda0b192,(2022, November 17); Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavior change. Psychological Review, 84, 191-215.; Cocorada, S. (2014). Computer anxiety, computer self-efficacy and demographic variables. eLearning & Software for Education, 2014, 4, 407-414.; Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.; Davison, A. C. (1997). Bootstrap Methods and Their Application (Cambridge Series in Statistical and Probabilistic Mathematics) (1st ed.) USA: Cambridge University Press.; Dahlstrom, E. (2012). ECAR Study of undergraduate students and information technology. EDUCAUSE Center for Applied Research (Research Report), 1-38.; Downey, J.P., & Kher, H.V. (2015). A longitudinal examination of the effects of computer self-efficacy growth on performance during technology training. Journal of Information Technology Education: Research, 14, 91-111.; Duncan-Howell, J. (2012). Digital mismatch: Expectations and realities of digital competency amongst pre-service education students. Australasian Journal of Educational Technology, 28(5). https://doi.org/10.14742/ajet.819; Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.; Gana, K., & Broc, G. (2019). Structural equation modeling with lavaan. John Wiley & Sons.; Güdel, K., Heitzmann, A., & Müller, A. (2018). Self-efficacy and (vocational) interest in technology and design: an empirical study in seventh and eighth-grade classrooms. International Journal of Technology and Design Education, 29, 1053-1081. https://doi.org/ 10.1007/s10798-018-9475-y; Güdel, K., Heitzmann, A., & Mueller, A. (2015). Affinity for technology—A potential instrument to measure PATT (Pupils’ Attitude Towards Technology)? In M. Chatoney (Ed.), Plurality and complementarity of approaches in design and technology education: PATT29 conference proceedings (pp. 180-187). Reston, VA: ITEEA.; Granados-López, H., Gallego, F.A., Rojas, S. C., & Sánchez, O. J. (2021). Creencias de autoeficacia y dominio y su influencia en la mediación TIC: Un estudio empírico en aulas de ingeniería. Revista Tesis Psicológica, 16(1), 1-17. https://doi.org/10.37511/tesis. v16n1a8; Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). United States of America: SAGE Publications, Inc.; Pintrich, P.R., Smith, D.A., García, T., & Mckeachie, W.J. (1991). A manual for the use of the motivational strategies for learning questionnaire (MSLQ). Ann Arbor, MI: NCRIPTAL, The University of Michigan.; Pintrich, P.R., Smith, D.A. F., García, T., & Mckeachie, W.J. (1993). Reliability and predictive validity of the motivational strategies for learning questionnaire. Educational and Psychological Measurement, 53, 801-813.; Renes, S.L., & Strange, A.T. (2011). Using technology to enhance higher education. Innovative Higher Education, 36, 203-213.; Sanchez, G. (2013). PLS Path Modeling with R. Retrieved from http://www. gastonsanchez.com/PLS_Path_Modeling_with_R.pdf; CARBAJAL-MORÁN, H., Fotocatálisis Solar en la Recuperación de Aguas Grises para la Agricultura: Innovación y Eficiencia por Control de Procesos con PLC S7-1500, Instituto Universitario de Innovación, Ciencia y Tecnología de la Universidad Nacional de Huancavelica, Perú, 2023.; CRIBARI-NETO, F. AND VASCONCELLOS, K., Nearly Unbiased Maximun Likelihood Estimation for the Beta Distribution, Journal of Statistical Computation and Simulation, Vol.72 (107-118), 2002.; FERRARI, S. AND CRIBARI-NETO, F., Beta Regression for Modelling Rates and Proportions, Journal of Applied Statistics, Vol.31 (799-815), 2004.; MYERS, R. AND MONTGOMERY, D., Response Surface Methodology, Process and Product Optimization Using Designed Experiments, John Wiley & Sons, U.S.A., 1995.; Alpaydin E. (2004): Introduction to Machine Learning. MIT Press.; Chen, A., Jesus, R.,and Vilarigues, M. (2023): Identification and Visualization of Pure and Mixed Paint Pigments in Heritage Artwork Using Machine Learning Algorithms. SN COMPUT. SCI. 4, 115. https://doi.org/10.1007/s42979-022-01529-8; McNemar, I. (1947): Note on the Sampling Error of the Difference between Correlated Proportions or Percentages". Psycometrika 12: 153-157.; Montgomery D.C. (2020): Design and Analysis of experiments. 10th ED. Wiley. UNAL; Ahouz, F., & Golabpour, A. Predicting the incidence of COVID-19 using data mining. BMC Public Health, 21, 1087, 2021. https://doi.org/10.1186/s12889-021-11058-3; Arias-Barahona, M.X., & Vanegas-Giraldo, J. J. Comparación de la distribución de probabilidad en pacientes fallecidos por COVID-19 en tres picos de la pandemia en Colombia. Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales, 45(177), 971-979, 2021. https://doi.org/10.18257/raccefyn.1474; Cerquera Cordoba, A.M., Moreno Salgado, A.K., & Lizarazo Jacome, R.A. Bienestar humano: Trascender el sintoma desde el humanismo y el psicoanalisis. Revista Virtual Universidad Católica Del Norte, (66), 243–264, 2022, https://doi.org/https://doi.org/10.35575/rvucn.n66a10; MURRAY, J. D. , Mathematical Biology, Vol. 1 y 2, 2nd ed., Springer, 2002.; OKUBO, A., & LEVIN, S. A., Diffusion and Ecological Problems, Modern Perspectives. Interdisciplinary and Applied Mathematics, Springer, 2001.; Chaves Castro, Álvaro H. Análisis sobre la evolución del COVID-19 en Colombia: ¿se alcanzará el pico de contagio?. Tiempo Y economía, 8(1), 123–160, 2021. https://doi.org/https://doi.org/10.21789/24222704.1672; Oppenheim, A.V., & Schafer, R.W.Digital Signal Processing (1st Ed.). New Jersey,NY: Prentice Hall, inc. 1975.; Gao, C. et al. Association of hypertension and antihypertensive treatment with COVID-19 mortality: a retrospective observational study. European heart journal, 41(22), 2058–2066, 2020. https://doi.org/10.1093/eurheartj/ehaa433; Guan, W.-J. et al. Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis. The European respiratory journal, 55(5), 2000547. 14 May. 2020. https://doi.org/10.1183/13993003.00547-2020; Holt, C.C. Forecasting Trends and Seasonal by Exponentially Weighted Averages. International Journal of Forecasting, 20(1), 5-10, 1957. https://doi.org/10.1016/j.ijforecast.2003.09.015; Jin, W., Dong, S., Yu, C., & Luo, Q. A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning. Computers in biology and medicine, 146, 105560, 2022. https://doi.org/10.1016/j.compbiomed.2022.105560; Malagón-Rojas, J. et al. Analysis of COVID-19 Mortality and Survival in Colombia: A prospective Cohort Study. Infectio, 25(3), 176-181, 2021. https://doi.org/10.22354/in.v25i3.943; Malik, J. et al. The Impact of COVID-19 On Comorbidities: A Review Of Recent Updates For Combating It. Saudi Journal of Biological Sciences, 29(5) 3586-3599, 2022. https://doi.org/10.1016/j.sjbs.2022.02.006; Marín-Sánchez, A. Características clínicas básicas en los primeros 100 casos fatales de COVID-19 en Colombia. Revista Panamericana de Salud Publica, 44(e87), 2020. https://doi.org/10.26633/RPSP.2020.87; Martinez, R., Pons, C., Rodriguez, R., & Vera, P. Quality Study of open government data related to COVID-19 in Latin America. Revista Facultad De Ingeniería Universidad De Antioquia, (108), 18-32. Epub September 19, 2023. https://doi.org/10.17533/udea.redin.20220579; Martínez, J.C. Factores asociados a la mortalidad por enfermedades no transmisibles en Colombia, 2008-2012. Biomédica, 36(4), 535-546, 2016.; Ministerio de Salud y Protección Social. Colombia confirma primera muerte por coronavirus. 2020. Recuperado el 24 de julio de 2021, de https://www.minsalud.gov.co/Paginas/Colombia-confirmaprimera- muerte-por-coronavirus.aspx; Ministerio de Salud y Protección Social. RUAF-NDv2. Recuperado el 14 de agosto de 2024, de https://nd.ruaf.gov.co/; Osorio-Sanabria, M., Amaya-Fernández, F., & González-Zabala, M. Políticas, normas y estrategias que fomentan los datos abiertos en Colombia: un análisis de literatura. Revista Virtual Universidad Católica Del Norte, (62), 155-188, 2020. https://doi.org/10.35575/rvucn.n62a7; Saavedra Trujillo, C. Consenso colombiano de atención, diagnóstico y manejo de la infección por SARS-COV-2/COVID-19 en establecimientos de atención de la salud. Recomendaciones basadas en consenso de expertos e informadas en la evidencia. Infectio, 24(3), 186-261,2020. https://doi.org/10.22354/in.v24i3.895; Shah, H. et al. The triumvirate: why hypertension, obesity, and diabetes are risk factors for adverse effects in patients with COVID-19. Acta Diabetologica, 58, 831–843, 2021. https://doi.org/10.1007/s00592-020-01636-z; Winters, P.R. Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6(3), 324-342, 1960. https://doi.org/10.1287/mnsc.6.3.324; Yang, Jing et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. International Journal of Infectious Diseases 94, 91-95, 2020. https://doi.org/10.1016/j.ijid.2020.03.017; Zurique Sánchez, M.S. et al. Prevalencia de hipertensión arterial en Colombia: Revisión sistemática y metaanálisis. Acta Médica Colombiana, 44(4), 2019. https://doi.org/10.36104/amc.2019.1293; Sheskin, D.J. Handbook of Parametric and Non-parametric Statistical Procedures. Chapman & Hall/CRC, Washington D.C, 2000. Edición 2.; Kim, S. ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients. Communications for Statistical Applications and Methods, 22(6), 665-674, 2015. https://doi.org/10.5351/CSAM.2015.22.6.665; Peck, E., Vining, G., & Montgomery, D. Introduction to Linear Regression Analysis. Wiley Series in Probability and Statistics, 2012. Capítulo 10.; Kendall, M.G. A New Measure of Rank Correlation. Biometrika, 30(1-2), 81-93, 1938. https://doi.org/10.1093/biomet/30.1-2.81; Brophy, A.L. An Algorithm and Program for Calculation of Kendall’s Rank Correlation Coefficient. Behavior Research Methods, Instruments, & Computers, 18(1), 45-46, 1986.; Chatterjee, S. A New Coefficient of Correlation. Arxiv- Cornell University, 2020. https://doi.org/10.48550/arXiv.1909.10140; Chatterjee, S., & Holmes, S. XICOR: Robust and Generalized Correlation Coefficients. CRAN, 2023. https://CRAN.R-project.org/package=XICOR.; Blanco Castaneda, L. Probabilidad. Facultad de Ciencias, Universidad Nacional de Colombia, 2004. Edición 1. ISBN 958-701-449-9.; Azadkia, M., & Chatterjee, S. A Simple Measure of Conditional Dependence. The Annals of Statistics, 49(6), 3070-3102, 2021. https://doi.org/10.1214/21-AOS2073; Chatterjee, S. A Survey of Some Recent Developments in Measures of Association. ar- Xiv:2211.04702, 2023.; Klein, M. (2000). Two alternatives to the Shewhart X control chart. Journal of Quality technology, 32(4), 427-431.; Vera do Carmo, C., Lopes, L. F. D., & Souza, A. M. (2004). Comparative study of the performance of the CuSum and EWMA control charts. Computers & Industrial Engineering, 46(4), 707-724; Figueiredo, F., & Gomes, M. I. (2013). The skew-normal distribution in SPC. REVSTATStatistical Journal, 11(1), 83-104.; Abu-Shawiesh, M. O. A., Golam Kibria, B. M., & George, F. (2014). A robust bivariate control chart alternative to the Hotelling’s T2 control chart. Quality and Reliability Engineering International, 30(1), 25-35.; Ahmed Abu Shawiesh, M. O., George, F., & Kibria, B. M. (2014). A Comparison of Some Robust Bicariate Control Charts for Individual Observations. Moustafa et al. (2014) hacen un estudio de simulación para comparar el desempeño de varios esquemas, utilizados para monitorear observaciones individuales; Saghir, A., Khan, Y. A., & Chakraborti, S. (2016). The phase I dispersion charts for bivariate process monitoring. Quality and Reliability Engineering International, 32(5), 1807-1823.; Haddad, F., Alsmadi, M. K., Badawi, U., Farag, T., Alkhasawneh, R., Almarashdeh, I., & Hassan, W. (2019). Bivariate modified hotelling’s T2 charts using bootstrap data. International Journal of Electrical and Computer Engineering, 9(6), 4721; SUAREZ, D., MUÑOZ, A., PARRA, M., RODRÍGUEZ, N., PRIETO, E., Y MAESTRE, R., Comportamiento epidemiológico de la hepatitis A en Barranquilla-Colombia, durante los años 2013 a 2017. Revista de Salud Pública, 21(3), 287- 291, 2019. Doi: https://doi.org/10.15446/rsap.V21n3.74932; LEAL, P. R., GUIMARÃES, R. J. P. S., Y KAMPEL, M., Sociodemographic and spatiotemporal profiles of hepatitis-A in the state of Pará, Brazil, based on reported notified cases. Geospatial Health, 16(2), 2021. Doi: https://doi.org/10.4081/GH.2021.981; R CORE TEAM., R: A Language and Environment for Statistical Computing Geospatial R Foundation for Statistical Computing, Vienna, Austria, 2024 Doi: https://www.R-project.org/; ABDELSATTAR, M., ZHUANG, Y., CUI, K., BI, Y., ZHANG, N., Predicting the Digestive Tract Development and Growth Performance of Goat Kids Using Sigmoidal Models. Animals, 11(3), 757., 2021. https://doi.org/10.3390/ani11030757.; AGUDELO GÓMEZ, DIVIER A, CERÓN MUÑOZ, MARIO F, RESTREPO BETANCUR, LUIS F., Modelación de las funciones de crecimiento aplicadas a la producción animal. Revista Colombiana de Ciencias Pecuarias, 21(1), 39-58., 2008. http://www.scielo.org.co/scielo.php?script=sci_arttext&pid= S0120-06902008000100005&lng=en&tlng=; AYOUB, H. H., MUMTAZ, G. R., SEEDAT, S., MAKHOUL, M., CHEMAITELLY, H., ABU-RADDAD, L. J., Estimates of global SARS-CoV-2 infection exposure, infection morbidity, and infection mortality rates in 2020. Global Epidemiology, 3, 100068., 2021. https://doi.org/10.1016/j.gloepi.2021.100068; FAO, UNEP, WHO, AND WOAH, One Health Joint Plan of Action (2022-2026)Working together for the health of humans, animals, plants and the environment. Rome., 2022. https://doi.org/10.4060/cc2289en; HE, H., LIN, D., ZHANG, J., WANG, Y., DENG, H. W, Biostatistics, data mining and computational modeling. Application of Clinical Bioinformatics, 23-57., 2016.; LONERGAN, S. M., TOPEL, D. G., MARPLE, D. N, The science of animal growth and meat technology (Second edition). Elsevier/Academic Press., 2019.; LUVIZUTTO, G. J., SILVA, G. F., ET AL, Use of artificial intelligence as an instrument of evaluation after stroke: A scoping review based on international classification of functioning, disability and health concept: AI applications for stroke evaluation. Topics in Stroke Rehabilitation, 29(5), 331-346., 2022. https://doi.org/10.1080/10749357.2021.1926149; MARTÍNEZ OSORIO, P. A., DA CUZ LANDIM, P., FERREIRA BARATA, T, Imaginarios sociales y prácticas embrionarias de diseño en la obra de José Rodrigo de Vivero, en las Sabanas del Sur de Bolívar. HiSTOReLo. Revista de Historia Regional y Local, 11(22), 369-398., 2019. https://doi.org/10.15446/historelo.v11n22.70950; ORGANIZACIÓN MUNDIAL DE LA SALUD (OMS)., La OMS elaboró el Plan de acción mundial con el apoyo de la Organización para la Alimentación y la Agricultura (FAO) y la Organización Mundial de Sanidad Animal (OIE). 2015. http://www.who.int/antimicrobial-resistance/publications/ global-action-plan/en/; ORGANIZACIÓN MUNDIAL DE LA SALUD (OMS)., Plan de acción mundial sobre la resistencia a los antimicrobianos. Organización Mundial de la Salud. 2016. https://iris.who.int/handle/10665/255204; WINFRIED, W., PONTE, B.,STARK, F. Y NANNO, M., Brechas de género en las cadenas globales de valor de América Latina y el Caribe: nuevos y viejos retos en un escenario de incertidumbre, CEPAL, 2023. https://hdl.handle.net/11362/48789; VERTEL, M., Y MÉNDEZ, M., Estudios desagregados por sexo en el mercado laboral. Simposio Internacional de Estadística., Simposio Internacional de Estadistica, 2022.; RAMOS, C. I., & BOLIVAR , M. C., Brecha de género en el mercado laboral colombiano en tiempos de la Covid-19., Semestre Económico, 23(55), 285–312, 2020. https://doi. org/10.22395/seec.v23n55a13; R CORE TEAM., R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2024 Doi: https://www.R-project.org/; ORGANIZACIÓN DE NACIONES UNIDAS (ONU), Obtenido de La era de la energía limpia debe empezar hoy, coinciden líderes mundiales, 24 de Septiembre de 2021. URL: https: //news.un.org/es/story/2021/09/1497412; PAGE, M., MCKENZIE, J., BOSSUYT, P., BOUTRON, I., HOFFMANN, T., MULROW, C., SHAMSEER, L., TETZLAFF, J., AKL, E., BRENNAN, S., CHOU, R., GLANVILLE, J., GRIMSHAW J., HRÓBJARTSSON, A., LALU, M., LI, T., LODER, E., MAYO-WILSON, E., MCDONALD, S., MCGUINNESS, L., STEWARTR, L., THOMAS, J., TRICCO, A., WELCH, V., WHITING, P. Y FERNÁNDEZ, S., Declaración PRISMA 2020: una guía actualizada para la publicación de revisiones sistemáticas , Sociedad Española de Cardiología, 790- 799, 2021.DOI: https://doi.org/10.1016/j.recesp.2021.06.016; PROGRAMA DE LAS NACIONES UNIDAS PARA EL DESARROLLO (PNUD), Informe Regional de Desarrollo Humano 2021, Atrapados: Alta desigualdad y bajo crecimiento en América Latina y el Caribe Nueva York ,2021.URL: PNUDpresentaInformeRegionaldeDesarrolloHumano2021"Atrapados: altadesigualdadybajocrecimientoenAmÃľricaLatinayelCaribe"%7C NacionesUnidasenElSalvador; THE CLUB OF ROME, Obtenido de Limits and Beyond: 50 years on from The Limits to Growth, what did we learn and what’s next?, 2022.URL: https://www.clubofrome.org/ publication/limits-and-beyond/.; R CORE TEAM., R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing, Vienna, Austria, 2024 Doi: https://www.R-project.org/; BELTRÁN HERNÁNDEZ, A, ROMERO ÁLVAREZ, A, GUEVARA BLANQUICETT, A Y VERTEL MORINSON, M. Análisis sensorial, composicional y fisicoquímico de una comida tradicional costeña (yuca, queso criollo y jugo de naranja): Diseño experimental y componentes principales ponderado. Simposio Internacional de Estadística - Memorias. Barranquilla: Universidad del Norte, 2019. Disponible en: https://repositorio.unisucre.edu.co/handle/001/992; GÖKMEN, V. Y SÜGUT, I. A non-contact computer vision-based analysis of color in foods. International Journal of Food Engineering, vol. 3(5): p1-13. 2007.; LEÓN, K.; MERY, D.; PEDRESCHI, F. Y LEÓN J. Color measurement in L*a*b* units from RGB digital images. Journal Food Research International, vol. 39: p1084-1091. 2006.; LÓPEZ, A. Diseño y Caracterización del Hormigón Autocompactante Coloreado. Tesis doctoral. Universidad Nacional de La Plata. 189 p. 2012; MATHIAS-RETTIG, K., & AH-HEN, K. El color en los alimentos: un criterio de calidad medible. Agro Sur, 42(2), 57-66. 2014. https://doi.org/10.4206/agrosur.2014.v42n2-07; R CORE TEAM (2024).R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/; SAHIN, S. Y SUMNU, S. Advances in deep-fat frying of foods. Ed. CRC Press Taylor & Francis Group. singNew York, USA, p81-113. 2009.; VERTEL-MORINSON, M. L., & QUINTANA, F. O. (2022). Modelos matemáticos de diferencia de color CIELAB y el procesamiento digital de imágenes en la industria de alimentos: Yuca (Manihot esculenta Crantz). Journal of Engineering Research, 2(11), 2-7. 2022. https://doi.org/10.22533/at.ed.3172112224062; ACEVEDO, M., La evaluación en el aula de matemáticas. En Trazas y Miradas. 2003. Recuperado de: http://www.bdigital.unal.edu.co/1559/8/07CAPI06.pdf; ARTEAGA, P, BATANERO, C., CAÑADAS, G, CONTRERAS, J. Las tablas y gráficos estadísticos como objetos culturales. Números revista didáctica de las matemáticas, 76, 55–67. ISSN: 1887-1984. 2012.; BATANERO, C. GODINO, J. Perspectivas de la educación estadística como área de investigación En R. Luengo (Ed.), Líneas de investigación en Didáctica de las Matemáticas (pp. 203-226). Badajoz: Universidad de Extremadura. 2005; COURTOIS, G. V. Situación nutricional en escolares de Santiago el Pinar, Chiapas (México). Tesis de grado de Doctor en Ciencias en Ecología y Desarrollo Sustentable. 2014.; DEL PINO, G. ESTRELLA, S., Educación estadística: relaciones con la matemática. Departamento de Estadística, Pontificia Universidad Católica de Chile. 2012; DICLENY, C. El Informe Estadístico: Una estrategia de evaluación en Estadística. 2014; VERTEL ET AL. Grupo de Investigación Estadística y Modelamiento Matemático aplicado a calidad educativa, universidad de Sucre. 2021; CANAZAS, A. P., BLAZ, J. J. R., MARTÍNEZ, P. D. T., & MAMANI, X. J. , Sistema de identificación de emociones a través de reconocimiento facial utilizando inteligencia artificial, Innovación y Software, 3(2), Article 2., 2022. https://doi.org/10.48168/innosoft.s9.a74; EKMAN, P., El rostro de las emociones. En: M. A. Flórez Góngora (Ed.). Bogotá: Renovación Urbana, Renovación Humana (pp. 80-100). 2006; FERNÁNDEZ-ABASCAL, E. G., GARCÍA RODRÍGUEZ, B., JIMÉNEZ SÁNCHEZ, M. P., MARTÍN DÍAZ, M. D., & DOMÍNGUEZ SÁNCHEZ, F. J. , Psicología de la Emoción. Editorial Universitaria Ramón Areces, 2010; LARA-LEVANO, Sistema de reconocimiento de gestos faciales captados a traves de camaras para analizar el nivel de satisfaccion de clientes en restaurantes. Interfases, 12, 61-85. 2019. https://doi.org/10.26439/interfases2019.n012.4638; MOYA CAJAS, M. & BONILLA, V., Modelado y simulación del Robot Mitsubishi RV-2JA controlado mediante señales electromiográficas. Electromyographic signals, 2018. https://doi.org/10.29019/enfoqueute.v9n2; XIA, X., CHEN, X., WU, G. ET AL., Three-dimensional facial-image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle. Nat Metab 2, 946–957, 2020. https://doi.org/10.1038/s42255-020-00270-x; FERRATY, F., & VIEU, P., Nonparametric Functional Data Analysis: Theory and Practice, Springer, 2006.; HSING, T., & EUBANK, R., Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators,Wiley, 2015. Available at: https://doi.org/10.1002/9781118762547; KOKOSZKA, P., & REIMHERR, M., Introduction to Functional Data Analysis, CRC Press, 2017. Available at: https://doi.org/10.1201/9781315117416.; RAMSAY, J. O., & SILVERMAN, B. W., Functional Data Analysis, 2nd ed., Springer, 2005. Available at: https://doi.org/10.1007/b98888.; ARBOLEDA, J. C., Prácticas pedagógicas, motivación y pensamiento crítico. Revista Boletín Redipe, 12(8), 14–19., 2023. https://doi.org/10.36260/rbr.v12i8.1986; CARNEROS, P. (2018)., Aprendizaje significativo: dotando de significado a nuestros progresos. Psicología y Mente, Universitat de Barcelona., 2018.; CASTRO, E. J. A., MEZA, O. B. R., ROSALES, M. Á., Algunas actividades experimentales como estrategias didácticas para la formación de profesores de física. Dialnet., 2019. https://dialnet.unirioja.es/servlet/articulo?codigo=7979555; ESPINOSA-RÍOS, EA, GONZÁLEZ-LÓPEZ, KD, HERNÁNDEZ-RAMÍREZ, LT, Las prácticas de laboratorio: una estrategia didáctica en la construcción de conocimiento científico escolar.Universidad del Valle, Colombia., 2016. https://www.redalyc.org/journal/ 2654/265447025017/html/; IRIARTE PUPO, A. J., ORTEGA PÉREZ, Y. D., ESTRADA SOTO, L. P., Estrategias didácticas basadas en laboratorios virtuales y presenciales en el aprendizaje de las leyes de Newton. Assensus, 7(12), 94-112., 2022. https://doi.org/10.21897/assensus.2944; MORENO, J., Y MARTÍNEZ, N., Enseñanza de las leyes de Newton en grado décimo bajo la Metodología de Aprendizaje Activo. Revista de Educação em Ciências e Matemática v.13 (26). P.82-101., 2017; PATIÑO GARCÍA, S. J., GARZÓN, J., Impacto de un Recurso Educativo Digital en el Aprendizaje de las Leyes de Newton. 2023. https://doi.org/10.15332/24224529.8650; García-González, J. J., Sandoval-Romero, M. (2018).Esperanza de vida y calidad de vida relacionada con la salud en México. Instituto Nacional de Salud Pública, Cuernavaca; Gispert, R., Ruíz-Ramos, M., Barés, M. A., Viciana, F., Clot-Razquin, G. (2007). Diferencias en la esperanza de vida libre de discapacidad por sexo y comunidades autónomas en España.Revista Española de Salud Pública; Mafla Uzuay, N. E., Córdova Luna, B. F. (2022).Esperanza de vida libre de discapacidad y esperanza de vida en estado de buena salud percibida en Ecuador 2018. Trabajo de titulación, Universidad Central del Ecuador, Quito; Rana, P., Muhammad, K. Mobeen, U. R., & Mandouh, A. S. A. (2024). Asymmetric liquidity risk and currency returns before and during COVID-19 pandemic. International Review of Financial Analysis, 91, 102919. https://doi.org/10.1016/j.irfa.2023.102919; Sequeda, P. R. (2014). Finanzas Corporativas y Valoración de Empresas. Ediciones de la U.; Bolzico, J., & Prats, J. O. (2022). Bank credit moratorium programs in times of COVID-19 in Latin America and the Caribbean. Inter-American Development Bank. http://dx.doi.org/10.18235/0004192; McCann, F., McGeever, N., & Yao, F. (2023). SME viability in the COVID-19 recovery. Small Business Economics, 61(3), 1053-1074. https://doi.org/10.1007/s11187-022-00723- 5; Li, K. (2024). Liquidity ratios and corporate failures. Accounting and Finance, 64(1), 1111- 1134. https://doi.org/10.1111/acfi.13174; Anaya, H. O. (2018). Análisis Financiero Aplicado Bajo NIIF. 16th ed. Universidad Externado de Colombia.; Petriashvili, A. (2017). Cash Flow Ratios’ and Liquidity Ratios’ Analysis of Selected Listed Companies in Sri Lanka. In Springer Proceedings in Business and Economics (Vol. 18, pp. 205-2016). Springer. https://doi.org/10.1007/978-3-319-68762-9_22; Gao, C. et al. (2023). An overview of clustering methods with guidelines for application in mental health research. Psychiatry Research, 321, 1-28. https://doi.org/10.1016/j.psychres.2023.11526; BOLZICO, J. & PRATS, J. O., Bank credit moratorium programs in times of COVID-19 in Latin America and the Caribbean. Inter-American Development Bank, 2022. Disponible en: http://dx.doi.org/10.18235/0004192.; EWANCHUK, L. & FREI, C., Recent Regulation in Credit Risk Management: A Statistical Framework. Risks, 7(2), 40, 2019. Disponible en: https://doi.org/10.3390/ risks7020040.; HEALD, D. & HODGES, R., The accounting, budgeting and fiscal impact of COVID- 19 on the United Kingdom. Journal of Public Budgeting, Accounting & Financial Management, 32(5), 785–795, 2020. Disponible en: https://doi.org/10.1108/ JPBAFM-07-2020-0121.; HOURCADEA, J. C., DASGUPTA, D. & GHERSI, F., Accelerating the speed and scale of climate finance in the post-pandemic context. Climate Policy, 21(10), 1383-1397, 2021. Disponible en: https://doi.org/10.1080/14693062.2021.1977599.; COWLING, M., LIU, W. & CALABRESE, R., Has previous loan rejection scarred firms from applying for loans during COVID-19? Small Business Economics, 59(4), 1327-1350, 2022. Disponible en: https://doi.org/10.1007/s11187-021-00586-2.; BASEL COMMITTEE ON BANKING SUPERVISION, Guidance on accounting for expected credit losses. Bank for International Settlements, 2015. Disponible en: www.bis.org.; BRITO, R. P. & JÚDICE, P., Efficient credit portfolios under IFRS 9. International Transactions in Operational Research, 30, 2453-2484, 2023. Disponible en: https://doi.org/ 10.1111/itor.13137.; SALAZAR, Y., MELELLO, P. & ZORIO, A. G., IFRS 9, banking risk and COVID-19: Evidence from Europe. Finance Research Letters, 50, 104130, 2023. Disponible en: https: //doi.org/10.1016/j.frl.2023.104130.; BRADBURY, M. & HOWIESO, B., Editorial: Evidence on APRA Proposals and Impact of COVID-19 on Expected Credit Loss Accounting. Australian Accounting Review, 30(94), 157-158, 2020. Disponible en: https://doi.org/10.1111/auar.12323.; GORDY, M. B., A Comparative Anatomy of Credit Risk Models. Journal of Banking & Finance, 24(1-2), 119-149, 2000. Disponible en: https://doi.org/10.1016/ S0378-4266(99)00054-0.; CHRISTODOULAKIS, G., BATIZ-ZUK, E. & POON, S., Systemic Basel II credit loss distributions under non-normality. DG ECFIN 6th Annual Research Conference: Crisis and Reform, 2009. Disponible en: https://ec.europa.eu/economy_finance/events/ event13393_en.htm.; VASICEK, O. A., The distribution of loan portfolio value. RISK, 15(3), 160–162, 2002. UNAL 509; PLUCHINO, BIONDO, RAPISARDA, TALENT VERSUS LUCK: THE ROLE OF RANDOMNESS IN SUCCESS AND FAILURE, Vol. 21 Nos. 3 y 4, 1850014, Advances in Complex Systems, 2018; TALEB, N. N., FOOLED BY RANDOMNESS, 2005 Incerto Series, Random House, 2005; BUBENIK, P. Y DŁOTKO, P. , A persistence landscapes toolbox for topological statistics, Journal of Symbolic Computation, 78, 91–114, 2017.; BUBENIK, P., Statistical Topological Data Analysis using Persistence Landscapes. Journal of Machine Learning Research 16, 77-102, 2015; E.Z. Chen and H. Li. A two part mixed effects model for analyzing longitudinal microbiome compositional data. Bioinformatics, 19:2611–2617, 2016.; S. Melo and O. Melo. Distance-based approach in univariate longitudinal data analysis. Journal of applied Statistics, 40:674–692, 2013; Y. Min and A. Agresti. Random effect models for repeated measures of zero-inflated count data. Stat. Modell, 19:1–19, 2005.; G. Molenberghs and G. Verbeke. Models for discrete longitudinal data, Springer, New York, 2005.; https://repositorio.unal.edu.co/handle/unal/87651; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; repositorio.unal.edu.co

Dostupnost: https://repositorio.unal.edu.co/handle/unal/87651

Artificial intelligence algorithms versus conventional radiological interpretation in breast cancer screening and classification: Systematic review and meta-analysis ; Algoritmos de inteligencia artificial versus interpretación radiológica convencional en tamizaje y clasificación del cáncer de mama: revisión sistemática con metaanálisis

Autoři: Alzate-Granados, Juan Pablo Sotomayor-Ricardo, Maria José Avella-Espinosa, María Kamila

Zdroj: Revista de la Facultad de Medicina; Vol. 73 (2025); e114297 ; Revista de la Facultad de Medicina; v. 73 (2025); e114297 ; 2357-3848 ; 0120-0011

Témata: Neoplasias de la Mama, Inteligencia Artificial, Diagnóstico por Computador, Tamizaje, Revisión Sistemática, Metaanálisis, Breast Neoplasms, Primary Health Care, Diagnosis, Computer-Assisted, Screening, Systematic Review, Meta-analysis

Popis souboru: application/pdf; text/html

Relation: https://revistas.unal.edu.co/index.php/revfacmed/article/view/114297/95946; https://revistas.unal.edu.co/index.php/revfacmed/article/view/114297/95947; https://revistas.unal.edu.co/index.php/revfacmed/article/view/114297

Dostupnost: https://revistas.unal.edu.co/index.php/revfacmed/article/view/114297

Pattern representations for classifying (non-)metric (non-)vectorial data with applications in Structural Health Monitoring and geotechnical/natural-hazard engineering ; Representaciones de patrones para la clasificación de datos (no-)vectoriales (no-)métricos con aplicaciones en el Monitoreo de Salud Estructural y la ingeniería de amenazas geotécnicas/naturales

Autoři: Ospina Dávila, Yesid Mauricio Orozco-Alzate, Mauricio

Přispěvatelé: Ospina Dávila, Yesid Mauricio Orozco-Alzate, Mauricio

Témata: 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores, Classifier system design, Data-driven surrogates, Dissimilarity pattern recognition, Landslides, Pattern representation, Structural health monitoring, Slope stability, Deslizamientos, Diseño de sistemas de clasificación, Estabilidad de taludes, Monitoreo de salud estructural, Reconocimiento de patrones basado en disimilitudes, Representación de patrones, Sustitutos basados en datos, Ingeniería de la construcción, Construction engineering

Popis souboru: xiv, 85 páginas; application/pdf

Relation: Jamal A. Abdalla, Mousa F. Attom, and Rami Hawileh. Prediction of minimum factor of safety against slope failure in clayey soils using artificial neural network. Environmental Earth Sciences, 73(9):5463–5477, May 2015.; Osama Abdeljaber, Onur Avci, Mustafa Serkan Kiranyaz, Boualem Boashash, Henry Sodano, and Daniel J. Inman. 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data. Neurocomputing, 275:1308–1317, 2018.; Osama Abdeljaber, Onur Avci, Serkan Kiranyaz, Moncef Gabbouj, and Daniel J. Inman. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of Sound and Vibration, 388:154–170, 2017.; Yacine Achour and Hamid Reza Pourghasemi. How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geoscience Frontiers, 11(3):871–883, 2020.; Ethem Alpaydin. Introduction to Machine Learning. MIT Press, 3rd edition, 2014.; Vinicius Alves, Alexandre Cury, Ney Roitman, Carlos Magluta, and Christian Cremona. Structural modification assessment using supervised learning methods applied to vibration data. Engineering Structures, 99:439 – 448, 2015.; Onur Avci, Osama Abdeljaber, Serkan Kiranyaz, Mohammed Hussein, Moncef Gabbouj, and Daniel J. Inman. A review of vibration–based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications. Mechanical Systems and Signal Processing; Yuequan Bao and Hui Li. Machine learning paradigm for structural health monitoring. Structural Health Monitoring, 20(4):1353–1372, 2021.; Alejandro Barredo Arrieta, Natalia Dı́az-Rodrı́guez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco Herrera. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58:82–115, 2020.; Robert M. Bell and Yehuda Koren. Lessons from the Netflix Prize Challenge. SIGKDD Explorations Newsletter, 9(2):75–79, dec 2007.; Manuele Bicego and Mauricio Orozco-Alzate. PowerHC: non linear normalization of distances for advanced nearest neighbor classification. In 25th International Conference on Pattern Recognition (ICPR), pages 1205–1211, 2021.; Michael Biehl, Barbara Hammer, and Thomas Villmann. Prototype-based models in machine learning. WIREs Cognitive Science, 7(2):92–111, 2016.; Alain Biem. A model selection criterion for classification: application to HMM topology optimization. In Proceedings of the Seventh International Conference on Document Analysis and Recognition - ICDAR 2003, pages 104–108, August 2003.; Christopher Bishop. Pattern Recognition and Machine Learning. Springer-Verlag, NY, 1st edition, 2006.; Luke Bornn, Charles R. Farrar, David Higdon, and Kevin P. Murphy. Modeling and diagnosis of structural systems through sparse dynamic graphical models. Mechanical Systems and Signal Processing, 74:133–143, 2016. Special Issue in Honor of Professor Simon Braun.; Steven L. Brunton and J. Nathan Kutz. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2019.; Lawrence A. Bull, Paul Gardner, Timothy J. Rogers, Elizabeth J. Cross, Nikolaos Dervilis, and Keith Worden. Probabilistic inference for structural health monitoring: New modes of learning from data. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7(1):03120003, 2021.; Raghavendra Chalapathy and Nguyen Lu Dang Khoa. Comparison of unsupervised shallow and deep models for structural health monitoring. Proceedings of the Institution of Civil Engineers - Bridge Engineering, pages 1–11, 2021.; Wei Chen, Hamid Reza Pourghasemi, Aiding Kornejady, and Ning Zhang. Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma, 305:314–327, 2017.; Zuoyi Chen, Yanzhi Wang, Jun Wu, Chao Deng, and Kui Hu. Sensor data-driven structural damage detection based on deep convolutional neural networks and continuous wavelet transform. Applied Intelligence, 51(8):5598–5609, 2021.; Min-Yuan Cheng and Nhat-Duc Hoang. Typhoon–induced slope collapse assessment using a novel bee colony optimized support vector classifier. Natural Hazards, 78(3):1961–1978, September 2015.; Vladimir Cherkassky and Filip M. Mulier. Learning from Data: Concepts, Theory, and Methods. Wiley-IEEE Press, 2007.; Jen-Tzung Chien and Chia-Chen Wu. Discriminant waveletfaces and nearest feature classifiers for face recognition. IEEE Trans. Pattern Anal. Machine Intell., 24(12):1644–1649, 2002.; Jianye Ching and Kok-Kwang Phoon. Characterizing Unknown Trend Using Sparse Bayesian Learning, chapter Geo-Risk 2017: Geotechnical Risk Assessment and Management, pages 22–31. 2017.; Hung V. Dang, Mohsin Raza, Tung V. Nguyen, T. Bui-Tien, and Huan X. Nguyen. Deep learning-based detection of structural damage using time-series data. Structure and Infrastructure Engineering, 17(11):1474–1493, 2021.; R. De Almeida Cardoso, Alexandre Cury, and Flavio Barbosa. Automated real–time damage detection strategy using raw dynamic measurements. Engineering Structures, 196:109364, 2019.; Hao Du and Yan Qiu Chen. Rectified nearest feature line segment for pattern classification. Pattern Recognition, 40(5):1486 – 1497, 2007.; Robert P. W. Duin. The origin of patterns. Frontiers in Computer Science, 3, 2021.; Robert P. W. Duin, Manuele Bicego, Mauricio Orozco-Alzate, Sang-Woon Kim, and Marco Loog. Metric learning in dissimilarity space for improved nearest neighbor performance. In Pasi Fränti, Gavin Brown, Marco Loog, Francisco Escolano, and Marcello Pelillo, editors, Structural, Syntactic, and Statistical Pattern Recognition, pages 183–192, Berlin, Heidelberg, 2014. Springer Berlin Heidelberg.; Robert P. W. Duin and E. Pȩkalska. The dissimilarity space: Bridging structural and statistical pattern recognition. Pattern Recognition Letters, 33(7):826–832, 2012. Special Issue on Awards from ICPR 2010.; Robert P. W. Duin, E. Pȩkalska, and Marco Loog. Non-Euclidean Dissimilarities: Causes, Embedding and Informativeness, pages 13–44. Springer London, London, 2013.; R.P.W. Duin and E. Pȩkalska. The dissimilarity representation for structural pattern recognition. In C. San-Martin and Sang-Woon Kim, editors, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, volume 7042 of Lecture Notes in Computer Science, pages 1–24. Springer Berlin Heidelberg, 2011.; Alireza Entezami, Hashem Shariatmadar, and Abbas Karamodin. Data–driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods. Structural Health Monitoring, 18(5-6):1416–1443, 2019.; Alireza Entezami, Hashem Shariatmadar, and Stefano Mariani. Early damage assessment in large-scale structures by innovative statistical pattern recognition methods based on time series modeling and novelty detection. Advances in Engineering Software, 150:102923, 2020.; Zhice Fang, Yi Wang, Ling Peng, and Haoyuan Hong. Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Computers & Geosciences, 139:104470, 2020.; Charles R. Farrar and Keith Worden. Structural Health Monitoring: A Machine Learning Perspective. John Wiley & Sons, West Sussex, UK, 2013.; Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, and Dinani Amorim. Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research, 15(90):3133–3181, 2014.; P. A. Gardner, L. A. Bull, N. Dervilis, and K. Worden. On the Application of Heterogeneous Transfer Learning to Population-Based Structural Health Monitoring. In Ramin Madarshahian and Francois Hemez, editors, Data Science in Engineering, Volume 9, pages 87–98, Cham, 2022. Springer International Publishing.; Lev Goldfarb. A unified approach to pattern recognition. Pattern Recognition, 17(5):575–582, 1984.; Ralf Herbrich. Learning Kernel Classifiers. Theory and Algorithms. MIT Press, 2001.; Nhat-Duc Hoang and Dieu Tien Bui. Chapter 18 - Slope Stability Evaluation Using Radial Basis Function Neural Network, Least Squares Support Vector Machines, and Extreme Learning Machine. In Pijush Samui, Sanjiban Sekhar, and Valentina E. Balas, editors, Handbook of Neural Computation, pages 333–344. Academic Press, 2017.; Nhat-Duc Hoang and Anh-Duc Pham. Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment: A multinational data analysis. Expert Systems with Applications, 46:60–68, 2016.; Yu Huang and Lu Zhao. Review on landslide susceptibility mapping using support vector machines. CATENA, 165:520–529, 2018.; Piotr Juszczak, David M.J. Tax, E. Pȩkalska, and R. P. W. Duin. Minimum spanning tree based one-class classifier. Neurocomputing, 72(7):1859–1869, 2009. Advances in Machine Learning and Computational Intelligence.; George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang. Physics-informed machine learning. Nature Reviews Physics, 3(6):422–440, 2021.; Eamonn Keogh, Stefano Lonardi, and Chotirat Ann Ratanamahatana. Towards parameter-free data mining. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’04, pages 206–215, New York, NY, USA, 2004. Association for Computing Machinery.; A.J. Li, S. Khoo, A.V. Lyamin, and Y. Wang. Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm. Automation in Construction, 65:42–50, 2016.; Bo Li, Duanyou Li, Zhijun Zhang, Shengmei Yang, and Fan Wang. Slope stability analysis based on quantum–behaved particle swarm optimization and least squares support vector machine. Applied Mathematical Modelling, 39(17):5253–5264, 2015.; Dian-Qing Li, Dong Zheng, Zi-Jun Cao, Xiao-Song Tang, and Kok-Kwang Phoon. Response surface methods for slope reliability analysis: Review and comparison. Engineering Geology, 203:3–14, 2016. Special Issue on Probabilistic and Soft Computing Methods for Engineering Geology.; Jiang Li and Can-Yi Lu. A new decision rule for sparse representation based classification for face recognition. Neurocomputing, 116:265–271, 2013. Advanced Theory and Methodology in Intelligent Computing.; Stan Z. Li and Juwei Lu. Face recognition using the nearest feature line method. IEEE Transactions on Neural Networks, 10(2):439–443, 1999.; Y. Lin, K. Zhou, and J. Li. Prediction of slope stability using four supervised learning methods. IEEE Access, 6:31169–31179, 6 2018.; Ramin Madarshahian and Francois Hemez, editors. Data Science in Engineering, Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics, volume 9 of Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS), Berlin, Germany, 2021. Springer.; Mehrisadat Makki Alamdari, Ali Anaissi, Nguyen L. D. Khoa, and Samir Mustapha. Frequency domain decomposition-based multisensor data fusion for assessment of progressive damage in structures. Structural Control and Health Monitoring, 26(2):e2299, 2019.; Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of Machine Learning. MIT Press, 2nd edition, 2018.; Maliki Moustapha, Stefano Marelli, and Bruno Sudret. Active learning for structural reliability: Survey, general framework and benchmark. Structural Safety, 96:102174, 2022.; Maximilian Münch, Christoph Raab, and Frank-Michael Schleif. Encoding of Indefinite Proximity Data: A Structure Preserving Perspective. In Maria De Marsico, Gabriella Sanniti di Baja, and Ana Fred, editors, Pattern Recognition Applications and Methods, pages 112–137, Cham, 2020. Springer International Publishing.; Maximilian Münch, Michiel Straat, Michael Biehl, and Frank-Michael Schleif. Complex-valued embeddings of generic proximity data. In Andrea Torsello, Luca Rossi, Marcello Pelillo, Battista Biggio, and Antonio Robles Kelly, editors, Structural, Syntactic, and Statistical Pattern Recognition, pages 14–23, Cham, 2021. Springer International Publishing.; W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu. Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 116(44):22071–22080, 2019.; Mauricio Orozco-Alzate, Sisto Baldo, and Manuele Bicego. Relation, transition and comparison between the adaptive nearest neighbor rule and the hypersphere classifier. In Elisa Ricci, Samuel Rota Bulò, Cees Snoek, Oswald Lanz, Stefano Messelodi, and Nicu Sebe, editors, Image Analysis and Processing – ICIAP 2019, pages 141–151, Cham, 2019. Springer International Publishing.; Mauricio Orozco-Alzate and Manuele Bicego. A cheaper rectified-nearest-feature-line-segment classifier based on safe points. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 2787–2794, 2021.; Mauricio Orozco-Alzate, Robert P. W. Duin, and César Germán Castellanos-Domı́nguez. A generalization of dissimilarity representations using feature lines and feature planes. Pattern Recognition Letters, 30(3):242–254, feb 2009.; E. Pȩkalska and R. P.W. Duin. The Dissimilarity Representation for Pattern Recognition: Foundations and Applications, volume 64 of Machine Perception and Artificial Intelligence. World Scientific, Singapore, 2005.; Elzbieta Pȩkalska, Pavel Paclik, and Robert P. W. Duin. A generalized kernel approach to dissimilarity-based classification. J. Mach. Learn. Res., 2:175–211, March 2002.; E. Pekalska and R. P. W. Duin. Beyond Traditional Kernels: Classification in Two Dissimilarity-Based Representation Spaces. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(6):729–744, Nov 2008.; Kok-Kwang Phoon, Jianye Ching, and Zijun Cao. Unpacking data-centric geotechnics. Underground Space, 2022.; Kok-Kwang Phoon, Jianye Ching, and Takayuki Shuku. Challenges in data-driven site characterization. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 0(0):1–13, 2021.; J. S.-Taylor and N. Cristianini. Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge, UK, 2004.; Adam Santos, Eloi Figueiredo, M.F.M. Silva, C.S. Sales, and J.C.W.A. Costa. Machine learning algorithms for damage detection: Kernel-based approaches. Journal of Sound and Vibration, 363:584 – 599, 2016.; Hassan Sarmadi and Abbas Karamodin. A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effects. Mechanical Systems and Signal Processing, 140:106495, 2020.; W. J. Scheirer, M. J. Wilber, M. Eckmann, and T. E. Boult. Good recognition is non–metric. Pattern Recognition, 47:2721–2731, 2014.; Frank-Michael Schleif and Peter Tino. Indefinite Proximity Learning: A Review. Neural Computation, 27(10):2039–2096, 10 2015.; Osvaldo Simeone. A Brief Introduction to Machine Learning for Engineers, volume 12. Now Foundations and Trends, 2018.; Hoon Sohn and Chang Kook Oh. Statistical Pattern Recognition, chapter 30. John Wiley & Sons, Ltd., 2009.; David M.J Tax and Robert P.W Duin. Support vector domain description. Pattern Recognition Letters, 20(11):1191–1199, 1999.; Sergios Theodoridis and Konstantinos Koutroumbas. Pattern Recognition. Academic Press, 2009.; Jigang Wang, Predrag Neskovic, and Leon N. Cooper. Improving nearest neighbor rule with a simple adaptive distance measure. Pattern Recognition Letters, 28(2):207 – 213, 2007.; Renjie Wu and Eamonn Keogh. Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress. IEEE Transactions on Knowledge and Data Engineering, pages 1–9, 2021.; JianXi Yang, Fei Yang, Likai Zhang, Ren Li, Shixin Jiang, Guiping Wang, Le Zhang, and Zeng Zeng. Bridge health anomaly detection using deep support vector data description. Neurocomputing, 2021.; Z. Zhou, S.Z. Li, and K.L. Chan. A theoretical justification of nearest feature line method. In Proc. 15th ICPR International Conference on Pattern Recognition, volume 2, pages 759–762, 2000.; Rania Rizki Arinta and Emanuel Andi W.R. Natural Disaster Application on Big Data and Machine Learning: A Review. In 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pages 249–254, 2019.; Luke Bornn, Charles R. Farrar, Gyuhae Park, and Kevin Farinholt. Structural Health Monitoring With Autoregressive Support Vector Machines. Journal of Vibration and Acoustics, 131(2), 02 2009.; Muhammad Shahzad Cheema, Abdalrahman Eweiwi, and Christian Bauckhage. High dimensional low sample size activity recognition using geometric classifiers. Digital Signal Processing, 42:61–69, 2015.; Wei Chen, Hamid Reza Pourghasemi, Mahdi Panahi, Aiding Kornejady, Jiale Wang, Xiaoshen Xie, and Shubo Cao. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology, 297:69–85, 2017.; A. J. Choobbasti, F. Farrokhzad, and A. Barari. Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran). Arabian Journal of Geosciences, 2(4):311–319, Nov 2009.; Paul Cilliers. Complexity and Postmodernism. Understanding Complex Systems. Routledge, 1st edition, 2002.; Alexandre Cury and Christian Crémona. Pattern recognition of structural behaviors based on learning algorithms and symbolic data concepts. Structural Control and Health Monitoring, 19(2):161–186, 2012.; Sarat Kumar Das, Rajani Kanta Biswal, N. Sivakugan, and Bitanjaya Das. Classification of slopes and prediction of factor of safety using differential evolution neural networks. Environmental Earth Sciences, 64(1):201–210, Sep 2011.; Rharã de Almeida Cardoso, Alexandre Cury, Flavio Barbosa, and Carmelo Gentile. Unsupervised real-time SHM technique based on novelty indexes. Structural Control and Health Monitoring, 26(7):e2364, 2019.; Robert P. W. Duin and Elżbieta Pekalska. The Science of Pattern Recognition. Achievements and Perspectives, chapter 10, pages 221–259. Springer Berlin Heidelberg, Berlin, Heidelberg, 2007.; Robert P.W. Duin, E. Pȩkalska, P. Paclı́k, and D.M.J. Tax. The dissimilarity representation, a basis for a domain-based pattern recognition? In J Goldfarb, editor, Proceedings of a satellite workshop of the 17th international conference on pattern recognition, pages 43–56. s.n., 2004.; R.P.W. Duin and E. Pȩkalska. Non–Euclidean dissimilarities: Causes and informativeness. In Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop, SSPR–SPR, Cesme, Izmir, Turkey, 2010.; C. R. Farrar and H. Sohn. Pattern recognition for structural health monitoring. In Workshop on Mitigation of Earthquake Disaster by Advanced Technologies, pages 1–6, Las Vegas, NV, USA, Nov–Dec 2000.; C. R. Farrar and K. Worden. An introduction to structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851):303–315, December 2007.; Charles Farrar, Mayuko Nishio, Francois Hemez, Chris Stull, Gyuhae Park, Phil Cornwell, Eloi Figueiredo, D. J. Luscher, and Keith Worden. Feature Extraction for Structural Dynamics Model Validation. Technical Report LA-UR-16-20151, Los Alamos National Laboratory, 2016.; Spilios D Fassois and John S Sakellariou. Time-series methods for fault detection and identification in vibrating structures. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851):411–448, 2007.; Qingxiang Feng, Lijun Yan, and Jeng-Shyang Pan. A novel classifier based on nearest feature line. In International Conference on Computing, Measurement, Control and Sensor Network (CMCSN), pages 265–268, July 2012.; M.D. Ferentinou and M.G. Sakellariou. Computational intelligence tools for the prediction of slope performance. Computers and Geotechnics, 34(5):362–384, 2007. Special Issue on Biologically Inspired and Other Novel Computing Techniques in Geomechanics.; Eloi Figueiredo, Joaquim Figueiras, Gyuhae Park, Charles R. Farrar, and Keith Worden. Influence of the Autoregressive Model Order on Damage Detection. Computer-Aided Civil and nfrastructure Engineering, 26(3):225–238, 2011.; Eloi Figueiredo, Gyuhae Park, Charles R Farrar, Keith Worden, and Joaquim Figueiras. Machine learning algorithms for damage detection under operational and environmental variability. Structural Health Monitoring, 10(6):559–572, 2011.; Eloi Figueiredo, Gyuhae Park, Joaquim Figueiras, Charles Farrar, and Keith Worden. Structural Health Monitoring Algorithm Comparisons Using Standard Data Sets. Technical Report LA-14393, Los Alamos National Laboratory, 2009.; Qing-Bin Gao and Zheng-Zhi Wang. Center–based nearest neighbor classifier. Pattern Recognition, 40(1):346–349, 2007.; Salvador Garcia, Joaquin Derrac, Jose Cano, and Francisco Herrera. Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3):417–435, 2012.; P. Gardner, L.A. Bull, J. Gosliga, J. Poole, N. Dervilis, and K. Worden. A population-based SHM methodology for heterogeneous structures: Transferring damage localisation knowledge between different aircraft wings. Mechanical Systems and Signal Processing, 172:108918, 2022.; Jason N. Goetz, Richard H. Guthrie, and Alexander Brenning. Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology, 129(3):376–386, 2011.; Behrouz Gordan, Danial Jahed Armaghani, Mohsen Hajihassani, and Masoud Monjezi. Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Engineering with Computers, 32(1):85–97, Jan 2016.; M. Gul and F. N. Catbas. Statistical pattern recognition for structural health monitoring using time series modeling: Theory and experimental verifications. Mechanical Systems and Signal Processing, 23(7):2192–2204, 2009.; B. Haasdonk and D. Keysers. Tangent distance kernels for support vector machines. In 2002 International Conference on Pattern Recognition, volume 2, pages 864–868 vol.2, 2002.; Ye Hua, Xianmin Wang, Yongwei Li, Peiyun Xu, and Wenxiang Xia. Dynamic development of landslide susceptibility based on slope unit and deep neural networks. Landslides, 18(1):281–302, 2021.; Faming Huang, Jing Zhang, Chuangbing Zhou, Yuhao Wang, Jinsong Huang, and Li Zhu. A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides, 17(1):217–229, 2020.; Yang H. Huang. Slope Stability Analysis by the Limit Equilibrium Method. American Society of Civil Engineers, 2014.; A. K. Jain, R. P. W. Duin, and Jianchang Mao. Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1):4–37, 2000.; Kejie Jiang, Qiang Han, Xiuli Du, and Pinghe Ni. A decentralized unsupervised structural condition diagnosis approach using deep auto-encoders. Computer-Aided Civil and Infrastructure Engineering, 36(6):711–732, 2021.; Navid Kardani, Annan Zhou, Majidreza Nazem, and Shui-Long Shen. Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data. Journal of Rock Mechanics and Geotechnical Engineering, 13(1):188–201, 2021.; Eamonn Keogh, Stefano Lonardi, Chotirat Ann Ratanamahatana, Li Wei, Sang-Hee Lee, and John Handley. Compression-based data mining of sequential data. Data Mining and Knowledge Discovery, 14:99–129, 2007.; Serkan Kiranyaz, Onur Avci, Osama Abdeljaber, Turker Ince, Moncef Gabbouj, and Daniel J. Inman. 1D convolutional neural networks and applications: A survey. Mechanical Systems and Signal Processing, 151:107398, 2021.; Mohammadreza Koopialipoor, Danial Jahed Armaghani, Ahmadreza Hedayat, Aminaton Marto, and Behrouz Gordan. Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Computing, May 2018.; Kundan Kumar, Prabir Kumar Biswas, and Nirjhar Dhang. Time series-based SHM using PCA with application to ASCE benchmark structure. Journal of Civil Structural Health Monitoring, 10(5):899–911, 2020.; Manoj Kumar, Pijush Samui, and Ajay Kumar Naithani. Determination of stability of epimetamorphic rock slope using Minimax Probability Machine. Geomatics, Natural Hazards and Risk, 7(1):186–193, 2016.; David J. Lary, Gebreab K. Zewdie, Xun Liu, Daji Wu, Estelle Levetin, Rebecca J. Allee, Nabin Malakar, Annette Walker, Hamse Mussa, Antonio Mannino, and Dirk Aurin. Machine Learning Applications for Earth Observation, pages 165–218. Springer International Publishing, Cham, 2018.; S.Z Li and J. Lu. Generalizing capacity of face database for face recognition. In Third IEEE International Conference on Automatic Face and Gesture Recognition, pages 402–406, Apr 1998.; Xueyou Li, Limin Zhang, and Shuai Zhang. Efficient bayesian networks for slope safety evaluation with large quantity monitoring information. Geoscience Frontiers, 9(6):1679–1687, 2018. Reliability Analysis of Geotechnical Infrastructures.; Zaobao Liu, Jianfu Shao, Weiya Xu, Hongjie Chen, and Yu Zhang. An extreme learning machine approach for slope stability evaluation and prediction. Natural Hazards, 73(2):787–804, Sep 2014.; Noel Lopes and Bernardete Ribeiro. Incremental Hypersphere Classifier (IHC). In Machine Learning for Adaptive Many-Core Machines - A Practical Approach, volume 7 of Studies in Big Data, chapter 6, pages 107–123. Springer International Publishing, Cham, 2015.; I. Lopez and N. Sarigul-Klijn. A novel dimensional reduction approach for structural damage diagnosis using feature similarity. In Tribikram Kundu, editor, Health Monitoring of Structural and Biological Systems 2009, volume 7295, pages 511 – 522. International Society for Optics and Photonics, SPIE, 2009.; Israel Lopez and Nesrin Sarigul-Klijn. Distance similarity matrix using ensemble of dimensional data reduction techniques: Vibration and aerocoustic case studies. Mechanical Systems and Signal Processing, 23(7):2287 – 2300, 2009.; Zhengjing Ma, Gang Mei, and Francesco Piccialli. Machine learning for landslides prevention: a survey. Neural Computing and Applications, 33(17):10881–10907, 2021.; Hossein Moayedi, Mansour Mosallanezhad, Ahmad Safuan A. Rashid, Wan Amizah Wan Jusoh, and Mohammed Abdullahi Muazu. A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications. Neural Computing and Applications, 32(2):495–518, 2020.; L. Moniz, J.M. Nichols, C.J. Nichols, M. Seaver, S.T. Trickey, M.D. Todd, L.M. Pecora, and L.N. Virgin. A multivariate, attractor-based approach to structural health monitoring. Journal of Sound and Vibration, 283(1):295–310, 2005.; J. M. Nichols, M. D. Todd, M. Seaver, and L. N. Virgin. Use of chaotic excitation and attractor property analysis in structural health monitoring. Phys. Rev. E, 67:016209, Jan 2003.; Mayuko Nishio, Francois Hemez, Keith Worden, Gyuhae Park, Nobuo Takeda, and Charles Farrar. Feature extraction for structural dynamics model validation. In Tom Proulx, editor, Linking Models and Experiments, Volume 2, pages 153–163, New York, NY, 2011. Springer New York.; L.A. Overbey and M.D. Todd. Analysis of Local State Space Models for Feature Extraction in Structural Health Monitoring. Structural Health Monitoring, 6(2):145–172, 2007.; E. Pȩkalska. Dissimilarity representations in pattern recognition. Concepts, theory and applications. PhD thesis, Delft University of Technology, 2005.; E. Pȩkalska and R. P.W. Duin. Dissimilarity measures, volume 64 of Machine Perception and Artificial Intelligence, chapter 5, pages 215–252. World Scientific, Singapore, 2005.; E. Pȩkalska and B. Haasdonk. Kernel discriminant analysis for positive definite and indefinite kernels. IEEE Transactions on Pattern Recognition and Machine Intelligence, 31(6):482–492, 2009.; Jin-Song Pei, Dean F. Hougen, Sai Teja Kanneganti, Joseph P. Wright, Eric C. Mai, Andrew W. Smyth, Sami F. Masri, Armen Derkevorkian, François Gay-Balmaz, and Ludian Komini. Interpretable Machine Learning for Function Approximation in Structural Health Monitoring, pages 369–388. Springer International Publishing, Cham, 2022.; Binh Thai Pham and Indra Prakash. A novel hybrid model of Bagging-based Naive Bayes Trees for landslide susceptibility assessment. Bulletin of Engineering Geology and the Environment, 78(3):1911–1925, 2021.; Binh Thai Pham, Indra Prakash, Sushant K. Singh, Ataollah Shirzadi, Himan Shahabi, Thi-Thu-Trang Tran, and Dieu Tien Bui. Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches. CATENA, 175:203–218, 2019.; Kok-Kwang Phoon and Wengang Zhang. Future of machine learning in geotechnics. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 0(0):1–16, 2022.; Luca Piciullo, Michele Calvello, and José Mauricio Cepeda. Territorial early warning systems for rainfall-induced landslides. Earth-Science Reviews, 179:228–247, 2018.; Daniele Posenato, Francesca Lanata, Daniele Inaudi, and Ian F.C. Smith. Model-free data interpretation for continuous monitoring of complex structures. Advanced Engineering Informatics, 22(1):135–144, 2008. Intelligent computing in engineering and architecture.; Miguel A. Prada, Janne Toivola, Jyrki Kullaa, and Jaakko Hollmén. Three-way analysis of structural health monitoring data. Neurocomputing, 80:119 – 128, 2012. Special Issue on Machine Learning for Signal Processing 2010.; Chongchong Qi and Xiaolin Tang. A hybrid ensemble method for improved prediction of slope stability. International Journal for Numerical and Analytical Methods in Geomechanics, 42(15):1823–1839, 2018.; Thanawin Rakthanmanon, Bilson Campana, Abdullah Mueen, Gustavo Batista, Brandon Westover, Qiang Zhu, Jesin Zakaria, and Eamonn Keogh. Addressing Big Data Time Series: Mining Trillions of Time Series Subsequences Under Dynamic Time Warping. ACM Transactions on Knowledge Discovery from Data, 7(3), September 2013.; Paola Reichenbach, Mauro Rossi, Bruce D. Malamud, Monika Mihir, and Fausto Guzzetti. A review of statistically-based landslide susceptibility models. Earth-Science Reviews, 180:60–91, 2018.; Hannah Ritchie and Max Roser. Natural disasters. Our World in Data, 2014. It was last revised in November 2021.; G. Roberti, J. McGregor, S. Lam, D. Bigelow, B. Boyko, C. Ahern, V. Wang, B. Barnhart, C. Smyth, D. Poole, and S. Richard. INSPIRE standards as a framework for artificial intelligence applications: a landslide example. Natural Hazards and Earth System Sciences, 20(12):3455–3483, 2020.; T.J. Rogers, K. Worden, R. Fuentes, N. Dervilis, U.T. Tygesen, and E.J. Cross. A Bayesian non-parametric clustering approach for semi-supervised structural health monitoring. Mechanical Systems and Signal Processing, 119:100–119, 2019.; S. Rukhaiyar, M. N. Alam, and N. K. Samadhiya. A PSO–ANN hybrid model for predicting factor of safety of slope. International Journal of Geotechnical Engineering, 12(6):556–566, 2018.; Seyedomid Sajedi and Xiao Liang. Dual Bayesian inference for risk-informed vibration-based damage diagnosis. Computer-Aided Civil and Infrastructure Engineering, 36(9):1168–1184, 2021.; M. G. Sakellariou and M. D. Ferentinou. A study of slope stability prediction using neural networks. Geotechnical & Geological Engineering, 23(4):419, Aug 2005.; P. Samui and D.P. Kothari. Utilization of a least square support vector machine (LSSVM) for slope stability analysis. Scientia Iranica, 18(1):53–58, 2011.; Pijush Samui. Slope stability analysis: a support vector machine approach. Environmental Geology, 56(2):255, Feb 2008.; Pijush Samui. Support vector classifier analysis of slope. Geomatics, Natural Hazards and Risk, 4(1):1–12, 2013.; João Pedro Santos, Christian Cremona, André D. Orcesi, and Paulo Silveira. Early Damage Detection Based on Pattern Recognition and Data Fusion. Journal of Structural Engineering, 143(2):04016162, 2017.; Hassan Sarmadi, Alireza Entezami, Behzad Saeedi Razavi, and Ka-Veng Yuen. Ensemble learning-based structural health monitoring by Mahalanobis distance metrics. Structural Control and Health Monitoring, 28(2):e2663, 2021.; Haichen Shi, Keith Worden, and Elizabeth J. Cross. A cointegration approach for heteroscedastic data based on a time series decomposition: An application to structural health monitoring. Mechanical Systems and Signal Processing, 120:16 – 31, 2019.; Moisés Silva, Adam Santos, and Elói Figueiredo. Damage Detection for Structural Health Monitoring of Bridges as a Knowledge Discovery in Databases Process, pages 1–24. Springer Singapore, Singapore, 2019.; Moisés Silva, Adam Santos, Eloi Figueiredo, Reginaldo Santos, Claudomiro Sales, and João C.W.A. Costa. A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges. Engineering Applications of Artificial Intelligence, 52:168 – 180, 2016.; H Sohn, C R Farrar, F M Hemez, and J J Czarnecki. A Review of Structural Health Monitoring Literature: 1996-2001. Technical Report LA-UR-02-2095, Los Alamos National Lab. (LANL), 1 2002.; Nikos A Spanos, John S Sakellariou, and Spilios D Fassois. Vibration-response-only statistical time series structural health monitoring methods: A comprehensive assessment via a scale jacket structure. Structural Health Monitoring, 19(3):736–750, 2020.; M.D. Spiridonakos and S.D. Fassois. Adaptable functional series TARMA models for non-stationary signal representation and their application to mechanical random vibration modeling. Signal Processing, 96:63 – 79, 2014.; Ingo Steinwart. Empirical Inference. Festschrift in Honor of Vladimir N. Vapnik, chapter Some Remarks on the Statistical Analysis of SVMs and Related Methods, pages 25–36. Springer-Verlag Berlin Heidelberg, 2013.; Han Sun, Henry V. Burton, and Honglan Huang. Machine learning applications for building structural design and performance assessment: State-of-the-art review. Journal of Building Engineering, 33:101816, 2021.; Zhiyi Tang, Zhicheng Chen, Yuequan Bao, and Hui Li. Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring. Structural Control and Health Monitoring, 26(1):e2296, 2019.; David Tax. One-class classification: Concept-learning in the absence of counter-examples. Doctoral thesis, Delft University of Technology, Netherlands, June 2001.; Dieu Tien Bui, Biswajeet Pradhan, Owe Lofman, Inge Revhaug, and Oystein B. Dick. Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): A comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. CATENA, 96:28–40, 2012.; Dieu Tien Bui, Biswajeet Pradhan, Haleh Nampak, Quang-Thanh Bui, Quynh-An Tran, and Quoc-Phi Nguyen. Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibility modeling in a high-frequency tropical cyclone area using GIS. Journal of Hydrology, 540:317–330, 2016.; Maria Valero, Fangyu Li, Liang Zhao, Chi Zhang, Jose Garrido, and Zhu Han. Vibration sensing-based human and infrastructure safety/health monitoring: A survey. Digital Signal Processing, 114:103037, 2021.; Chaofeng Wang, Qian Yu, Kincho H. Law, Frank McKenna, Stella X. Yu, Ertugrul Taciroglu, Adam Zsarnóczay, Wael Elhaddad, and Barbaros Cetiner. Machine learning-based regional scale intelligent modeling of building information for natural hazard risk management. Automation in Construction, 122:103474, 2021.; H.B. Wang, J.M. Li, B.Zhou, Y.Zhou, Z.Q. Yuan, , and Y.P. Chen. Application of a hybrid model of neural networks and genetic algorithms to evaluate landslide susceptibility. Geoenvironmental Disasters, 4(15):1–12, 2017.; Xiaoyue Wang, Abdullah Mueen, Hui Ding, Goce Trajcevski, Peter Scheuermann, and Eamonn Keogh. Experimental comparison of representation methods and distance measures for time series data. Data Mining and Knowledge Discovery, 26(2):275–309, 2013.; Keith Worden, Charles R. Farrar, Jonathan Haywood, and Michael Todd. A review of nonlinear dynamics applications to structural health monitoring. Structural Control and Health Monitoring, 15(4):540–567, 2008.; Keith Worden and Graeme Manson. The application of machine learning to structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851):515–537, 2007.; Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, , Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, and Dan Steinberg. Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1):1–37, 2008.; Beibei Yang, Kunlong Yin, Suzanne Lacasse, and Zhongqiang Liu. Time series analysis and long short-term memory neural network to predict landslide displacement. Landslides, 16(4):677–694, 2019.; Xin-She Yang, Siamak Talatahari, Amir Hossein Gandomi, and Amir Hossein Alavi, editors. Metaheuristics in Water, Geotechnical and Transport Engineering. Elsevier, 2013.; Yang Yang, Zhike Peng, Wenming Zhang, and Guang Meng. Parameterised time–frequency analysis methods and their engineering applications: A review of recent advances. Mechanical Systems and Signal Processing, 119:182 – 221, 2019.; Ruigen Yao and Shamim N. Pakzad. Autoregressive statistical pattern recognition algorithms for damage detection in civil structures. Mechanical Systems and Signal Processing, 31:355–368, 2012.; Zaher Mundher Yaseen, Ahmed El-shafie, Othman Jaafar, Haitham Abdulmohsin Afan, and Khamis Naba Sayl. Artificial intelligence based models for stream-flow forecasting: 2000–2015. Journal of Hydrology, 530:829–844, 2015.; Saleh Yousefi, Hamid Reza Pourghasemi, Sayed Naeim Emami, Soheila Pouyan, Saeedeh Eskandari, and John P. Tiefenbacher. A machine learning framework for multi-hazards modeling and mapping in a mountainous area. Scientific Reports, 10(1):12144, 2020.; F Zhang, Zaobao Liu, Lifeng Zheng, and Yu Zhang. Development of an adaptive relevance vector machine approach for slope stability inference. Neural Computing and Applications, 25(7):2025–2035, December 2014.; Yin Zhang, Miaolin Dai, and Zhimin Ju. Preliminary Discussion Regarding SVM Kernel Function Selection in the Twofold Rock Slope Prediction Model. Journal of Computing in Civil Engineering, 30(3):04015031, 2016.; Hongbo Zhao, Shunde Yin, and Zhongliang Ru. Relevance vector machine applied to slope stability analysis. International Journal for Numerical and Analytical Methods in Geomechanics, 36(5):643–652, 2012.; Wenming Zheng, Li Zhao, and Cairong Zou. Locally nearest neighbor classifiers for pattern classification. Pattern Recognition, 37(6):1307–1309, 2004.; Jian Zhou, Enming Li, Shan Yang, Mingzheng Wang, Xiuzhi Shi, Shu Yao, and Hani S. Mitri. Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Safety Science, 118:505–518, 2019.; Lim Yi Zhou, Fam Pei Shan, Kunio Shimizu, Tomoaki Imoto, Habibah Lateh, and Koay Swee Peng. A comparative study of slope failure prediction using logistic regression, support vector machine and least square support vector machine models. AIP Conference Proceedings, 1870(1):060012, 2017.; Yonglei Zhou, Changshui Zhang, and Jingchun Wang. Extended nearest feature line classifier. In Chengqi Zhang, H. W. Guesgen, and Wai-Kiang Yeap, editors, PRICAI 2004: Trends in Artificial Intelligence, volume 3157 of Lecture Notes in Computer Science, pages 183–190. Springer Berlin Heidelberg, 2004.; Zonglin Zhou and Chee Keong Kwoh. The pattern classification based on the nearest feature midpoints. In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, volume 3, pages 446–449, Aug 2004.; https://repositorio.unal.edu.co/handle/unal/82365; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

Dostupnost: https://repositorio.unal.edu.co/handle/unal/82365
https://repositorio.unal.edu.co/

32° Simposio Internacional de estadística 2023 : bioestadística y datos funcionales

Přispěvatelé: Universidad Nacional de Colombia. Sede Bogotá. Facultad de Ciencias. Departamento de Estadística

Témata: 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas, ANALISIS NUMERICO-PROCESAMIENTO DE DATOS-CONGRESOS,CONFERENCIAS,ETC, ANALISIS DE REGRESION-PROCESAMIENTO DE DATOS, ANALISIS DE REGRESION LOGISTICA, Numerical analysis - data processing congresses, Regression analysis - data processing, Logistic regression analysis, Estadística, Modelado, Regresión, Análisis de datos, Series de tiempo, Aprendizaje automático (Machine Learning), Redes neuronales, Bayesiano, Inferencia estadística, Control estadístico de procesos, Distribuciones estadísticas, Simulación, Visualización de datos

Popis souboru: 555 páginas; application/pdf

Relation: BLANCO, Liliana; ARUNACHALAM, Viswanathan; DHARMARAJA, Selvamuthu: Introduction to probability and stochastic processes with applications. John Wiley & Sons, 2012; DIEKER, Ton: Simulation of fractional Brownian motion, Masters Thesis, Department of Mathematical Sciences, University of Twente., Tesis de Grado, 2004; FRIEDRICH, Jan; GALLON, Sebastian; PUMIR, Alain; GRAUER, Rainer: Stochastic interpolation of sparsely sampled time series via multipoint fractional Brownian bridges. En: Physical Review Letters 125 (2020), Nr.17, p.170602; KRANSTAUBER, Bart; KAYS, Roland; LAPOINT, ScottD.; WIKELSKI, Martin; SAFI, Kamran: A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement. En: Journal of Animal Ecology 81 (2012),Nr.4,p. 738–746; YUAN, Chenggui; MAO, Xuerong: Convergence of the Euler–Maruyama method for stochastic differential equations with Markovian switching. Mathematics and Computers in Simulation 64(2004),Nr.2,p.223–235; Chen, Y., & Georgiou, T.(2015).Stochastic bridges of linear systems. IEEE Transactions on Automatic Control,61(2),526-531.; Wang, S., Ramkrishna, D.,& Narasimhan, V. Exact sampling of polymer conformations using Brownian bridges. The Journal of Chemical Physics, 153 (3), 034901, 2020; Kranstauber, B. Modelling animal movement as Brownian bridges with covariates.; CHEN, Nan; ZI, Xuemin; ZOU, Changliang. A distribution-free multivariate control chart. En: Technometrics 58 (2016), Nr. 4,p. 448–459.; HAPP, Clara; GREVEN, Sonja. Multivariate functional principal component analysis for data observed on different (dimensional) domains. En: Journal of the American Statistical Association 113 (2018), Nr.522, p. 649–659.; HAPP-KURZ, Clara. Object-Oriented Software for Functional Data. En: Journal of Statistical Software 93 (2020), Nr.5, p. 1–38.; JANG, Jeong H. Principal component analysis of hybrid functional and vector data. En: Statistics inmedicine 40 (2021), Nr. 24, p. 5152–5173.; JIANG, Qingchao; YAN, Xuefeng; ZHAO, Weixiang. Fault detection and diagnosis in; MONTGOMERY, Douglas C. Introduction to statistical quality control. John Wiley & Sons, 2020.; RESEARCH, Eigenvector. NIR of Corn Samples for Standardization Benchmarking. (2005).; RYAN, Thomas P. Statistical methods for quality improvement. John Wiley & Sons,2011.; Dixon, M.F., Halperin,I., & Bilokon, P. (2020). Machine learning in finance (Vol.1170). Springer.; Edelmann, D.,Fokianos, K., & Pitsillou, M.(2019). An updated literature review of distance correlation and its applications to time series. International Statistical Review, 87(2),237– 262. Wiley Online Library.; Chatterjee, S.(2021). A new coefficient of correlation. Journal of the American Statistical Association, 116 (536), 2009–2022.Taylor & Francis.; HERNÁNDEZ ESQUIVIAS, PEDRO, Análisis de sentimientos en Twitter en castellano con redes neuronales recurrentes LSTM, B.S. thesis, 2022.; HOCHREITER, SEPP y SCHMIDHUBER, JÜRGEN, Long short-term memory, Neural computation, 9 (8), 1735–1780, 1997, MIT Press.; ROSENBROCK, GERMÁN, TROSSERO, SEBASTIÁN, y PASCAL, ANDRÉS, Técnicas de análisis de sentimientos aplicadas a la valoración de opinión es en el lenguaje español, en XXVII Congreso Argentino de Ciencias de la Computación (CACIC) (Modalidad virtual, 4 al 8 de octubre de 2021), 2021.; SHINY , Shiny for R Gallery. Available at: https://shiny.posit.co/r/gallery/ 2016; CASELLA, G.,&BERGER, R.L.], Statistical Inference. Duxbury Press. 2002; FREEDMAN, D.A. Statistical Models: Theory and Practice. Cambridge University Press. 2009; Tanner, M. A., & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82(398),528–540.Taylor& Francis.; Schafer, J. L.(1997). Analysis of incomplete multivariate data. CRC Press.; Little, R., & Rubin, D.(2019). Statistical analysis with missing data (Vol.793). John Wiley & Sons.; Liu, C.(1994). Statistical analysis using the multivariate t distribution. Harvard University.; Liu, C.(1995). Missing data imputation using the multivariate t distribution. Journal of multivariate analysis, 53(1),139–158.Elsevier.; Dempster, A. P., Laird, N. M.,& Rubin, D.B.(1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1),1–22. Wiley Online Library.; Little, R.J.A.(1988). Robust estimation of the mean and covariance matrix from data with missing values. Journal of the Royal Statistical Society: Series C (Applied Statistics), 37(1), 23–38. Wiley Online Library.; Li, K.-H.(1988). Imputation using Markov chains. Journal of Statistical Computation and Simulation, 30(1),57–79.Taylor & Francis; PEÑA, D, Analisis de datos multivariantes, Cambridge: McGraw-Hill España, 2002.; ALEXANDER, C, . A primer on the ortogonal GARCH model, manuscript ISMA Centre, University of Reading, UK, 2000.; BOLLERSLEV, T, . A primer on the orthogonal GARCH model, Journal of econometrics, 31(3), 307-327,1986.; MINUTTI, C, Métodos de optimización en la construcción de portafolios (Doctoral dissertation, Tesis de licenciatura), Universidad Autónoma Chapingo, 2010.; ZIVOT, E, Introduction to Computational Finance and Financial Econometrics with R, Springer,2016.; Barraza M.,A.(2008). El estrés académico en alumnos de maestría y sus variables moduladoras: un diseño de diferencia de grupos. Avances en Psicología Latinoamericana. 26(2). pp. 270-289.; Escobedo M, Hernández J, Estebané V ,Martínez G, (2016). Modelos de Ecuaciones Estructurales: Características, Fases, Construcción, Aplicación y Resultados. CiencTrab.18(55): 16-22.; Romero, R., Babativa, G(2016). Modelo de lealtad apartir de un análisis de ecuaciones estructurales. Comunicaciones en Estadística, 9 (2), 165-197.; Ruiz, M.A., Pardo, A, San Martin, R. (2010), Modelos de Ecuaciones Estructurales, papeles del Psicólogo, 31(1), 34-45, Recuperado de: http://www.redalyc.org/pdf/778/77812441004.pdf.; BAILEY, D.W., TROTTER, M.G. KNIGHT, C.W. AND THOMAS, M.G.(2018), Use of GPS tracking collars and accelerometers for rangel and livestock production research, Translational Animal Science 2:81–88.; CASTILLA, A.L., MORA-DELGADO, J., RODRÍGUEZ-MÁRQUEZ, M.A.,&LOPEZ, J.(2022), Cattle movement as a function of some biotic and abiotic factors in atropical pasture. Revista de Ciencias Agrícolas, 39 (2), 69-88. https://doi.org/10.22267/rcia.223902.184.; LI, Y.,SCHWARTZ, W.J.,&INDIC, P.(2020)., Dynamics of periodically forced finite N-oscillators, with implications for the social synchronization of animal rest–activity rhythms, Translational Animal Science 2:81–88. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(10),103106.; MUÑOZ-ESPINOZA, M.,ARTIEDA-ROJAS, J.,ESPINOZA-VACA, S.,CURAY-QUISPE, S.,PÉREZ-SALINAS, M.,NÚÑEZ-TORRES, O.,&BARROS-RODRÍGUEZ, M.(2016), Granjas sostenibles: integración de sistemas agropecuarios, Tropical and Subtropical Agroecosystems, 19(2),93-99.; MORA-DELGADO. J.M.,SERRANO. R.,VARÓN. R.P.,&DÍAZ. G.(2018), Use of GPS and GIS for monitoring of cattle’s grazing on a silvipasture of Tolima (Colombia), Investigaciones Andina.20(36).23-38.; POLANIA, Y.,DELGADO, J.M.,SERRANO, R.,&PIÑEROS, R.(2013), Movimiento de ganado en pastoreo en un sistema silvo pastoril del valle cálido del Magdalena tolimense (Colombia), Revista Colombiana de Ciencia Animal, 6(1).; RODRIGUEZ-MARQUEZ, M.A.R.,DÍAZ, H.A.G.,&DELGADO, J.M.(2022), Behavioral fractal method associated with GPS tracking to spatial activity sequences of grazing cattle, Scientia Agricola, 80. https://doi.org/10.1590/1678-992X-2022-0052.; RUSSELL, M.,BAILEY, D.,THOMAS, M.,&WITMORE, B.(2012), Grazing Distribution and Diet Quality of Angus, Brangus, and Brahman Cows in the Chihuahuan Desert, Rangeland Ecology & Management,65(4),371-381.RetrievedOctober1,2020,from http://www.jstor.org/stable/41681555.; SUN, J.,BOLLT, E.M.,PORTER, M.A.,&DAWKINS, M.S.(2011), A mathematical model for the dynamics and synchronization of cows. Physica D: Non linear Phenomena, 240(19), 1497-1509.; UNGAR, E.D.,HENKIN, Z.,GUTMAN, M.,DOLEV, A.,GENIZI, A.,&GANSKOPP, D.(2005), Inference of animal activity from GPS collar data on free-ranging cattle., Rangel and Ecology & Management, 58(3), 256-266. https://doi.org/10.2111/1551-5028(2005)58[256:IOAAFG]2.0.CO; Ticona, Regina, Joe Tekli, Irvin Dongo, Renato Guzman, y Richard Chbeir. Toward RDF Normalization. 2017. http://arxiv.org/abs/1707.03602; Martinez, J. ANÁLISIS DE LA FORMACIÓN DEL PRECIO DE LA VIVIENDA URBANA EN BOGOTÁ: LOCALIDADES DE KENNEDY, FONTIBÓN Y ENGATIVÁ EN EL ÚLTIMO BIENIO 2015-2016. Tesis de maestría, Instituto Geográfico Agustín Codazzi, 2016.; w3c. Resource Description Framework (RDF): Concepts and Abstract Syntax. 2004. https:// www.w3.org/TR/rdf-concepts/ [Web; accedido el 06-10-2018].; Pappas, Alexandros, Georgia Troullinou, Giannis Roussakis, Haridimos Kondy lakis B., y Dimitris Plexousakis. The Semantic Web. Volumen 10249, 2017. Páginas 387-403. ht tp:// link. springer.com/10.1007/978-3-319-58068-5. DOI:10.1007/978-3-319-58068-; Cheng, Gong, Feng Ji, Shengmei Luo, Weiyi Ge, y Yuzhong Qu. BipRank: Ranking and summarizing RDF vocabulary descriptions. Volumen 7185 LNCS, 2012. Páginas 226-241. DOI:10.1007/978-3-642-29923-0_15.; Kawtrakul, Asanee, Dominique Laurent, Nicolas Spyratos, y Yuzuru Tanaka. Information search, integration, and personalization. Volumen 421 CCIS, 2014. Paginas 69-87. DOI:10.1007/978-3-319-08732-0.; Manolescu, Ioana. Structural Summarization of Semantic Graphs. 2018. 181; Zhang, Xiang, Gong Cheng, y Yuzhong Qu. Ontology summarization based on rdf sentence graph. En Proceedings of the 16th international conference on World Wide Web – WWW '07, 2007. Página 707. DOI:10.1145/1242572.1242668.; Peroni, Silvio, Enrico Motta, y Mathieu D'Aquin. Identifying key concepts in an ontology, through the integration of cognitive principles with statistical and topological measures. Volumen 5367 LNCS, 2008. Páginas 242-256. DOI: 10. 1007/978-3-540-89704-0_17.; Fan, Zhengjie. Concise Pattern Learning for RDF Data Sets Interlinking. Tesis de doctorado, Université, 2017.; Semantic Search On Summarized RDF Triples. 2017.; Basse, Adrien, Fabien Gandon, Isabelle Mirbel, y Moussa Lo. Incremental characterization of RDF Triple Stores. Volumen 24, 2012. Páginas 691201-2. https://hal. inria.fr/ hal- 00691201v2.; Guzewicz, Pawel, y loana Manolescu. Quotient RDF summaries based on type hierarchies. En Proceedings - IEEE 34th International Conference on Data Engineering Workshops, ICDEW 2018, 2018. Páginas 66-7 1. DOI:10.1109/ICDEW.2018.00018.; Zneika, Mussab, Claudio Lucchese, Dan Vodislav, y Dimitris Kotzinos. Summarizing Linked Data RDF Graphs Using Approximate Graph Pattern Mining. En Proc. 19th International Conference on Extending Database Technology, 2016. Páginas 684-685. DOI:10.5441/002/edbt.2016.86.; Bursztyn, Damian, François Goasdoué, y loana Manolescu. Optimizing Reformulation-based Query Answering in RDF. En 18th International Conference on Extending Database Technology (EDBT), 2014. Páginas 265-276. DOI:10.5441/002/edbt.2015.24.; Pouriyeh, Seyedamin, Mehdi Allahyari, Qingxia Liu, Gong Cheng, Hamid Reza Arabnia, Yuzhong Qu y Krys Kochut. Graph-based Ontology Summarization: A Survey. 2018. Páginas 1-15. DOI:10.5441/002/edbt.2015.24. http://arxiv.org/abs/1805. 06051; Tzitzikas, Y., D. Kotzinos y Y. Theoharis. On ranking RDF schema ele-ments (and its application in visualization). En Journal of Universal Com-puter Science, volumen 13, número 12, 2007. Páginas 1854-1880. ISSN: 09486968 (ISSN). http://www.scopus.com/inward/record.url?eid=2-s2. 0- 40849134188(&} partner ID=40(&}md5=11f36c1ed93169bec179b20aaf0a2555.; Cheng, Gong, Feng Ji, Shengmei Luo, Weiyi Ge y Yuzhong Qu. BipRank: Ranking and summarizing RDF vocabulary descriptions. En Lecture Notes in Computer Science (includ-ing subseries Lecture Notes in Artificial Inteligence and Lecture Notes in Bioinformatics), volumen 7185 LNCS, 2012. Páginas 226-241. DOI:10.1007/978-3-642-29923-0_15.; Cheng, Gong, Weiyi Ge y Yuzhong Qu. Generating summaries for ontology search. En Pro-ceedings of the 20th international conference companion on World wide web - WWW '11, 2011. Página 27. DOI:10.1145/1963192.1963207. http://portal. acm.org/citation. ctm?doid=1963192. 1963207.; Drumond, Lucas, Steffen Rendle y Lars Schmidt-Thieme. Predicting RDF triples in incomplete knowledge bases with tensor factorization. En Proceedings of the 27th Annual ACM Symposium on Applied Computing - SAC '12, 2012. Página 326. DOI:10.1145/2245276.2245341. http://dl.acm. org/citation. cfm?doid=22452762246341; Sebiric, Sejla, François Goasdoué y loana Manolescu. Query-Oriented summarization of RDF graphs. En Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volumen 9147, 2015. Paginas 87-91. DOI:10.1007/978-3-319-20424-6_9.; Zneika, Mussab, Dan Vodislav, Dimitris Kotzinos y Quality Metrics For RDF Graph Summarization. Quality Metrics For RDF Graph Summarization. 2018.; Su, Xiang, Hao Zhang, Jukka Riekki, Ari Keränen, Jukka K. Nurminen y Libin Du. Connect-ing loT sensors to knowledge-based systems by transforming SenML to RDF. En Procedia Computer Science, volumen 32, 2014. Páginas 215-222. DOI:10.1016/j procs.2014.05.417.; Hassad, Sara El. Learning Commonalities in RDF. 2017. DOI:10.1007/978-3-319-58068-5.; Ayvaz., Serkan y Aydar, Mehmet. Using RDF Summary Graph For Keyword-based Seman-tic Searches. 2017. DOI:10.1109/COMPSAC.2015.107. http://arxiv. org/abs/1707. 03602; Discipline, S T a P S, Cnu, Section, Vincent, Chaubet, Richard, Montoya y Nicolas, Forestier. Université de pau et des pays de l'adour. 2011.; Zliobaite, Indre, Leaming under Concept Brif: an Overview. 2010. DOT: 10.1002/sam. http://arxiv.org/abs/1010.4784; Ticona-herrera, Regina, Tekli, Joe, Dongo, Irvin, Guzman, Renato y Chbeir, Richard. Toward RDF Normalization.; Manolescu, Ioana. Structural Summarization of Semantic Graphs. 2018.; Zhu, Ganggao y Iglesias, Carlos A. Sematch: Semantic similarity framework for Knowledge Graphs. 2017. DOI:10.1016/j.knosys.2017.05.02l. UNAL https://www.simposioestadistica.unal.edu.co/; Hees, Jörn, Bauer, Rouven, Folz, Joachim, Borth, Damian y Dengel, Andreas. An evolutionary algorithm to learn SPARQL queries for source-target-pairs finding pat-terns for human associations in DBpedia. 2016. DOI:10.1007/978-3-319-49004-5_22. http://arxiv.org/abs/1607.07249; De Vries, Gerben Klaas Dirk y De Rooij, Steven. Substructure counting graph kernels for machine learning from RDF data. 2015. DOI:10.1016/j.websem.2015.08.002.; Xylogiannopoulos, Konstantinos F, Karampelas, Panagiotis y Alhajj, Reda. Dynamic Pattern Detection for Big Data Stream Analytics.; Potoniec, Jedrzej. An on-line learning to query system. 2016.; Hayes, Jonathan y Gutierrez, Claudio. Bipartite Graphs as Intermediate Model for RDF. 2004. DOI:10.1007/978-3-540-30475-3_5.; Ayvaz, Serkan, Aydar, Mehmet y Melton, Austin. Building Summary Graphs of RDF Data in Semantic Web. 2015. DOI:10.1109/COMPSAC.2015.107.; Amaout, Hiba y Elbassuoni, Shady. Effective searching of RDF knowledge graphs. 2018. DOI:10.1016/j.websem.2017.12.001.; Cebiric, Sejla, Goasdoué, François y Manolescu, Ioana. A framework for eficient represen-tative summarization of RDF graphs. 2017.; Aydar, Mehmet, Ayvaz, Serkan y Melton, Austin. Automatic weight generation and class predicate stability in RDF summary graphs. 2015./; Hettmansperger, T. (1984). Statistical Inference Based on Ranks. John Wiley & Sons, New York.; Lehmann, E. L., & D'A brera, H. J. (1975). Nonparametrics: Statistical Methods Based on Ranks. Holden-Day.; Shuster, J. J. (2019). CRC Handbook of Sample Size Guidelines for Clinical Trials. CRC Press.; McCrum-Gardner, E. (2008). Which Is the Correct Statistical Test to Use?. British Journal of Oral and Maxillofacial Surgery, 46(1), 38-41. Elsevier.; McWilliams, T. P. (1990). A Distribution- Free Test for Symmetry Based on a Runs Statistic.; Journal of the American Statistical Association, 85(412), 1130-1133. Taylor & Francis Group.; Hall, P., & Heyde, C. C. (2014). Martingale Limit Theory and Its Application. Academic Press.; Baklizi, A. (2003). A Conditional Distribution Runs Test for Symmetry. Journal of Nonpara-metric Statistics, 15(6), 713-718.; Rocha, P. (1994). Estadística de Prueba para Problemas de Localización Basada en Rachas. Tesis de Maestría, Universidad Nacional de Colombia, Facultad de Ciencias, Departamento de Estadística, Bogotá.; Randles, R. H., & Wolfe, D. A. (1979). Introduction to the Theory of Nonparametric Statistics. John Wiley.; DIRECCIÓN DE POLÍTICAS Y ESTRATEGIA, Fiscalía General de la Nación, 2023.; ARRIETA-BURGOS, E., DUQUE-PEDROZA, A. Y DíEz-RUGELES, M., Delitos sexuales en contra de menores de edad en Colombia: caracterización criminológica y políticocriminal, Revista Criminalidad, 62(2): 247-274, 2020.; NORZA-CÉSPEDES, ERVYN, Y ESPINO-DUQUE, GLORIA PATRICIA., Criminalidad y análisis espacial de los delitos en Colombia, 2010, Revista Criminalidad, 53(1), 17-43, 2011.; MEJIA, D; ORTEGA, D; ORTIz, K., Un análisis de la criminalidad urbana en Colombia, Banco de desarrollo de América Latina, 2015.; CHAZAL F., FASY, B. (2014) CONFIDENCE SETS FOR PERSISTENCE DIAGRAMS. Annals of Statistics, Vol. 42, No. 6, 2301-2339.; JIsu KIM, BRITTANY TERESE FASY AND FABRIZIO LECCI (2015) Tutorial on the R package TDA., Journal of Symbolic Computation; PETER BUBENIK (2023) Department of Mathematics. University of Florida; Jolliffe, Ian T. (2002). Principal component analysis for special types of data. Springer.; Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome H. (2009). The elements of statistical learning: data mining, inference, and prediction, Vol. 2. Springer.; Van der Maaten, Laurens, Hinton, Geoffrey. (2008). "Visualizing data using t-SNE." Journal of machine learning research, 9(11).; Mclnnes, Leland, Healy, John, Melville, James. (2018). "Umap: Uniform manifold approx-imation and projection for dimension reduction." arXiv preprint arXiv: 1802.03426.; Pramanik, Md, Rahman, Md Mahbubur, Anam, ASM, Ali, Amin Ahsan, Amin, M Ashraful, Rahman, AKM, and others. (2021). "Modeling Traffic Congestion in Developing Countries using Google Maps Data." In Future of Information and Communication Conference, pp. 513-531. Springer.; Wu, Tianlong, Chen, Feng, Wan, Yun. (2018). "Graph attention LSTM network: A new model for traffic flow forecasting." In 2018 Sth International Conference on Information Science and Control Engineering (ICISCE), pp. 241-245. TEEE.; United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. Disponible en: https://sustainabledevelopment.un.org/post2015/ transformingourworld; World Health Organization (WHO). (2021). Ambient air pollution: Health impacts. Disponible en: https://www.who.int/health-topics/ambient-air-pollution# tab=tab_; McVean, Gil. (2009). "A genealogical interpretation of principal components analysis." PLoS genetics, 5(10), e 1000686.; Velliangiri, S, Alagumuthukrishnan, SJPCS, and others. (2019). "A review of dimensionality reduction techniques for efficient computation." Procedia Computer Science, 165, 104-111. Elsevier.; Ignacio Pérez, José Ricardo Cure, Néstor Monroy. Modelo de predicción y manejo de cultivos de rosas. Keywords: Grado - día, Cosecha, Invernadero.; Mark Everingham, Luc Van Gool, C. K. I. Williams, J. Winn, Andrew Zisserman. The PASCAL Visual Object Classes (VOC) challenge. (2010).; Andrew Engel. Feature Engineering for Time Series Data. Keywords: Feature Engineering for Time Series Data; Rubén Costas, Patricio Mac Donagh, Elizabeth Weber, Santiago Figueredo, Claudio Gómez, Pedro Irschick. Modelos predictivos de la producción de Pinus taeda empleando variables vinculadas con las podas. Bosque (Valdivia), 27(2), 2006. DOI:10.4067/s0717-92002006000200004.; Manual de observaciones fenológicas, Servicio Nacional de Meteorología e Hidrología (Senanhi), Perú, 2011.; Francesca Lazzeri. Introduction to feature engineering for time series forecasting. (2021).; Rob J Hyndman, George Athanasopoulos. Forecasting: Principles and Practice; Classical Decomposition, 2021.; Alex Mitrani. Time Series Decomposition and Statsmodels Parameters, 2020.; Charles Potters. Variance Inflation Factor (VIF), 2021.; Benjamin Kedem, Konstantinos Fokianos. Regression Models for Time Series Analysis. Wiley- Interscience, 2002.; A lan McLeod. Time Series Analysis: Methods and Applications Volume 30. Time Series Analysis with R. Elsevier, 2012. DOI:10.1016/B978-0-444-53858-1.00023-5.; Walter Zucchini, lain MacDonald. Hidden Markov Models for Time Series: An Introduction Using R. Chapman and Hall/CRC, 2009.; Sam Ewen. The Poisson Distribution.; Sachin Date. An Illustrated Guide to the Poisson Regression Model.; Sachin Date. Negative Binomial Regression: A Step by Step Guide.; NCSS Statistical Software. Negative Binomial Regression.; A. Colin Cameron, Pravin K. Trivedi. Regression analysis of count data. Cambridge University Press, 2013.; CFI Corporate Financial Institute. Overfitting - A modeling error that occurs when a function corresponds too closely to a particular set of data.; Shaun Turney. Coefficient of Determination (R squared) %7C Calculation & Interpretation.; Rohan Joseph. Grid Search for model tuning.; Gareth James, Daniela Witten, Trevor Hastie. An Introduction to Statistical Learning: With Applications in R. Springer Nature, 2017.; James Franklin. The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer, 27(2), 2005. DOI:10.1007/bf02985802.; Emelina López, Marcos Ruiz. Análisis de datos con el Modelo Lineal Generalizado. Una aplicación con R. Revista española de pedagogía, 2011.; Jerome Friedman, Trevor Hastie, Robert Tibshirani. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 2010. DOI:10.18637/jss.v033.i01.; Skipper Seabold, Josef Perktold. statsmodels: Econometric and statistical modeling with python. In 9th Python in Science Conference, 2010.; R: A Language and Environment for Statistical Computing, R Core Team, 2022.; Robert E Fay III, Roger A Herriot. Estimates of income for small places: an application of James-Stein procedures to census data. Journal of the American Statistical Association, 74(366a), 1979.; Richard Valliant. Generalized variance functions in stratified two-stagy sampling. Journal of the American Statistical Association, 82(398), 1987.; H. S. Gutiérrez. Estrategias de Muestreo: Diseño de encuestas y estimación de parámetros. Ediciones de la U, 2016.; IGAC, Suelos y Tierras de Colombia. Instituto Geográfico A gustín Codazzi, IGAC., 2016.; THOMPSON L. M. AND TROEH F. R, Soils and Fertility. 1980.; HARVEY D., Teorías, leyes y modelos en geografta. Alianza Editorial, 1969.; PABÓN J. & MONTEALEGRE J., Los Fenómenos de El Niño Y de la Niña. Bogotá, Editorial Gente Nueva, 2017. [5] CRESSIE N., Statistics for Spatial Data. New York: Jhon Wiley & Sons, 1993.; TOBLER W., A Computer Movie Simulating Urban Growth in the Detroit Region, Economic Geography, Supplement: Proceedings. International Geographical Union. Commission on Quantitative Methods, vol. 46, pp. 234-240, 1970.; UNWIN D., Introductory Spatial Analysis. New York: Methuen & Co., 1981.; RHIND D. W., A skeletal overview of spatial interpolation techniques, Computer Applica-tions, vol. 2, pp. 293-309, 1975.; ISAAKS E. H. & SRIVASTAVA M., Applied Geostatistics. New York: Oxford University Press, Inc., 1989; JOURNEL A. G. & HUIBREGTS CH. J, Mining Geoestatistics. Francia: Harcourt Brace Jnovich, Publishers, 1978.; GIRALDO R., Introducción a la Geoestadistica, Teoría y Aplicación. Bogotá, D.C. : Universidad Nacional de Colombia, 2011.; GIRALDO R, Geoestadística con Datos Funcionales. Conferencia, 32° Simposio Internacional de Estadística, Bioestadística y Datos Funcionales, Aug. 03, 2023.; SIABATO W., Técnicas de Análisis Modelado Espacial (TAME). Notas de apoyo en clase. Bogotá D.C., Colombia: Universidad Nacional de Colombia, 2023.; CRESSIE N. & C. WIKLE, Statistics for Spatio-Temporal Data. New Jersey, USA y Canada : Wiley & Sons, Inc, 2011.; HARVEY D., Social justice and the city. Johns Hopkins University Press, 1973.; WATERS, N., Unit 40 - Spatial interpolation 1, 1990.; STEEN F. H. & BALLOU D. H., Geometría Analítica. México D.F: Publicaciones Cultural, S. A., 1966.; DE IACO, S. & CAPELLO, C., Spatio-temporal modeling and prediction, ISI Online Course: Spatio-temporal modeling and prediction, 2023.; JHONSTON K., VER HOEF J., KRIVORUCHYO, K. & LUCAS, N., Using Arcgis Geostatis-tical Analyst, GIS by ESRI, United States of America, 2001.; DíAz-FRANCÉS., Introducción a Conceptos Básicos de Geoestadística, Memorias Seminario Estadística y Medio Ambiente, Guanajuato, México, Centro de Investigación en Matemáticas, CIMAT, 1993.; NowicKI, K. AND SNIDERS, T. A. B., Estimation and prediction for stochastic block-structures. Journal of the American statistical association, 96(455): 1077-1087, 2001.; ] KOLACZYK, E. D. AND CÁRDI, G., Statistical analysis of network data with R, Springer, Volume 65, 2020.; HASELMAYER, M. AND JENNY, M., Sentiment analysis of political communication: Combining a dictionary approach with crowdcoding, Quality & Quantity, 51:2623-2646. 2017.; ampo Elías Pardo. Estadística Descriptiva Multivariada. Universidad Nacional de Colom-bia. Sede Bogotá, 2020.; Encuesta Nacional de Calidad de Vida. DANE, 2023. Agencia Nacional de Datos Abiertos.; DANE. Encuesta Nacional de Calidad de Vida. 2023. Disponible en: https:// microdatos.dane.gov.co/index. php/catalog/POBCONVID; Glenn C. Blomquist. Measuring Quality of Life. A Companion to Urban Economics, 2006. DOl: https://doi.org/10.1002/9780470996225. ch28; Bilver Astorquiza-Bustos, Kevin Bravo-Bolaño, Enmanuel Aguirre-Bernal. Indice de precariedad laboral en Colombia: una construcción teórica y analítica a partir de mi-crodatos. Revista Mexicana de Economía y Finanzas Nueva Época REMEF, vol. 18, no. 1, 2022. Palabras clave: Precariedad laboral, microdatos, metodología de conjuntos di-fusos, Colombia. Resumen y DOI: https://www.remef.org.mx/index.php/remef/ article/view/822; IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales). (s.f.). Contami-nación y calidad ambiental.; Holmes, J., Hassini, S. Discrete-Time Markov Chain Modelling of the Ontario Air Quality Health Index. Water Air Soil Pollut 232, 158 (2021).; Blanco-Castañeda, L., & Arunachalam, V. (20J3). Applied Stochastic Modeling. Springer.; Holmes, J., Hassini, S. Discrete-Time Markov Chain Modelling of the Ontario Air Quality Health Index. Water Air Soil Pollut 232, 158 (2021). https://www.paho.org/es/temas/ calidad-aire/calidad-aire-ambiente; IBOCA (Índice Bogotano de Calidad del Aire). (s.f.). ¿Qué es el IBOCA? Recuperado de http://iboca.ambientebogota.gov. co/publicaciones/175/que-es-el-iboca/; DÍAz, F., y HERNÁNDEz, G., Estrategias docentes para un aprendizaje significativo: una interpretación constructivista (2" ed.), Mc Graw Hill, 2002.; CABERO, J., Las TIC y las Universidades: retos, posibilidades y preocupaciones, Revista de la Educación Superior, 34 (3), 77-100.; HAIR, J., HULT, T., RINGLE, C. Y SARSTED, M., A primer on partial least square structural equation modeling (PLS-SEM) (2" ed.) , Sage Publications,2017.; SÁNCHEZ-OTERO, M., GARCÍA-GUILIANY, J., STEFFENS-SANABRIA, E., Y PALMA, H. H., Estrategias pedagógicas en procesos de enseñanza y aprendizaje en la educación superior incluyendo tecnologías de la información y las comunicaciones, Información Tecnológica, 30(3), 277-286.; VARGAS, C. G. J., La investigación prixeológica: un enfoque alternativo, Praxis Pedagóg-ica, 20(26), , 117-148.; Madden, L.V., Hughes, G., & Bosch, F.V. Spatial aspects of epidemics II: A theory of spatio-temporal disease dynamics. En: The Study of Plant Disease Epidemics. APS Press - The American Phytopathological Society, St. Paul, Minnesota, U.S.A., 2007. 421 pp. ISBN 978-0-89054-354-2.; Grubesic, Tony H, Wei, Ran y Murray, Alan T, Spatial clustering overview and comparison: Accuracy, sensitivity, and computational expense, Annals of the Association of American Geographers, vol. 104, no. 6, pp. 1134-1156, 2014, Taylor & Francis.; Siabato, Willington y Guzmán-Manrique, Jhon, Spatial autocorrelation and the development of quantitative geography, Cuadernos de Geografta: Revista Colombiana de Geografia, vol. 28, no. 1, pp. 1-22, 2019.; Esri, Spatial Statistics Resources, Disponible en: https://spatialstats-analysis-1/ hub.arcgis.com/, 2021.; David R. Cagna, Terence E. Donovan, James R. McKee, Frederick Eichmiller, James E. Metz, Jean-Pierre Albouy, Ricardo Marzola, Kevin G. Murphy y Matthias Troeltzsch, "Annual review of selected scientific literature: A report of the Committee on Scientific Investigation of the American Academy of Restorative Dentistry," The Journal of Prosthetic Dentistry, vol. 126, no. 3, pp. 276-359, 2021. DOI: https://doi.org/10.1016/j.prosdent.2021 06.014.; E. L. Kaplan y Paul Meier, "Nonparametric Estimation from Incomplete Observations," Journal of the American Statistical Association, vol. 53, no. 282, pp. 457-481, 1958. URL: http://www.jstor.org/stable/2281868 (visitado el 19-07-2023).; David G. Kleinbaum y Mitchel Klein, Survival Analysis: A Self-Learning Text, Springer New York, 2012.; David Machin, Yin Bun Cheung y Mahesh Parmar, Survival Analysis: A Practical Approach, 2nd ed., Wiley, 2023.; Terry M. Thereau y The R Foundation, Survival Analysis in R, marzo de 2023. URL: https://cran.rproject.org/web/packages/survival/index.html (visitado el 24- 03-2023).; J. P. Van Nieuwenhuysen, W. D'Hoore, J. Carvalho y V. Qvist, "Long-term evaluation of extensive restorations in permanent teeth," J Dent, vol. 31, no. 6, pp. 395-405, 2003. DOI: 10. 1016/s0300-5712 s0300-5712(03) 00084-8; Gail Potter, Jimmy Wong, Irvin Alcaraz, Peter Chi, y otros, Web application teaching tools for statistics using R and shiny. Technology Innovations in Statistics Education, vol. 9, no. 1, 2016.; Andrew Gelman, John B Carlin, Hal S Stern y Donald B Rubin, Bayesian data analysis. Chapman and Hall/CRC, 1995.; Yen-Ting Lin y Min Jou, Integrating popular web applications in classroom learning environments and its effects on teaching, student learning motivation and performance. Turkish Online Journal of Educational Technology-TOJET, vol. 12, no. 2, pp. 157-165, 2013.; Gil Perry, Silla Blondheim y Eliran Kuta, D-ID, IA generativa. Recuperado julio de 2023, de https://www.d-id.com/ 1 2023.; NATIONAL HEART, LUNG AND BLOOD INSTITUTE, Pneumonia Recovery, 2022. Recuper-ado de https://www.nhlbi.nih.gov/health/pneumonia/recover.; R CoRE TEAM, R: A Language and Environment for Statistical Computing, 2020. URL = https://www.R-project.org/; Ros-GUTIÉRREZ A, To https://www.R-project.org/AM V. • (2023) An updated estimation approach for SEIR models wun swenuse penurbations: Application to COVID-19 data in Bogotá. PLOS ONE 18(8): e0285624. htups://doi. org/ 10. 1371/journal pone 0285624; Soumyya Kanti Datta, Mohammad Abuzar Shaikh, Sargur N Srihari y Mingchen Gao, "Soft attention improves skin cancer classification performance," en Interpretabiliry of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data: 4th International Workshop, iMIMIC 2021, and Ist International Workshop, TDA4MedicalData 2021, Held in Conjunction with MICCAT 2021, Strasbourg, France, September 27, 2021, Proceedings 4, pp. 13-23, 2021, Springer.; Giona Kleinberg, Michael J Diaz, Sai Batchu y Brandon Lucke-Wold, "Racial under-representation in dermatological datasets leads to biased machine learning models and inequitable healthcare," en Journal of biomed research, vol. 3, no. 1, p. 42, 2022, NIH Public Access.; MaryBeth B Culp y Natasha Buchanan Lunsford, "Melanoma among non-Hispanic black Americans," en Preventing Chronic Disease, vol. 16, 2019, Centers for Disease Control and Prevention.; Andres MORALES-FORERO, Lili RUEDA, Sebastian GIL-QUINONEZ, M BARRERA, Samuel BASSETTO y Eric COATANEA, "AN INSIGHT INTO RACIAL BIAS IN DER-MOSCOPY REPOSITORIES: A HAM10000 DATASET ANALYSIS," presentado en Journal of the European Academy of Dermatology and Venereology, agosto de 2023.; International Skin Imaging Collaboration y otros, "Siim-isic 2020 challenge dataset," en International Skin Imaging Collaboration, 2020.; Marc Combalia, Noel CF Codella, Veronica Rotemberg, Brian Helba, Veronica Vilaplana, Ofer Reiter, Cristina Carrera, Alicia Barreiro, Allan C Halpern, Susana Puig y otros, "Ben20000: Dermoscopic lesions in the wild," en arXiv preprint ar Xiv:1908.02288, 2019.; Philipp schandl, Cliff Rosendahl y Harald Kittler, "The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,' " en Scientific data, vol. 5, no. 1, Pp. 1-9, 2018, Natere Publishing Group.; Jose L Cortez, Juan Vasquez y Maria L. Wei, "The impact of demographics, socioeconomics, and health care access on melanoma outcomes," en Journal of the American Academy of Dermatology, vol. 84, no. 6, pp. 1677-1683, 2021, Elsevier.; Zehra Rizvi, Viktor Kunder, Hanna Stewart, Paola Torres, Sana Moon, Nimisha Lingappa, Mallory Kazaleh, Varshini Mallireddigari, Julian Perez, Nigel John y otros, "The bias of physicians and lack of education in patients of color with melanoma as causes of increased mortality: a scoping review," en Cureus, vol, 14, no. 11, 2022, Cureus.; JC Lester, JL. Jia, L Zhang, GA Okoye y E Linos, *Absence of images of skin of colour in publications of COVID-19 skin manifestations," en British Journal of Dermatology, vol. 183, no. 3, pp. 593-595, 2020, Blackwell Publishing Ltd Oxford, UK.; Patricia Patricia Louie y Rima Wilkes, "Representations of race and skin tone in medical textbook imagery," en Social Science & Medicine, vol. 202, PP. 38-42, 2018, Elsevier.; Mina Kim, Susan L Boone, Dennis P West, Alfred W Rademaker y Roopal V Kundu, "Perception of skin cancer risk by those with ethnic skin," en Archives of dermatology, vol. 145, no. 2, pp. 207-208, 2009, American Medical Association.; "Why the Color of Your Skin Can Affect the Quality of Your Diagnosis," https://www.improvediagnosis.org/dxiq-column/ why-the-color-of-your-skin-can- affect-the-quality-of-your-diagnosis/ consultado el 01-06-2023.; Sean M Dawes, Sheena Tsai, Haley Gittleman, Jill S Barnholtz-Sloan y Jeremy S Bor-deaux, "Racial disparities in melanoma survival," en Journal of the American Academy of Dermatology, vol. 75, no. 5, pp. 983-991, 2016, Elsevier.; Joshua Brady, Reem Kashlan, Julie Ruterbusch, Mehdi Farshchian y Meena Moossavi, "Racial disparities in patients with melanoma: a multivariate survival analysis," en Clinical, Cosmetic and Investigational Dermatology, PP. 547-550, 2021, Taylor & Francis.; Hugh M Gloster Jr y Kenneth Neal, "Skin cancer in skin of color," en Journal of the American Academy of Dermatology, vol. 55, no. 5, pp. 741-760, 2006, Elsevier.; U.S. Census Bureau Newsroom, https://www.census-gov/newsroom/releases/ archives/population/ cb12-243. html, consultado el 01-06-2023.; Mona A Gohara, "Skin cancer in skins of color," en Journal of drugs in dermatology: JDD, vol. 7, no. 5, pp. 441-445, 2008.; Xiao-Cheng Wu, Melody J Eide, Jessica King, Mona Saraiya, Youjie Huang, Charles Wiggins, Jill S Barnholtz-Sloan, Nicolle Martin, Vilma Cokkinides, Jacqueline Miller y otros, "Racial and ethnic variations in incidence and survival of cutaneous melanoma in the United States, 1999-2006," en Journal of the American Academy of Dermatology, vol. 65, no. 5, pp. S26-el, 2011, Elsevier.; Stephen J Merrill, Madhan Subramanian y Dianne E Godar, "Worldwide cutaneous malignant melanoma incidences analyzed by sex, age, and skin type over time (1955-2007): Is HPV infection of androgenic hair follicular melanocytes a risk factor for developing melanoma exclusively in people of European-ancestry?," en Dermato-endocrinology, vol. 8, no. 1. p. e1215391, 2016, Taylor & Francis.; Alpana K Gupta, Mausumi Bharadwaj y Ravi Mehrotra, Skin cancer concerns in people of color: risk factors and prevention," en A sian Pacific journal of cancer prevention: APJCP, vol. 17, no. 12, p. 5257, 2016, Shahid Beheshti University of Medical Sciences.; Andres Morales-Forero, Samuel Bassetto y Eric Coatanea, "Toward safe AI," en Al & SOCIETY, vol. 38, no. 2, pp. 685-696, 2023, Springer.; Cynthia Rudin, "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead," en Nature machine intelligence, vol. I, no. 5, pp. 206-215, 2019, Nature Publishing Group UK London.; ELCA 2016 RONDA 3. Bases de datos Rural, formulario personas. Tomado de https: //encuestalongitudinal. uniandes.edu. co/es/; PARDO, C. E., (2020). Estadística descriptiva multivariada. Universidad Nacional.; RAMONI, J. Y ORLANDONI, G., (2016). Análisis de la estructura del mercado laboral en Colombia: un estudio por género mediante correspondencias múltiples. Universidad de Santander. Recuperado de https://repositorio.uam.es/bitstream/handle/10486/ 690541/CB_113_2. pdf; MAYORGA, M.,Caracterización ecofisiológica de curuba (Passiflora tripartita var. Mollis-sima) en dos condiciones ambientales, Tesis de maestría en Ciencias A grarias, Universidad Nacional de Colombia, Bogota, 2016.; R CORE TEAM, R: A Language and Environment for Statistical Computing, Vienna, Austria, R Foundation for Statistical Computing, 2023. url - https://www.R-project.org/; MARDIA, K. V. AND KENT, J. T. AND BIBBY, J. M., Multivariate analysis, London, Academic Press, 2002.; GOWER, J. C. AND KRZANOWSKI, W.J., Analysis of distance for structured multivariate data and extensions to multivariate analysis of variance, Applied statistics, 48(4), 505-519, 1999.; C. Zelada, "Evaluación de modelos de clasificación." Recuperado de https://rpubs.com/ chzelada/275494, 2017. Online.; Haolun S., Yuping Y., Liangliang W., Da M., Mirza F. B., Jian P. y Jiguo C., "Two-Dimensional Functional Principal Component Analysis for Image Feature Extraction.* Recuperado de https://doi.org/10.1080/10618600.2022. 2035738, 2022. Online.; M.E. Arrieta, "Material de clase Analisis de regresión. Semana 17." 2023. Online.; Navoneel Chakrabarty, "Brain MRI Images for Brain Tumor Detec- tion." Recuperado de https://www.kaggle.com/datasets/navonee1/ brain-mri-images-for-brain-tumor-detection, 2019. Online.; J.0. Ramsay y B.W. Silverman, "Functional Data Analysis." Springer, vol. 322, no. 2, 2005. (7] A. Géron, "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow." O'REILLY, no. 2, 2019.; DE AMO, E., DÍAZ CARRILLO, M., DURANTE, F., FERNÁNDEZ SÁNCHEZ, J., Extensions of subcopulas, Journal of Mathematical Analysis and Applications 452 (1): 1 - 15, 2017; DURANTE, F. AND SEMPI, C., Principles of Copula Theory, CRC press, 2016; ERDELY, A., A subcopula based dependence measure, Kybernetika 53 (2), 231 - 243, 2017; JOE, H., Dependence Modeling with Copulas, CRC Press, 2014; NELSEN, R. B., An Introduction to Copulas, Springer, New York, 2006; Organización Mundial de la Salud y Banco Mundial: Informe mundial sobre la discapacidad. 11. https://www.oas.org(2011); Pardo, Campo Elías: Estadística descriptiva multivariada, Bogotá, Colombia: Universidad Nacional de Colombia. 2020; Pineda Duque, J. A., Luna Ruiz, A: Intersecciones de género y discapacidad. La inclusión laboral de mujeres con discapacidad, Sociedad y Economía, (35), 158-181. https://doi.org/ 10.25100/sye.v0i35.5652(2018); Universidad de los Andes: Encuesta Longitudinal Colombiana: Formularios Urbano adultos y Urbano Hogar, 2016.; D. Kosiorowski, J. P. Rydlewski, M. Snarska. (2017). Detecting a structural change in functional time series using local Wilcoxon statistic; D. Paindaveine & G. Van bever (2013). From Depth to Local Depth: A Focus on Centrality Journal of the American Statistical Association; S. Lopez-Pintado, R. Jornsten (2007). Functiodal analysis via extensions of the band depth J Stat Softw 29:5; AGRESTI, A. Y FINLAY, B. , Statistical Methods for the Social Sciences, 4th ed. Prentice Hall, 2009.; R CORE TEAM., R: A Language and Environment for Statistical Computing. Tech. rep. Vienna, Austria, 2020. url: https://www.R-project.org; SISTEMA NACIONAL DE INFORMACIÓN DE LA EDUCACIÓN SUPERIOR, SNIES. Minedu-cación. BOGOTA, 2000. url: https://www.mineducacion.gov.co/sistemasinfo/Informacion-Institucional/.; Azzalini, A., with the collaboration of A. Capitanio (2014). The Skew-Normal and Related Families. Cambridge University Press, Cambridge; Koenker, R., Bassett Jr., G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.; Mazucheli, J., Alves, B., Menezes, A. and Leiva, V. An overview on parametric quantile regression models and their computational implementation with applications to biomedical problems including COVID-19 data. Computer Methods and Programs in Biomedicine 2022, 221, 106816.; Muzamhindo, Yusheng Kong and Takur iramunashe Famba. (2017); PRINCIPAL COMPONENT ANALYSIS AS A RANKING TOOL - A CASE OF WORLD UNIVERSITIES. int J. of Adv. Res. 5 (Jun). 2114-2135 (ISSN 2320-5407). www.journalijar.com; Bracken J, van Assen MALM. An empirical Kaiser criterion. Psychol Methods. (2017);22(3):450-466. doi:10.1037/met0000074. Epub 2016 Mar 31. PMID: 27031883.; R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/; Andrzej Mackiewicz, Waldemar Ratajczak. Principal components analysis (PCA) (1993), Computers & Geosciences, Volume 19, Issue 3,1993, Pages 303-342, ISSN 0098-3004, https://doi.org/10.1016/0098-3004(93)90090-R.; Banco Mundial. Indicadores de Desarrollo Mundial, World Bank Group, 2015, data. worldbank.org; Khatun, Tahmina. (2009). Measuring environmental degradation by using principal component analysis. Environment, Development and Sustainability. 11. 439-457. 10.1007/s 10668- 007-9123-2.; PERSSON, RICKARD, Weight of evidence transformation in credit scoring models: How does it affect the discriminatory power? Student Paper, 2021.; ZENG, G., A necessary condition for a good binning algorithm in credit scoring, 2014.; Basu S, Santra S (2010). "A joint model for incomplete data in crossover trials." Journal of Statistical Planning and Inference, 140(10), 2839-2845.; Chard AN, Trinies V, Edmonds CJ, Sogore A, Freeman MC (2019). "The impact of water consumption on hydration and cognition among schoolchildren: Methods and results from a crossover trial in rural Mali." PloS one, 14(1), e0210568.; Cruz NA, Melo OO, Martinez CA (2023). "A correlation structure for the analysis of Gaussian and non-Gaussian responses in crossover experimental designs with repeated measures." Statistical Papers, pp. 1-28.; Davis CS (2002). Statistical Methods for the Analysis of Repeated Measurements. Springer, San Diego. [6] Fleiss JL (1989). "A critique of recent research on the two-treatment crossover design.* Controlled clinical trials, 10(3), 237-243.; Grayling MJ, Mander AP, Wason JM (2018). *Blinded and unblinded sample size reesti-mation in crossover trials balanced for period." Biometrical Journal, 60(5), 917-933.; Hardin JW, Hilbe J (2003). Generalized Estimating Equations, Chapman & Hall, Boca Raton.; Harville DA (1997). Matrix algebra from a statistician's perspective, volume I. Springer, New York.; Hin LY, Wang YG (2009). "Working-correlation-structure identification in generalized estimating equations." Statistics in medicine, 28(4), 642-658.; Hinkelmann K, Kempthorne O (2005). Design and Analysis of Experiments, volume Volume 2 of Wiley series in probability and mathematical statistics. Applied probability and statistics. Wiley, New York. ISBN 9780471551775,0-471-55177-5,0471551783.; Hojsgaard S, Halekoh U, Yan J (2006). "The R package geepack for generalized estimating equations." Journal of statistical software, 15, 1-11.; Jones B, Kenward MG (2015). Design and Analysis of Cross-Over Trials Third Edition. Chapman & Hall/CRC, Boca Raton.; Josephy H, Vansteelandt S, Vanderhasselt MA, Loeys T (2015). "Within-subject mediation analysis in AB/BA crossover designs." The international journal of biostatistics, 11(l), 1-22.; Kitchenham B, Madeyski L, Curtin F, et al. (2018). "Corrections to effect size variances for continuous outcomes of cross-over clinical trials." Statistics in medicine, 37(2), 320-323.; Kleinman K, Sakrejda A, Moyer J, Nugent J, Reich N (2021). clusterPower: Power Calculations for Cluster-Randomized and Cluster-Randomized Crossover Trials. R package version 0.7.0, URL ht tps:// CRAN.R-project.org/package=clusterPower.; Labes D (2019). randomizeBE: Create a Random List for Crossover Studies. R package version 0.3-5, URL https:// CRAN.R-project. org/package=randomizeBE; Liang KY, Zeger SL (1986). "Longitudinal data analysis using generalized linear models." Biometrika, 73(1), 13-22.; Madeyski L, Kitchenham B (2018). "Effect sizes and their variance for AB/BA crossover design studies." Empirical Software Engineering, 23(4), 1982-2017.; McDaniel LS, Henderson NC, Rathouz PJ (2013). "Fast pure R implementation of GEE: application of the Matrix package." The R Journal, 5, 181-187. URL https://journal! I-project.org/archive/2013-1/mcdaniel-henderson-rathouz.pdf; Pan W (2001). "Akaike's information criterion in generalized estimating equations." Bio-metrics, 57, 120-125.; Patterson HD (1951). "Change-Over Trials." Journal of the Royal Statistical Society. Series B (Methodological), 13, 256-271.; Rohmeyer K (2021). Crossover: Analysis and Search of Crossover Designs. R package version 0.1-20, URL attps://CRAN.R-projget.org/package-Crossover.; Rosenkranz GK (2015). "Analysis of cross-over studies with missing data." Statistical methods in medical research, 24(4), 420-433.; Sailer MO (2022), crossdes: Construction of Crossover Designs. R package version 1.1-2, URL https://CRAN.R-project.org/package=crossdes.; Senn S (1992). "Is the 'simple carry-over'model useful?" Statistics in Medicine, 11(6), 715-726.; Vegas S, Apa C, Juristo N (2016). "Crossover designs in software engineering experiments: Benefits and perils." IEEE Transactions on Software Engineering, 42(2), 120-135.; Wei T, Simko V (2021). R package 'corrplot: Visualization of a Correlation Matrix. (Version 0.92), URL https://github. com/taiyun/corrplot.; Zhang H, Yu Q, Feng C, Gunzler D, Wu P, Tu X (2012). "A new look at the difference between the GEE and the GLMM when modeling longitudinal count responses." Journal of Applied Statistics, 39(9), 2067-2079.; N. M. Laird and J. H. Ware, Random-Effects Models for Longitudinal Data, Biometrics, vol. 38, no. 4, pp. 963-974, 1982. URL: http://www.jstor.org/stable/2529876; B. A. Chvatal et al., Development of Multilevel Models for Longitudinal Craniofacial Growth Prediction, Am. J. Orthod. Dentofacial Orthop., vol. 128, no. 1, pp. 45-56, 2005. DOI:10.1016/j-ajodo.2004. 03.035; J. C. Correa and J. C. Salazar, Introducción a los Modelos Mixtos, 2016. URL: http: //www.bdigital.unal.edu.co/57330/; E. W. Frees, Longitudinal and Panel Data: Analysis and Applications in the Social Sciences, Cambridge University Press, 2004. New York.; A. Charpentier, Confidence Intervals and Credible Intervals, 2016. URL: http:// freakonometrics.hypotheses.org/18117 Consultado el 24 de julio de 2023.; L. D. Jiménez, L. Villegas, G. Álvarez, and J. C. Salazar-Uribe, Modeling Facial Growth Data in 49 Untreated Colombian Mestizo Subjects During 18 Years Follow-up Using Linear Mixed Models, Am. J. Orthod. Dentofacial Orthop., In Press, 2019.; R: A Language and Environment for Statistical Computing, R Core Team, R Foundation for Statistical Computing, 2023. URL: https://www.R-project.org/; AZZALINI, A. A class of distributions which includes the normal ones. Scandinavian Journal of Statistics, 1985, 12, 171-178.; GUO, B. AND WANG, B Control charts for monitoring the Weibull shape parameter based on type II censored sample. Quality and Reliability Engineering International, 2014; LI, C.; SU, N.C.; SU, P.F. AND SHYR, Y. The design of X and R control charts for skew-normal distributed data. Communications in Statistics-Theory and Methods, 2014, 43(23), 4908-4924.; MORALES, V.H. AND PANZA, C.A. Control charts for monitoring the mean of skew-normal samples. Symmetry, 2022, 14, 2302-2312.; SHEN, X.; ZOU, C.; JIANG, W. AND TSUNG, F. Monitoring Poisson count data with probability control limits when sample sizes are time varying. Naval Research Logistics, 2013, 60(8), 625-636.; CHOW S-C, SHAO J, WANG H, LOKHNYGINA Y., Sample size calculations in clinical research., In: Second. Chapman & Hall; 2008. p. 1-21; FRIEDMAN LM, FURBERG CD, DEMETS DL, REBOUSSIN DM, GRANGER CB., Fundamentals of clinical trials. In: Fundamentals of Clinical Trials. Spinger; 2015. p. 1-17.; KUMBHARE D, ALAVINIA M, FURLAN J., Hypothesis Testing in Superiority, Noninfe-riority, and Equivalence Clinical Trials. Am J Phys Med Rehabil. 2019;98(3):226-30. Interdisciplinary and Applied; CHARITOS T, DE WAAL PR Y VAN DER GAAG LC., Computing short-interval transition matrices of a discrete-time Markov chain from partially observed data. Stat Med. 2008 Mar 15;27(6): 905-21. doi:10.1002/sim.2970. PMID: 17579926.; ALEXANDRE JACQUILLAT, A Queuing Model of Airport Congestion and Policy Implications at JFK and EWR Massachusetts Institute of Technology. 2012 May 11.; HIGHAM, N.J. Y LIN, L., On pth Roots of Stochastic Matrices. Linear Algebra and its Applications, 435, 448-463,2011.; HIGHAM, N.J., Functions Of Matrices. Society for Industrial and Applied Mathemat-ics,2008.; MARCUS, M. AND MINC, H., ome Results on Doubly Stochastic Matrices. American Mathematical Society, 13(4), 571-579, 1962. doi: http://doi.org/10. 2307/2034828; Q1-MING H. Y GUNN E., Note on the Stochastic Roots of Stochastic Matrices. Jourmal of Systems Science and Systems Engineering, 12(2), 210-223, Jun 2003.; Sara López-Pintado and Juan Romo, A half-region depth for functional data, Computational Statistics & Data Analysis, vol. 55, no. 4, pp. 1679-1695, 2011, Elsevier.; James O. Ramsay and Bernard W. Silverman, Applied functional data analysis: methods and case studies, 2002, Springer.; Subhabrata Chakraborti and Marien Graham, Nonparametric statistical process control, 2019, John Wiley & Sons.; Partha Sarathi Mukherjee, On phase Il monitoring of the probability distributions of univariate continuous processes, Statistical Papers, vol, 57, no. 2, pp. 539-562, 2016. Springer.; Douglas C. Montgomery, Statistical quality control, vol. 7, 2009, Wiley New York.; Alessandro Fasso, Maurizio Toccu, and Marino Magno, Functional control charts and health monitoring of steam sterilizers, Quality and Reliability Engineering International, vol. 32, no. 6, pp. 2081-2091, 2016, Wiley Online Library.; Alba M Franco Pereira and Rosa E Lillo, Rank tests for functional data based on the epigraph, the hypograph and associated graphical representations, Advances in Data Analysis and Classification, vol. 14, no. 3, pp. 651-676, 2020, Springer.; Miguel Flores, Salvador Naya, Rubén Fernández-Casal, Sonia Zaragoza, Paula Raña, and Javier Tarrfo-Saavedra, Constructing a control chart using functional data, Mathematics, vol. 8, no. 1, p. 58, 2020, MDPI.; Piotr Kokoszka and Matthew Reimherr, Introduction to functional data analysis, 2017, CRC press.; Ana Arribas-Gil and Juan Romo, Shape outlier detection and visualization for functional data: the outliergram, Biostatistics, vol. 15, no. 4, pp. 603-619, 2014, Oxford University Press.; Sara López-Pintado and Juan Romo, On the concept of depth for functional data, Journal of the American statistical Association, vol. 104, no. 486, pp. 718-734, 2009, Taylor & Francis.; Christian Capezza, Fabio Centofanti, Antonio Lepore, and Biagio Palumbo, A functional data analysis approach for the monitoring of ship CO 2 emissions, Gestão & Produção, vol. 28, 2021, SciELO Brasil.; Marco Grasso, Bianca Maria Colosimo, and Fugee Tsung, A phase I multi-modelling ap- proach for profile monitoring of signal data, International Joumal of Production Research, vol. 55, no. 15, pp. 4354-4377, 2017, Taylor & Francis.; Koenker, R.; Bassett, G., Jr. Regression quantiles. Econometrica 1978, 46, 33-50.; McKeague, I.W.; López-Pintado, S.; Hallin, M.; Siman, M. Analyzing growth trajectories. J. Dev. Orig. Health Dis. 2011 2, 322-329.; Petrella, L.; Raponi, V. Joint estimation of conditional quantiles in multivariate linear regression models with an application to financial distress. J. Multivar. Anal. 2019, 173, 70-84.; BARÓN, J. & MARTÍNEZ, G., Probabilistic Sampling Design and Strategies. Package ProbSamplingI, software R-project, 2021.; SARNDAL, C., SWENSSON, B. & WRETMAN, J. Model Assisted Survey Sampling, 2003.; Calderón, S. (2023). TimeSeries. GitHub. de URL: https: //github.con/sacalderonv/ Time Series; Martinez C. (2017) Suavizacion Exponencial Simple - Pronostico de la Demanda en Excel de https://excelcartagena. com.; SciPy developers. (Agosto, 2023). SciPy Reference Guide. URL: https://docs. scipy org/doc/scipy/; Statsmodels developers. (AMayo, 2023). User Guide. URL: https://www.statsmodels org/stable/user-guide.html; BARDWELL, G. E. AND CROW, E. L., A two-parameter family of hyper-poisson distribu-tions. Journal of the American Statistical Association, 59(305): 133-141, (1964).; BENJAMIN, M. A., RIGBY, R. A., AND STASINOPOULOS, D. M., Generalized au-toregressive moving average models. Journal of the American Statistical Association, 98(461):214-223, (2003).; JOHNSON, N., KEMP, A., AND KOTZ, S. Univariate Discrete Distributions. Wiley Series in Probability and Statistics. Wiley, (2005).; R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, (2022).; SÁEZ-CASTILLO, A. AND CONDE-SÁNCHEZ, A. A hyper-poisson regression model for overdispersed and underdispersed count data. Computational Statistics & Data Analysis, 61:148-157, (2013).; R, r-project.org, R version 4.2.3, 2023.; ZEITLIN, W. AND ES AUERBACH, C., Basic statistics for the behavioral and social sciences using R., Oxford University Press, 2019.; GRAJALES-MORALES S., https://github.com/SaraGraMo/DaneFinancEducat,2023.; Harrou, F., Sun, Y., Hering, A. S., & Madakyaru, M. Statistical process monitoring using advanced data-driven and deep learning approaches: theory and practical applications. Elsevier.2020; Nurettin Dorukhan Sergin & Hao Yan Toward a better monitoring statistic for profile monitoring via variational autoencoders, Joural of Quality Technology, 53:5, 454-473,2021; Lee, S., Kwak, M., Tsui, K. L., & Kim, S. B. Process monitoring using variational au-toencoder for high-dimensional nonlinear processes. Engineering Applications of Artificial Intelligence, 83, 13-27.2019; BRIAN EVERITT, & HOTHORN, T., An Introduction to Applied Multivariate Analysis with R,Springer, 2011 ed.; CATTELL, R. B. The scree test for the number of factors, "Multivariate Behavioural Re-search, 1, 245-276. Cited on p. 72,1966.; KAISER, H., The varimax criterion for analytic rotation in factor analysis, " Psychometrika, 23, 187-200. Cited on p. 72, 146, 1958.; HAN, J., PEL, J., & KAMBER, M., Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) (English Edition), 3.a ed, Morgan Kaufmann, 2011.; HOERL, ARTHUR E AND KENNARD, ROBERT W, Ridge regression: Biased estimation for nonorthogonal problems. Technometries, Taylor & Francis, 1970.; GOLUB, G.H. AND REINSCH, Singular value decomposition and least square solutions Numer. Math, Marcel Dekker, Inc., 1970.; HOERL, A.E., Applications of ridge analysis to regression problems, Chem. Eng. Progress, 1962.; BELSLEY, DA, E, Kuh, RE Welsch, Regression Diagnostics. New York: Wiley, 1980.; R. Ferland, A. Latour, D. Oraichi, Integer-Valued GARCH Process, Journal of Time Series Analysis, vol. 27, no. 6, pp. 923-942, 2006. https: //doi. org/10.1111/j. 1467-9892 2006. 00496.20; K. Fokianos, A. Rahbek, D. Tjestheim, Poisson Autoregression, Journal of the American Statistical Association, vol. 104, no. 488, pp. 1430-1439, 2009. http://www.jstor.org/ stable/ 40592351; K. Fokianos, D. Tjostheim, Log-linear Poisson autoregression, Journal of Multivariate Analysis, vol. 102, no. 3, pp. 563-578, 2011. https://doi.org/10.1016/j. juva. 2010. 11.002; K. Fokianos, Count Time Series Models, Time Series Analysis: Methods and Applications, 2012. http://dx.doi. org/10.1016/B978-0-444-53858-1. 00012-0; A. Zhang, Z. C. Lipton, M. Li, A. J. Smola, Dive into Deep Learning, arXiv preprint arXiv:2106.11342, 2021. [6] S. Ruder, An overview of gradient descent optimization algorithms, CORR, vol. abs/1609.04747, 2016. http://arxiv.org/abs/1609.04747; R. J. Hyndman, G. Athanasopoulus, Forecasting: Principles and Practice, Monash Univer-sity, Australia, 2018; T. Liboschik, K. Fokianos, R. Fried, scount: An R Package for Analysis of Count Time Series Following Generalized Linear Models, Journal of Statistical Software, vol. 82, no. 5, pp. 1-51, 2017. https://www.jstatsoft.org/index.php/jss/article/view/v082i05 https://doi.org/10.18637/jss.v082. 105; BERRENDERO, J. R., JUSTEL, A., & SVARC, M., Principal components for multivariate functional data. Computational Statistics & Data Analysis., Recuperado de https: // repositorio.uam.es/bitstream/handle/10486, 2011.; YAMAMOTO, M, Clustering of functional data in a low-dimensional subspace Advances in Data Analysis and Classification. Recuperado de https://link. springer) com/article/10.1007/s11634-012-0113-3, 2012.; Medellincomovamos (2021), 2020 Pico de probreza en Medellin. https://www.medellincomovamos.org/2020-pico-de-la-pobreza-en-medellin; Breadfortheworld, https://www.bread.org/es/que-causa-el-hambre; Alcaldia de Medellin, Encuesta de calidad de vida 2020. https://www.medellin.gov.co/irj/portal/medellin?NavigationTarget=contenido/9946- Encuesta-de-Calidad-de-Vida-2020; Kass, R. E., Gilks, W. R., Richardson, S., Spiege lhalter, D. J. (1997), Markov Chain Monte Carlo in Practice. Journal of the American Statistical Association. https://doi.org/10.2307/2965438; Chang, M., Dalpatadu, R. J., Phanord, D., Singh, A. K., Harrah, W. F., Administration, H. (2018), Breast Cancer Prediction Using Bayesian Logistic Regression. https://doi.org/10.31031/OABB.2018.02.000537; Box G E P and Tiao G C. (1992), Bayesian Inference in Statistical Analysis. Canada: John Wiley Sons, Inc; R package version 3-13, Bayesian graphical models using MCMC. http://cran.r-project.org/package=rjags; ARAGÓN-MORENO, J. A., Y LERMA-LERMA, B. D., Analysis temporary space (1981-2010) of the precipitation in the city of Bogota: advances in the generation of extreme indices. Revista Facultad de Ingenieria, 28(57), 57-71. (2019).; SOCHA, L. M., Análisis tendencial de la variación climática " temperatura y precipitación" espacio-temporal del departamento de Boyacá, (2014); WIKLE, C. K., ZAMMIT-MANGION, A., AND CRESSIE, N., Spatio-Temporal Statistics with R, Boca Raton (2019); MATEU, J. GIRALDO, R, Geostatistical Functional Data Analysis (2021); Yuanjun Guo, Zhile Yang, Shengzhong Feng, Jinxing Hu, Complex power system status monitoring and evaluation using big data platform and machine learning algorithms: a review and a case study, Complexity, vol. 2018, 2018, Hindawi.; Mohamed E. El-Hawary, Electrical Power Systems: Design and Analysis, vol. 7, 1995, TEBE Press.; Iván Matulic, Introducción a Los Sistemas eléctricos de potencia, Acta Nova, Universidad Católica Boliviana, 2005, http://www.scielo.org.bo/scielo.php?script-sci arttext&: pid=51683-07892003000100005.; Comisión de Regulación de Energía y Gas, Resolución CREG 025-95, Codigo de Redes, 1995.; Walter Andrew Shewhart, Economic quality control of manufactured product 7, Bell System Technical Joumal, vol. 9, no. 2, 1930, Wiley Online Library.; Ewan S Page, Continuous inspection schemes, Biometrika, vol. 41, no. 1/2, 1954, JSTOR.; SW Roberts, Control chart tests based on geometric moving averages, Technometrics, vol. 42, no. 1, 2000, Taylor & Francis.; JA Vargas, Control estadístico de calidad, Universidad Nacional de Colombia, Bogotá, Colombia, 2006.; S. Calderón, Notas de clase Series de tiempo, Universidad Nacional de Colombia, 2022.; MARTINEZ-FLÓREZ, G., MORENO-ARENAS, G. Y VERGARA-CARDOZO, S. Properties and inference for proportional hazard models. Revista Colombiana de Estadistica, 2013, Vol. 36, 95-114.; MARTÍNEZ-FLÓREZ, G., GÓMEZ, H. W. Y TOVAR-FALÓN, R. Modeling Proportion Data with Inflation by Using a Power-Skew-Normal/Logit Mixture Model. Mathematics 2021, 9, 1989.; AGRESTI, A. (2002). Categorical Data Analysis. John Wiley and Sons. New Jersey.; CHRISTENSEN, R. (1997). Log-Linear Models and Logistic Regression. Springer-Verlag. New York.; MARTÍNEZ-FLÓREZ, G., MORENO-ARENAS, G. Y VERGARA-CARDOZO, S. (2013). Properties and inference for proportional hazard models. Revista Colombiana de Estadística. Vol. 36, 95-114.; PAOLINO, P. Maximum likelihood estimation of models with beta-distributed dependent variables. Political Anal. 2001, 9, 325-346.; CRIBARI-NETO, F. Y VASCONCELLOS, K. L. P. Nearly unbiased maximum likelhood estimation for the beta distribution. J. Stat. Comput. Simul. 2002, 72, 107-118.; KIESCHNICK, R. Y MCCULLOUGH, B. D. Regression analysis of variates observed on (O, 1). Stat. Model. 2003, 3, 193-213.; FERRARI, S.; CRIBARI-NETO, F. Beta regression for modelling rates and proportions. J. Appl. Stat. 2004, 31, 799-815.; VASCONCELLOS, K. L.P.: CRIBARI-NETO, F. Improved maximum likelihood estimation in a new class of beta regression models. Braz. J. Probab. Stat 2005, 19, 13-31.; MARTÍNEZ-FLÓREZ, G., BOLFARINE, H. Y GÓMEZ, H. W. Skew-normal Alpha-Power Model. 2013 Statistics-doi 10.1080/02331888.2013.826659.; CRAGG, J. Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica 1971, 39, 829-844.; MOULTON, L. Y HALSEY, N. A Mixture Model with Detection Limits for Regression Analysses of Antibody Response to Vaccine. Biometrics, 1995, 51, 1550-1578.; MARTÍNEZ-FLÓREZ, G., GOMEZ, H. W. Y TOVAR-FALON, R. Modeling Proportion Data with Inflation by Using a Power-Skew-Normal/Logit Mixture Model, Mathematics, 2021, 9, 1989.; EL-MORSHEDY, M., ELIWA, M. S., & ALTUN, E., Discrete Burr-Hatke Distribution With Properties, Estimation Methods and Regression Model, IBEE Access, 2020.; RIGBY, R.A. AND STASINOPOULOS, D.M., Generalized Additive Models for Location, Scale and Shape, Journal of the Royal Statistical Society: Series C (Applied Statistics), 2005.; Multivariate functional random fields: prediction and optimal sampling. Stochastic Environmental Research and Risk Assessment. 2017 * Bohorquez, M., & Sandoval D., Villamil A., Guevara R., Sanchez S., Vergel N., Bejarano; V., Castro J., Muñoz M., Giraldo R., Mateu J. SpatFD (Version 0.0.1). R cran package. https://github.com/mpbohorquezc/GeostatFunct. (2023).; Villamizar, S., Lopez, O., Collazos, A. C., Sarmiento, J., and Rodriguez, J. B. Recognition of EEG signals from imagined vowels using deep leaming methods. Sensors. 2021.; C. DAVIDSON. QUICKSTART, 2020., URL https://github.com/CamDavidsonPilon/lifetimes/blob/master/docs/Quickstart.md.; P. FADER AND B. HARDIE., The gamma-gamma model of monetary value., 2013.; P. FADER, B. HARDIE, AND K. LEE., Counting your customers, the easy way: An alternative to the pareto/nbd model. 24:275-284, 2005.; D. MCCARTHY AND E. WADSWORTH. BUY *TIL YOU DIE - A WALKTHROUGH, 2014., URL https://cran. r-project.org/web/packages/BTYD/vignettes/BTYDwalkthrough.pdf; H. ONNEN., Buy 'til you die: Predict customer lifetime value in python, 2021. URL https://towardsdatascience.com/buy-til-you-die-predict-customer-lifetime-value-in-python-9701bfd4ddc0.; D. SCHMITTLEIN, D. MORRISON, AND R. COLOMBO., Counting your customers: Who are they and what will they do next? 33: 1-24, 1987.; Leung, A., Zhang, H., & Zamar, R. H. (2016). Robust Regression Estimation and Inference in the Presence of Cellwise and Casewise Contamination. Computational Statistics & Data Analysis, 99, 1-11.; Falk, M. (1997). On Mad and Comedians. Annals of the Institute of Statistical Mathematics, 49(4), 615-644. [3] Kunjunni, S. O., & Sajesh, T. A. (2020). S, Covariance. Communications in Statistics - Theory and Methods, 49(24), 6133-6138.; Tarr, G., Muller, S., & Weber, N. (2012). A Robust Scale Estimator Based on Pairwise Means. Journal of Nonparametric Statistics, 24(1), 187-199. [5] Rousseeuw, P. J., & Croux, C. (1993). Alternatives to the Median Absolute Deviation. Journal of the American Statistical Association, 88(424), 1273-1283.; Pinzón, M. A., Ortiz, S., Holguín, H., Betancur, J. F., Cardona Arango, D., Laniado, H., . Quiceno, J. (2021). Dexamethasone vs Methylprednisolone High Dose for Covid-19 Pneumonia. PLOS ONE, 16(5), 1-13.; Jobe, J. M., & Pokojovy, M. (2015). A Cluster-Based Outlier Detection Scheme for Multivariate Data. Journal of the American Statistical Association, 110(512), 1543-1551.; Ro, K., Zou, C., Wang, Z., & Yin, G. (2015). Outlier Detection for High-Dimensional Data. Biometrika, 102(3), 589-599.; Hubert, M., Rousseeuw, P. J., Vanpaemel, D., & Verdonck, T. (2015). The DetS and DetMM Estimators for Multivariate Location and Scatter. Computational Statistics & Data Analysis, 87, 64-75.; Boudt, K., Rousseeuw, P. J., Vanduffel, S., & Verdonck, T. (2020). The Minimum Regularized Covariance Determinant Estimator. Statistics and Computing, 30, 113-128.; Kandanaarachchi, S., & Hyndman, R. J. (2021). Dimension Reduction for Outlier Detection Using DOBIN, Journal of Computational and Graphical Statistics, 30(1); Navarro-Esteban, P., & Cuesta-Albertos, J. A. (2021). High-Dimensional Outlier Detection Using Random Projections.; Pena, D., &e Prieto, F: 1. (2001), Multivariate Outlier Detection and Robust Covariance Matrix Estimation. Technometrics, 43(3), 286-300.; Maronna, R. A., & Zamar, R. H. (2002). Robust Estimates of Location and Dispersion for High-Dimensional Datasets. Technometrics, 44(4), 307-317.; Peña, D., & Prieto, F. J. (2007). Combining Random and Specific Directions for Outlier Detection and Robust Estimation in High-Dimensional Multivariate Data. Journal of Computational & Graphical Statistics, 16(1), 228-254.; Rousseeuw, P. J., & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.; Donoho, D. (1982). Breakdown Properties of Multivariate Location Estimators. Harvard University, Boston.; Rousseeuw, P. J., & Leroy, A. M. (1987). Robust Regression and Outlier Detection. Wiley.; Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.; Stahel, W. A. (1981). Robuste Schatzungen: Infinitesimale Optimalitat und Schatzungen von Kovarianzmatrizen. ETH, Zurich, Switzerland.; Sajesh, T. A., & Srinivasan, M. R. (2012). Outlier Detection for High Dimensional Data Using the Comedian Approach. Journal of Statistical Computation and Simulation, 82(5), 745-757.; Atkinson, A. C. (1994). Fast Very Robust Methods for the Detection of Multiple Outliers. Journal of the American Statistical Association, 89(428), 1329-1339.; Filzmoser, P. (2005). Identification of Multivariate Outliers: A Performance Study. Austrian Journal of Statistics, 34(2), 127-138.; Hubert, Mia, & Van der Veeken, Stephan (2008). Outlier Detection for Skewed Data. Journal of Chemometrics, 22(3-4), 235-246.; Magnotti, John F., & Billor, Nedret (2014). Finding Multivariate Outliers in fMRI Time-Series Data. Computers in Biology and Medicine, 53, 115-124.; Rocke, David M. (1989). Robust Control Charts. Technometrics, 31(2), 173-184.; Davis, Cali Manning, & Adams, Benjamin M. (2005). Robust Monitoring of Contaminated Data. Journal of Quality Technology, 37(2), 163-174.; Galeano, P., Peña, D., & Tsay, R. (2006). Outlier Detection in Multivariate Time Series by Projection Pursuit. Journal of the American Statistical Association, 101(474), 654-669.; Maronna, Ricardo A., Martin, R. Douglas, & Yohai, Victor J. (2006). Robust Statistics: Theory and Methods. Wiley.; Rocke, David M., & Woodruff, David L. (1996). Identification of Outliers in Multivariate Data. Journal of the American Statistical Association, 91(435), 1047-1061.; Brys, G., Hubert, M., & Struyf, A. (2004). A Robust Mfasure of Skewness. Journal of Computational and Graphical Statistics, 13(4), 996-1017.; Olive, D. J. (2004). A Resistant Estimator of Multivariate Location and Dispersion. Computational Statistics & Data Analysis, 46(I), 93-102.; A gostinelli, C., Leung, A., Yohai, V. J., & Zamar, R. H. (2015). Robust Estimation of Multivariate Location and Scatter in the Presence of Cellwise and Casewise Contamination. TEST, 24(3), 441-461.; Cuesta-Albertos, J. A., & Nieto-Reyes, A. (2008). The Random Tukey Depth. Computational Statistics & Data Analysis, 52(11), 4979-4988.; Fraiman, Ricardo, Meloche, Jean, García, Luis A., Gordaliza, Alfonso, He, Xuming, Maronna, Ricardo,. Wood, Andrew (1999). Multivariate L-estimation. TEST, 8(2), 255-317.; Liu, Regina Y., & Parelius, Jesse M., & Singh, Kesar (1990). On a Notion of Data Depth Based on Random Simplices. The Annals of Statistics, 18(1), 405-414; Liu, Regina Y., Parelius, Jesse M., & Singh, Kesar (1999). Multivariate Analysis by Data Depth: Descriptive Statistics, Graphics and Inference. The Annals of Statistics, 27(3), 783-858.; Zuo, Yijun, & Serfling, Robert (2000). General Notions of Statistical Depth Function. The Annals of Statistics, 28(2), 461-482.; Cabana, Elisa, Lillo, Rosa E., & Laniado, Henry (2019). Multivariate Outlier Detection Based on a Robust Mahalanobis Distance with Shrinkage Estimators. Statistical Papers, 60(2), 199-210.; Hubert, M., & Vandervieren, E. (2008). An Adjusted Boxplot for Skewed Distributions. Computational Statistics & Data Analysis, 52(12), 5186-5201.; Filzmoser, P., Maronna, R., & Werner, M. (2008). Outlier Identification in High Dimensions. Computational Statistics & Data Analysis, 52(3), 1694-1711.; Loperfido, N. (2018). Skewness-Based Projection Pursuit: A Computational Approach. Computational Statistics & Data Analysis, 120, 42-57.; Ortiz, S. (2019). Multivariate Outlier Detection and Robust Estimation Using Skewness and Projections. Master's thesis, Universidad EAFIT, Medellín, Colombia.; Maronna, Ricardo A., & Yohai, Víctor J. (1995). The Behavior of the Stahel-Donoho Robust Multivariate Estimator. Journal of the American Statistical Association, 90(429), 330-341.; Van Aelst, S., Vandervieren, E., & Willems, G. (2012). A Stahel-Donoho Estimator Based on Huberized Outlyingness. Computational Statistics & Data Analysis, 56(3), 531-542.; Gervini, Daniel (2002). The Influence Function of the Stahel-Donoho Estimator of Multivariate Location and Scatter. Statistics & Probability Letteks, 60(4), 425-435.; Jones, M. C., & Sibson, R. (1987). What is Projection Pursuit? Journal of the Royal Statistical Society: Series A (General), 750(L), 1-18.; Algallaf, F., Van Aelst, S., Yohai, V. J., & Zamar, R. H. (2009). Propagation of Outliers in Multivariate Data. The Annals of Statistics, 37(1), 311-331.; R Core Team (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.; Van Aelst, S. (2016). Stahel-Donoho Estimation for High-Dimensional Data. International Journal of Computer Mathematics, 93(4), 628-639.; Juan, J., & Prieto, F. J. (1995). A Subsampling Method for the Computation of Multivariate Estimators with High Breakdown Point. Journal of Computational & Graphical Statistics, 4(4), 319-334.; BANCOLDEX, Bancoldex,30 de 07 de 2018. Obtenido de Bancoldex: attps: //www.bancoldex.com/sabe-que-es-el-sistema-financiero-colombiano-1630.; COLOMBIA COMPETITIVA, Índice de Competitividad Global -ICG (Global Competitiveness Index Obtenido de http://www.colombiacompetd-global, 2019.; R. C. TEAM, «R: A language and environment for statistical computing,» 2016. [En Inea]; Torres, R., Montes E., Pérez, O & Andrade, R. (2012). Influencia del estado de madurez. sobre las propiedades visco elásticas de frutas tropicales (mango, papaya y plátano), Información Tecnológica, 23(5), 115-124.; Organización Mundial de la Salud (OMS). (2015). Plan de acción mundial sobre la resistencia a los antimicrobianos [Internet]. 68.* Asamblea Mundial de la Salud; del 18 al 26 de mayo del 2015; Ginebra. Disponible en: https://apps.who.int/gb/ebwha/pdf_files/WHA68/A68_20-sp.pdf; Organización Mundial de la Salud (OMS). (2019). Antimicrobianos de importancia crítica para la medicina humana, 6.* revisión [Critically important antimicrobials for human medicine, 6th revisión]. Ginebra. Licencia: CC BY-NC-SA 3.0 IGO.; Organización Mundial de la Salud (OMS), Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO) y Organización Mundial de Sanidad Animal (OIE). (2021). Encuesta tripartita de autoevaluación nacional sobre la RAM (TrACSS) 2020-2021. Disponible en: https://www.who.int/es/publications/m/item/ tripartite-amr-country-solf-assessnent-supy®y-(traces)-2020-2021; Pagès, J. (2004) Analyse Factorielle de Données Mix https://www.who.int/es/publications/m/ite 52, 93-111 m/tripartite-amr-country-self-assessment-survey-(tracss)-2020-2021; R Core Team (2022). R: A language and environment Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/; World Organisation for Animal Health (OIE). (2021). OIE Annual Report on Antimicrobial Agents Intended for Use in Animals. better understanding of the global situation. Fifth report.; ABONAZEL, M.R., EL-SAYED, S. M., Y SABER, O. M, , Performance of robust count regression estimators in the case of overdispersion, zero inflated, and outliers: simulation study and application to German health data, Communications in mathematical biology and neuroscience, Article ID 55, 2021, https://doi.org/10.28919/cmbn/5658.; ASWI, A., ASTUTI, S. A., Y SUDARMIN, S., Evaluating the performance of zero-inflated and hurdle Poisson models for modeling overdispersion in count data. Inferensi, Vol. 5 num. 1, 2022, https://doi.org/10.12962/j27213862.v511.12422.; CAMPBELL, H., The consequences of checking for zero-inflation and overdispersion in the analysis of count data, Methods in Ecology and Evolution, Vol. 12 num. 4, pp. 665-680, 2021, https://doi.org/10.1111/2041-210x.13559.; HILBE, J. M., Negative Binomial Regression (2a ed.), Cambridge University Press, 2012.; HILBE, J. M. ,Modeling Count Data, Cambridge University Press, 2014.; ZUUR, A. F., IENO, E. N., WALKER, N., SAVELIEV, A. A., Y SMITH, G. M, Negative Binomial Regression (2a ed.) Mixed effects models and extensions in ecology with R, Springer, 2011.; Bos, M. (2016.), educación en America Latina y el Caribe: Diagnostico y Perspectiva. BID-OCDE.; Costa M. Domingos A.(2019) promover o ensino da matematica num contexto de formacao profissional com STEM. Educacion Matematica, vol 31,num 1.; WISE (2019). Emprendedoras STEM en América latina. BID-LAB-IAE. [4] Mody, C. (2015). Scientific practice and science education. Science Education, 99(6), 1026-1032.; R Core Team. (2021). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org; Vertel et al. (2021). Grupo de Estadística y Modelamiento Matemático aplicado a calidad educativa adscrito a la universidad de Sucre.; Torres, R., Montes E., Pérez, O & Andrade, R. (2012). Influencia del estado de madurez: sobre las propiedades visco-elásticas de frutas tropicales (mango, papaya y plátano), Información Tecnológica, 23(5), 115-124.; Pardo, C. & Del Campo, P. (2007). Combinación de métodos factoriales y de análisis de conglomerados en R: el paquete FactoClass. Rev. Colombiana de Estadística, 30 (2), 231-245.; R Development Core Team. (2020). R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Available at: http://www.R-project.org; Chessel, D., Dufour, A., & Thioulouse, J. (2004), The ade4 Package-I: One table Methods, RNews 4(1), 5-10.; Boyd, L. R., & Muggia, F. M. (2018). Carboplatin/Paclitaxel Induction in Ovarian Cancer: The Finer Points. Oncology (Williston Park, N.Y.), 32(8), 418-420, 422-424.; Cobo, M. J., López Herrera, A. G., Herrera Viedma, E., & Herrera, F. (2012). SciMAT: A new science mapping analysis software tool. Journal of the American Society for Information Science and Technology, 63(8), 1609-1630. https://doi.org/10.1002/asi.22688; Damia, G., & Broggini, M. (2019). Platinum Resistance in Ovarian Cancer: Role of DNA Repair. Cancers, 11(1), 119. https://doi.org/10.3390/cancers11010119; Norouzibarough, L., Sarookhani, M. R., & Sharifi, M. (2017). Molecular Mechanisms of Drug Resistance in Ovarian Cancer. Journal of Cellular Physiology, 233(6), 4546-4562. https://doi.org/10.1002/j cp.26289; HARRISON, N., CHUDRY, F., WALLER, R. Y HATT, S., Towards a Typology of Debt Attitudes among Contemporary Young uk Undergraduates. Journal of Further and Higher Education, Vol 39(1), 85-107, 2015.; HAULTAIN, S., KEMP, S. Y CHERNISHENCKO, O., The structure of attitudes to student debt. Journal of Economic Psychology, Vol 31, 322-330, 2010.; ] KAUR, J. Y ARORA, S., Indian students' attitude toward educational debt: scale development and validation. Quality Assurance in Education, Vol 27(4), 361-383, 2019.; PARK, T., YUSUF, A.A. Y HADSALL, R.S. , Pharmacy students' attitudes toward debt. American Journal of Pharmaceutical Education, Vol 79(4), 52, 2015.; CEPEDA-CUERVO, E., Modelagem da Variabilidade em Modelos Lineares Generalizados. Unpublished Ph.D. thesis, Mathematics Institute, Universidade Federal do Rio de Janeiro, 2001.; CEPEDA-CUERVO E., jo? beta and uo? Beta-Binomial Regression Models. Revista Colombiana de Estadística, 46(1), 63-79, 2023.; CEPEDA-CUERVO E., Beta regression models: joint mean and variance modeling. Journal of Statistical Theory and Practice 9, 134-145, 2015.; PAOLINO, P., Maximum likelihood estimation of models with beta-distributed dependent variables. Political Analysis, 9(4), 325-346, 2001.; S. FERRARI AND F. CRIBARI-NETO., Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31 (7) 799-815, 2004.; QUINE, S., Achievement orientation of aboriginal and white Australian adolescents, Ph.d. thesis, Australian National University, Australia.; OKUBO, A. Y LEVIN, S. A., Diffusion and Ecological Problems, Modern Perspectives. Interdisciplinary and Applied Mathematics, Springer, 2001.; QUINTERO-SARMIENTO, A., AND CEPEDA-CUERVO, E., AND NUNEZ-ANTON, V. , Estimating infant mortality in Colombia: some overdispersion modelling approaches. Joumal of Applied Statistics, 39(5), 1011-1036, 2012.; H. Zhou, L. Li, and H. Zhu, Tensor Regression with Applications in Neuroimaging Data Analysis, J. Amer. Statist. Assoc. Vol. 108 502 (2013), pp. 540-552.; J. Bobb, C. Crainiceanu, B. Caffo, D. Reich and J. Goldsmith, Penalized Funcitonal Regression, J. Comput. Graph. Statist. Vol. 20 4 (201 1), pp. 830-851.; A. Chambolle, An algorithm for total variation minimization and application, Comput. Imaging Vision Vol. 20 1 (2014), pp. 89-97.; C. LI, W. Yin, H. Jiang and Y. Zhang, An Efficient Augmented Lagrangian Method with Applications to Total Variation Minimization, Comput. Optim. Appl. Vol. 56 3 (2013), pp. 507-530.; C. LI, W. Yin, H. Jiang and Y. Zhang, An Alternating Direction Method for Total Variation Denoising, Optim. Methods Softw. Vol. 30 3 (2015), pp. 594-615.; Y. Wang, J. Yang, W. Yin, and Y. Zhang, A new alternating minimization algorithm for totalvariation image reconstruction, SIAM J. Imaging Sci. Vol. 13 (2018), pp. 248-272.; C. Chen, L. He, H. Li, and J. Huang, Fast Iteratively Reweighted Least Squares Algorithms for Analysis-Based Sparsity Reconstruction, Med. Image Anal. Vol. 49 1 (2018), pp. 141-151.; S. Toshi, Applied Matrix and Tensor Variate Data Analysis, Springer, Tokyo, 2016. [9] PT. Reiss, and RT. Ogden, Fuctional Principal Component Regression and Functional Partial Least Squares, J. Amer. Statist. Assoc. Vol. 49 1 (2018), pp. 984-996.; PT. Reiss and RT. Ogden, Fuctional generalized Linear Models with Images as Predictors, Biometrics Vol. 66 1 (2010), pp. 61-69.; K.J. Worsley, Developments in Random Field Theory, Human Brain Function: Second Edition Vol. 2 (2004).; N. Lazar, The Statistical Analysis of Functional MRI Data, Springer, New York, 2008.; Kokoszka P. and M. Reimher, Introduction to Functional Data Analysis, Chapman and Hall-CRC, New York, 2017.; L. Horváth and P. Kokoszka, Inference for Functional Data with Applications, Springer, New York, 2012.; X. Wang and H. Zhu, Generalized Scalar-on-Image Regression Models via Total Variation, J. Amer. Statist. Assoc. Vol. 112 519 (2017), pp. 1156-1168.; H. Zhou, Matlab Tensor Reg Toolbox Version 1.0, Available online. Software available at https://hua-zhou.github.io/TensorReg/.; J. Zeng, W. Wang X. and Zhang, TRES: An R Package for Tensor Regression and Envelope Algorithms, Available online. Software available at https://cran.r-project.org/web/packages/TRES/TRES.; I.A. Eckley and G.P. Nason, LS2W: Implementing the Locally Stationary 2D Wavelet Process Approach in R, J. Stat. Softw. Vol. 43 3 (2011), pp. 1-23.; G. Nason, Wavelet Methods in Statistics with R, Springer, New York, 2011.; J. A. Clarkson and C. R. Adams, On definitions of bounded variation for functions of two variables, Transactions of the American Mathematical Society Vol. 35 4 (1933), pp. 824-854; P. J. Basser and S. Pajevic, A normal distribution for tensor valued random variables: applications to diffusion tensor MRI, IBEE transactions on medical imaging vol. 22 7 (2003), pp. 785-794.; M. Ohlson and M. R. Ahmad and D. Von Rosen, The multilinear normal distribution: Introduction and some basic properties, Joumal of Multivariate Analysis, vol 113 (2013), pp 37-47.; ] M. Ohlson and M. R. Ahmad and D. Von Rosen, Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition, PLoS One, vol 10 (2015).; L. laliang and G. Ming and D. Ralph, Evaluatin' classification accuracy for modern learning approaches, Statistics in medicine, vol 38 13 (2019), pp 2477-2503.; S. SumaiyaZ and S. Loraine and L. Jialiang and D. Ralph, Statistical Learning in Medical Research with Decision Threshold and Accuracy Evaluatio, Journal of Data Science, vol 19 4 (2021); P. Margaret Sullivan The statistical evaluation of medical tests for classification and prediction, Oxford University Press, USA, 2003.; H. David J and A, Christoforos, When is the area under the receiver operating characteristic curve an appropriate measure of classifier performance?, Patter Recognition Letters, vol 34 5 (2013), pp 492-495.; T. Alaa, Classification assessment methods, Pattern Recognition Letters, vol 17 1 (2021), pp 168-192.; N. Christos ROC Analysis for Classification and Prediction in Practice, CRC Press, 2023.; D. Yang and C. Martinez and L. Visuña and H. Khandhar and C. Bhatt and J. Carretero, Detection and analysis of COVID-19 in medical images using deep learning techniques, Scientific Reports, vol 11 5 (2021), pp 19638; M. Puttagunta and S. Ravi, Medical image analysis based on deep leaming approach, Multimedia tools and applications, vol 80 (2021), pp 24365-24398.; Branscum, A. J., Johnson, W. O., & Thurmond, M. C. (2007), Bayesian beta regression: applications to household expenditure data and genetic distance between foot-and-mouth disease viruses. Australian & New Zealand Journal of Statistics, 49(3), 287-301.; Diaz, P. R. (2018). Regresión con proyecciones aleatorias para datos funcionales (Doctoral dissertation, Uniandes).; Espinheira, P. L., Ferrari, S. L., & Cribari-Neto, F. (2008). On beta regression residuals. Journal of Applied Statistics, 35(4), 407-419.; Ferrari, S., & Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of applied statistics, 31(7), 799-815.; Galvis, D. M., Bandyopadhyay, D., & Lachos, V. H. (2014). Augmented mixed beta regression models for periodontal proportion data. Statistics in medicine, 33(21), 3759-3771.; Sentürk, D., Dalrymple, L. S., & Nguyen, D. V. (2014). Functional linear models for zero-inflated count data with application to modeling hospitalizations in patients on dialysis. Statistics in medicine, 33(27), 4825-4840.; Wang, J. L., Chiou, J. M., & Muller, H. G. (2016). Functional data analysis. Annual Review of Statistics and its application, 3, 257-295.; YS Amirkhalili, A Aghsami, and F Jolai. Comparison of Time Series ARIMA Model and Support Vector Regression. In: International Journal of Hybrid Information Technology, 13.1 (2020), pp. 7-18.; Yukun Bao, Tao Xiong, and Zhongyi Hu. Multi-step-ahead time series prediction using multiple-output support vector regression. In: Neurocomputing, 129 (2014), pp. 482-493.; Barreto, H. (2012). El progreso de la Estadística y su utilidad en la evaluación del desarrollo. Papeles de Población, 18(73), 241-27 L.; S. KALIRAMESH, V. CHELLADURAI, D. JAYAS, K. ALAGUSUNDARAM, N. WHITE, AND P. FIELDS, Detection of infestation by Callosobruchus maculatus in mung bean using near-infrared hyperspectral imaging, Journal of Stored Products Research, 52:107-111, 2013.; F. MARTINELLI, R. SCALENGHE, S. DAVINO, S. PANNO, G. SCUDERI, P. RUISI, P. VILLA, D. STROPPIANA, M. BOSCHETTI, L. R. GOULART, ET AL., Advanced methods of plant disease detection. A review, Agronomy for Sustainable Development, 35(1): 1-25, 2015.; A. RATNADASS, P. FERNANDES, J. AVELINO, AND R. HABIB, Plant species diversity for sustainable management of crop pests and diseases in agroecosystems: a review, A gronomy for Sustainable Development, 32(1):273-303, 2012.; J. ZHANG, R. PU, W. HUANG, L. YUAN, J. LUO, AND J. WANG, Using in-situ hyperspec-tral data for detecting and discriminating yellow rust disease from nutrient stresses, Field Crops Research, 134:165-174, 2012.; CABALLERO, Y., GIRALDO, R., AND MATEU, J., A spatial randomness test based on the box-counting dimension, AStA Advances in Statistical Analysis, 106:499-524, 2022.; FALCONER, K., Fractal Geometry: Mathematical Foundations and Applications, John Wiley & Sons, Chichester, 2nd edition, 2004.*; STOYAN, D. AND STOYAN, H., Fractals, Random Shapes and Point Fields: Methods of Geometrical Statistics, John Wiley & Sons, Chichester, 1994.; KOLDA, T. G. & BADER, B. W., Tensor decompositions and applications, SIAM Review, 51(3), 455-500, 2009.; GUO, W., KOTSIA, I. & PATRAS, I., Tensor learning for regression, IEEE Transactions on Image Processing, 21(2), 816-827, 2011.; OUT, C., NIE, F., YI, D. & WU, Y., Eficient image classification via multiple rank regression, IEEE Transactions on Image Processing, 22(1), 340-352, 2012.; HILBE, J. M. (2009)., Logistic regression models., Boca Raton: Chapman HalV/CRC.; HOSMER JR., D., Y LaMESHOW, S. (2000)., Applied logistic regression (2nd ed.). New York: John Wiley Sons.; PANDO, V., Y SAN MARTÍN, R. (2004)., Regresión logística multinomial., 18, 323-327.; ACKROYD, S., Part Il Critique and development, En S. FLEETWOOD & S. ACKROYD (EDS.), Critical realist applications in organisation and management studies (pp. 121-153), Routledge, Taylor & Francis Group, 2004.; BHASKAR, R., A Realistic Theory of Science, Verso, 1975.; BHASKAR, R., Plato etc.: The Problems of Philosophy and their Resolution, Verso, 1994; BHASKAR, R., A Realist Theory of Science, Taylor & Francis Group, 2008.; BHASKAR, R., Plato Etc. Problems of Philosophy and their Resolution, Taylor & Francis Group, 2010.; CARAYANNIS, E. G., BARTH, T. D. & CAMPBELL, D. F., The Quintuple Helic innovation model: global warming as a challenge and driver for innovation, Journal of Innovation and Entrepreneurship, 1(1), 2, 2012.; CARAYANNIS, E. G. & CAMPBELL, D. F. J., Triple Helix, Quadruple Helix and Quintuple Helix and How Do Knowledge, Innovation and the Environment Relate To Each Other?, International Journal of Social Ecology and Sustainable Development, 1(1), 41-69, 2010.; CARAYANNIS, E. G. & CAMPBELL, D. F. J., Open Innovation Diplomacy and a 21 st Century Fractal Research, Education and Innovation (FREIE) Ecosystem: Building on the Quadruple and Quintuple Helix Innovation Concepts and the "Mode 3" Knowledge Production System, Journal of the Knowledge Economy, 2(3), 327-372, 2011. https: //doi.org/10.1007/813132-011-0058-3; CARMAGNOLA, FRANCESCA & OSBORNE, FRANCESCO & TORRE, ILARIA, Cross-systems identification of users in the Social Web. 1101 C. CHEN, N. N. XIONG, X. GUO AND J. REN, The System Identification and Prediction of the Social Earthquakes Burst in Human Society, IEEE Access, 8, 103848-103859, 2020. doi:10.1109/ACCESS. 2020. 2999575; FERNÁNDEZ-ESQUINAS, M. & PEREZ-YRUELA, M., Knowledge Transfer in Regional Innovation Systems: The effects of Socioeconomic Structure, En H. PINTO (BD.), Resilient Territories: Innovation and Creativity for New Modes of Regional Development (pp. 53-74), Cambridge Scholar Publishing, 2015.; GÓMEZ, A., Aportes para la construcción de una política pública para la formación doctoral en Colombia, 2015.; HAMM, R., KOPPER, J., JAGER, A., KARL, H., STROTEBECK, F. & WARNECKE, C., RegTrans - Zwischenbericht I Regionale Transfereffekte verschiedener Hochschultypen Analyse und Strategien für eine verbesserte Potenzialausschöpfung, 2012.; H. -T. WAI, A. SCAGLIONE & A. LESHEM, The social system identification problem, 2015 54th IEEE Conference on Decision and Control (CDC), Osaka, Japan, pp. 406-411, 2015. doi: 10. 1109/CDC. 2015.7402234; JAEGER, A. & KOPPER, J., Third mission potential in higher education: measuring the regional focus of different types of HEls, Jahrbuch Fur Regionalwissenschaft, 95-118, 2014.; KASIANIUK, K., A system-cybernetic approach to the study of political power. In-troductory remarks, Kybernetes, 47(6), 1262-1276, 2018. https://doi.org/10.1108/ K-04-2017-0146%7C; LLISTERRI, J. J.J. & PIETROBELLI, C., Los Sistemas Regionales de Innovación en América Latina, Banco Interamericano de Desarrollo, 2011.; MELO, A., BECK, C. L., PENA, J. I. & PARE, P. E., Knowledge transfer from universities to regions as a network spreading process, 4th IEEE International Symposium on Systems Engineering, ISSE 2018 - Proceedings, 2018. https://doi.org/10. 1109/SysEng. 2018, 8644398; UNAL-CID, Lineamientos de Política y Plan Operativo para la política de Fomento y Formación de Vocaciones en Ciencia Tecnología e Innovación: Producto Final, 2020.; https://repositorio.unal.edu.co/handle/unal/87529; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; repositorio.unal.edu.co

Dostupnost: https://repositorio.unal.edu.co/handle/unal/87529

Aceleración de algoritmos de clasificación basados en disimilitudes, utilizando arquitecturas computacionales con múltiples núcleos. ; Acceleration of dissimilarity-based classi cation algorithms using multi-core computational architectures.

Autoři: Uribe Hurtado, Ana Lorena Orozco-Alzate, Mauricio Grupo de Ambientes Inteligentes Adaptativos - GAIA

Přispěvatelé: Uribe Hurtado, Ana Lorena Orozco-Alzate, Mauricio Grupo de Ambientes Inteligentes Adaptativos - GAIA

Témata: 620 - Ingeniería y operaciones afines, Clasificación y agrupamiento basados en disimilitudes, Computación paralela, Múltiples núcleos CPU, Muchos núcleos GPU, Medidas de disimilitud, Prueba leave-one-out para múltiples núcleos, Dissimilarity-based classifiers and clustering Inglés, Parallel computer, Multi-core CPU, Many-core GPU, Dissimilarity measures, Leave-one-out test for multi-core

Popis souboru: xxiii, 201 páginas; application/pdf

Relation: Doug Abbott. Chapter 12 - Posix Threads. In Doug Abbott, editor, Linux for Embedded and Real-time Applications (Second Edition), Embedded Technology, pages 197-214. Newnes, Burlington, second edition edition, 2006.; Elfatih M. Abdel-Rahman, Onisimo Mutanga, Elhadi Adam, and Riyad Ismail. Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classi ers. ISPRS Journal of Photogrammetry and Remote Sensing, 88:48-59, 2014.; Karim Abou-Moustafa and Frank P. Ferrie. Local generalized quadratic distance metrics: Application to the k-nearest neighbors classi er. Advances in Data Analysis and Classi cation, 12(2):341-363, 2018.; Samson Abramsky. Computational interpretations of linear logic. Theoretical Computer Science, 111(1-2):3-57, 1993.; A. Ahmadzadeh, R. Mirzaei, H. Madani, M. Shobeiri, M. Sadeghi, M. Gavahi, K. Jafari, M. M. Aznaveh, and S. Gorgin. Cost-e cient implementation of k-NN algorithm on multi-core processors. In Twelfth ACM/IEEE International Conference on Formal Methods and Models for Codesign, MEMOCODE 2014, pages 205-208, oct 2014.; https://repositorio.unal.edu.co/handle/unal/81347; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

Dostupnost: https://repositorio.unal.edu.co/handle/unal/81347
https://repositorio.unal.edu.co/

Estudio de la reducción del sobreajuste en arquitecturas de redes neuronales residuales ResNet en un escenario de clasificación de patrones ; Study of overfitting reduction in residual neural network architectures (ResNet) in a pattern classification scenario

Autoři: Chacón Chamorro, Manuela Viviana Riaño Rojas, Juan Carlos Gallego Restrepo, Fernando Andrés a další

Přispěvatelé: Chacón Chamorro, Manuela Viviana Riaño Rojas, Juan Carlos Gallego Restrepo, Fernando Andrés a další

Témata: 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas, Continuidad Lipschitz, Generalización, ODENet, Regularización, Redes neuronales, ResNet, Sobreajuste, Generalization, Lipschitz continuity, Neural Networks, Overfitting, Regularization

Popis souboru: xx, 150 páginas; application/pdf

Relation: C. C. Aggarwal, Neural Networks and Deep Learning. Springer, 2018.; Z.-Q. Zhao, P. Zheng, S.-t. Xu, and X. Wu, “Object detection with deep learning: A review,” 2018. [Online]. Available: https://arxiv.org/abs/1807.05511; A. Kamilaris and F. X. Prenafeta-Bold ́u, “A review of the use of convolutional neural networks in agriculture,” The Journal of Agricultural Science, vol. 156, no. 3, p.312–322, 2018. [Online]. Available: https://doi.org/10.1017/S0021859618000436; A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath, “Generative adversarial networks: An overview,” IEEE Signal Processing Magazine, vol. 35, no. 1, pp. 53–65, jan 2018. [Online]. Available: https://doi.org/10.1109%2Fmsp.2017.2765202; A. Fadaeddini, M. Eshghi, and B. Majidi, “A deep residual neural network for low altitude remote sensing image classification,” in 2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), 2018, pp. 43–46. [Online]. Available: https://ieeexplore.ieee.org/document/8336623; L. Massidda, M. Marrocu, and S. Manca, “Non-intrusive load disaggregation by convolutional neural network and multilabel classification,” Applied Sciences, vol. 10, no. 4, 2020. [Online]. Available: https://www.mdpi.com/2076-3417/10/4/1454; K. Muralitharan, R. Sakthivel, and R. Vishnuvarthan, “Neural network based optimization approach for energy demand prediction in smart grid,” Neurocomputing, vol. 273, pp. 199–208, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925231217313681; O. Al-Salman, J. Mustafina, and G. Shahoodh, “A systematic review of artificial neural networks in medical science and applications,” in 2020 13th International Conference on Developments in eSystems Engineering (DeSE), 2020, pp. 279–282. [Online]. Available: https://ieeexplore.ieee.org/document/9450245; M. M. Bejani and M. Ghatee, “A systematic review on overfitting control in shallow and deep neural networks,” Artificial Intelligence Review, vol. 54, no. 8, pp. 6391–6438, 2021. [Online]. Available: https://doi.org/10.1007/s10462-021-09975-1; S. Salman and X. Liu, “Overfitting mechanism and avoidance in deep neural networks,” arXiv preprint arXiv:1901.06566, 2019. [Online]. Available: https://arxiv.org/abs/1901.06566; X. Ying, “An overview of overfitting and its solutions,” in Journal of physics: Conference series, vol. 1168, no. 2. IOP Publishing, 2019, p. 022022. [Online]. Available: https://iopscience.iop.org/article/10.1088/1742-6596/1168/2/022022; I. Bilbao and J. Bilbao, “Overfitting problem and the over-training in the era of data: Particularly for artificial neural networks,” in 2017 eighth international conference on intelligent computing and information systems (ICICIS). IEEE, 2017, pp. 173–177. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8260032; R. T. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, “Neural ordinary differential equations,” Advances in neural information processing systems, vol. 31, 2018. [Online]. Available: https://proceedings.neurips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf; D. Karlsson and O. Svanstr ̈om, “Modelling dynamical systems using neural ordinary differential equations,” Master’s thesis, Chalmers University of Technology, 2019. [Online]. Available: https://odr.chalmers.se/server/api/core/bitstreams/83a4c17f-35e7-43ce-ac60-43a0b799f82f/content; E. Weinan, “A proposal on machine learning via dynamical systems,” Communications in Mathematics and Statistics, vol. 1, no. 5, pp. 1–11, 2017. [Online]. Available: https://link.springer.com/article/10.1007/s40304-017-0103-z; M. Benning, E. Celledoni, M. J. Ehrhardt, B. Owren, and C.-B. Schonlieb, “Deep learning as optimal control problems: Models and numerical methods,” arXiv preprint arXiv:1904.05657, 2019. [Online]. Available: https://arxiv.org/abs/1904.05657; Q. Li, L. Chen, C. Tai et al., “Maximum principle based algorithms for deep learning,” Journal of Machine Learning Research, pp. 1–29, 2018. [Online]. Available: https://www.jmlr.org/papers/volume18/17-653/17-653.pdf; Q. Li and S. Hao, “An optimal control approach to deep learning and applications to discrete-weight neural networks,” in International Conference on Machine Learning. PMLR, 2018, pp. 2985–2994. [Online]. Available: http://proceedings.mlr.press/v80/li18b/li18b.pdf; J. Han, Q. Li et al., “A mean-field optimal control formulation of deep learning,” Research in the Mathematical Sciences, vol. 6, no. 1, pp. 1–41, 2019. [Online]. Available: https://link.springer.com/article/10.1007/s40687-018-0172-y; E. Haber and L. Ruthotto, “Stable architectures for deep neural networks,” Inverse problems, vol. 34, no. 1, p. 014004, 2017. [Online]. Available: https://iopscience.iop.org/article/10.1088/1361-6420/aa9a90/meta; B. Chang, L. Meng, E. Haber, F. Tung, and D. Begert, “Multi-level residual networks from dynamical systems view,” arXiv preprint arXiv:1710.10348, 2017. [Online]. Available: https://arxiv.org/abs/1710.10348; M. Ciccone, M. Gallieri, J. Masci, C. Osendorfer, and F. Gomez, “Nais-net: Stable deep networks from non-autonomous differential equations,” Advances in Neural Information Processing Systems, vol. 31, 2018. [Online]. Available: https://proceedings.neurips.cc/paper/2018/file/7bd28f15a49d5e5848d6ec70e584e625-Paper.pdf; C. Finlay, J. Calder, B. Abbasi, and A. Oberman, “Lipschitz regularized deep neural networks generalize and are adversarially robust,” arXiv preprint arXiv:1808.09540, 2018. [Online]. Available: https://arxiv.org/abs/1808.09540; P. Pauli, A. Koch, J. Berberich, P. Kohler, and F. Allg ̈ower, “Training robust neural networks using lipschitz bounds,” IEEE Control Systems Letters, vol. 6, pp. 121–126, 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9319198; H. Gouk, E. Frank, B. Pfahringer, and M. J. Cree, “Regularisation of neural networks by enforcing lipschitz continuity,” Machine Learning, vol. 110, no. 2, pp. 393–416, 2021. [Online]. Available: https://link.springer.com/article/10.1007/s10994-020-05929-w; B. Dherin, M. Munn, M. Rosca, and D. G. Barrett, “Why neural networks find simple solutions: the many regularizers of geometric complexity,” arXiv preprint arXiv:2209.13083, 2022. [Online]. Available: https://arxiv.org/abs/2209.13083; T. Zhou, Q. Li, H. Lu, Q. Cheng, and X. Zhang, “Gan review: Models and medical image fusion applications,” Information Fusion, vol. 91, pp. 134–148, 2023. [Online]. Available: https://doi.org/10.1016/j.inffus.2022.10.017; C. A. Charu, Neural networks and deep learning: a textbook. Spinger, 2018.; S. Theodoridis, “Neural networks and deep learning,” Machine Learning, pp. 875–936, 2015; F. Rosenblatt, “The perceptron: a probabilistic model for information storage and organization in the brain.” Psychological review, vol. 65, no. 6, p. 386, 1958. [Online]. Available: https://psycnet.apa.org/record/1959-09865-001; B. Pang, E. Nijkamp, and Y. N. Wu, “Deep learning with tensorflow: A review,” Journal of Educational and Behavioral Statistics, vol. 45, no. 2, pp. 227–248, 2020. [Online]. Available: https://doi.org/10.3102/1076998619872761; K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778. [Online]. Available: https://ieeexplore.ieee.org/document/7780459; D. Hunter, H. Yu, M. S. Pukish III, J. Kolbusz, and B. M. Wilamowski, “Selection of proper neural network sizes and architectures a comparative study,” IEEE Transactions on Industrial Informatics, vol. 8, no. 2, pp. 228–240, 2012. [Online]. Available: https://ieeexplore.ieee.org/document/6152147; MATLAB, “Statistics and machine learning toolbox.” [Online]. Available: https://la.mathworks.com/products/statistics.html; R, “neuralnet: Training of neural networks.” [Online]. Available: https://www.rdocumentation.org/packages/neuralnet/versions/1.44.2/topics/neuralnet; TensorFlow, “Tensorflow 2.10.0.” [Online]. Available: https://www.tensorflow.org/; J. Reunanen, “Overfitting in making comparisons between variable selection methods,” Journal of Machine Learning Research, vol. 3, pp. 1371–1382, 2003. [Online]. Available: https://www.jmlr.org/papers/volume3/reunanen03a/reunanen03a.pdf; I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of machine learning research, vol. 3, pp. 1157–1182, 2003. [Online]. Available: https://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf; F. Johnson, A. Valderrama, C. Valle, B. Crawford, R. Soto, and R. Nanculef, “Automating configuration of convolutional neural network hyperparameters using genetic algorithm,” IEEE Access, vol. 8, pp. 156 139–156 152, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/9177040; Y. Zhu, G. Li, R. Wang, S. Tang, H. Su, and K. Cao, “Intelligent fault diagnosis of hydraulic piston pump combining improved lenet-5 and pso hyperparameter optimization,” Applied Acoustics, vol. 183, p. 108336, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0003682X21004308; A. Gaspar, D. Oliva, E. Cuevas, D. Zaldıvar, M. Pérez, and G. Pajares, “Hyperparameter optimization in a convolutional neural network using metaheuristic algorithms,” in Metaheuristics in Machine Learning: Theory and Applications. Springer, 2021, pp. 37–59. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-030-70542-8 2; O. S. Steinholtz, “A comparative study of black-box optimization algorithms for tuning of hyper-parameters in deep neural networks,” Lulea University of Technology, 2018. [Online]. Available: https://ltu.divaportal.org/smash/get/diva2:1223709/FULLTEXT01.pdf; L. Lugo, “A recurrent neural network approach for whole genome bacteria classification,” Master’s thesis, Universidad Nacional de Colombia, Bogota, Colombia, 2018.; A. T. Sarmiento and O. Soto, “New product forecasting demand by using neural networks and similar product analysis.” Master’s thesis, Universidad Nacional de Colombia, Medellin, Colombia, 2014.; A. E. Casas Fajardo, “Propuesta metodológica para calcular el avalúo de un predio empleando redes neuronales artificiales,” Master’s thesis, Universidad Nacional de Colombia, Bogotá, Colombia, 2014.; S. Ortega Alzate, “Exploración de las redes neuronales para la proyección de la máxima pérdida esperada de una póliza de seguros: aplicación para un seguro previsionales,” Master’s thesis, Universidad Nacional de Colombia, Medellin, Colombia, 2021.; D. Collazos, “Kernel-based enhancement of general stochastic network for supervised learning,” Master’s thesis, Universidad Nacional de Colombia, Manizales, Colombia, 2016.; Y. Lu, A. Zhong, Q. Li, and B. Dong, “Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations,” in International Conference on Machine Learning. PMLR, 2018, pp. 3276–3285. [Online]. Available: http://proceedings.mlr.press/v80/lu18d/lu18d.pdf; B. Geshkovski and E. Zuazua, “Turnpike in optimal control of pdes, resnets, and beyond,” Acta Numerica, vol. 31, pp. 135–263, 2022. [Online]. Available: https://doi.org/10.1017/S0962492922000046; D. Ruiz-Balet and E. Zuazua, “Neural ode control for classification, approximation and transport,” arXiv preprint arXiv:2104.05278, 2021. [Online]. Available: https://arxiv.org/abs/2104.05278; D. Ruiz-Balet, E. Affili, and E. Zuazua, “Interpolation and approximation via momentum resnets and neural odes,” Systems & Control Letters, vol. 162, p. 105182, 2022. [Online]. Available: https://doi.org/10.1016/j.sysconle.2022.105182; M. Fazlyab, A. Robey, H. Hassani, M. Morari, and G. Pappas, “Efficient and accurate estimation of lipschitz constants for deep neural networks,” Advances in Neural Information Processing Systems, vol. 32, 2019. [Online]. Available: https://proceedings.neurips.cc/paper/2019/file/95e1533eb1b20a97777749fb94fdb944-Paper.pdf; A. Xue, L. Lindemann, A. Robey, H. Hassani, G. J. Pappas, and R. Alur, “Chordal sparsity for lipschitz constant estimation of deep neural networks,” arXiv preprint arXiv:2204.00846, 2022. [Online]. Available: https://arxiv.org/abs/2204.00846; L. Massidda, M. Marrocu, and S. Manca, “Non-intrusive load disaggregation by convolutional neural network and multilabel classification,” Applied Sciences, vol. 10, no. 4, p. 1454, 2020. [Online]. Available: https://www.mdpi.com/2076-3417/10/4/1454; K. Muralitharan, R. Sakthivel, and R. Vishnuvarthan, “Neural network based optimization approach for energy demand prediction in smart grid,” Neurocomputing, vol. 273, pp. 199–208, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0925231217313681; R. Lu and S. H. Hong, “Incentive-based demand response for smart grid with reinforcement learning and deep neural network,” Applied energy, vol. 236, pp. 937–949, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0306261918318798; A. P. Marug ́an, F. P. G. M ́arquez, J. M. P. Perez, and D. Ruiz-Hern ́andez, “A survey of artificial neural network in wind energy systems,” Applied energy, vol. 228, pp. 1822–1836, 2018. [Online]. Available: https://doi.org/10.1016/j.apenergy.2018.07.084; F. Saeed, M. A. Khan, M. Sharif, M. Mittal, L. M. Goyal, and S. Roy, “Deep neural network features fusion and selection based on pls regression with an application for crops diseases classification,” Applied Soft Computing, vol. 103, p. 107164, 2021. [Online]. Available: https://doi.org/10.1016/j.asoc.2021.107164; M. Loey, A. ElSawy, and M. Afify, “Deep learning in plant diseases detection for agricultural crops: A survey,” International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), vol. 11, no. 2, pp. 41–58, 2020. [Online]. Available: https://www.igi-global.com/article/deep-learning-in-plant-diseases-detection-for-agricultural-crops/248499; B. Pandey, D. K. Pandey, B. P. Mishra, and W. Rhmann, “A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions,” Journal of King Saud University-Computer and Information Sciences, 2021.; A. Nogales, A. J. Garcia-Tejedor, D. Monge, J. S. Vara, and C. Antón,“A survey of deep learning models in medical therapeutic areas,” Artificial Intelligence in Medicine, vol. 112, p. 102020, 2021. [Online]. Available: https://doi.org/10.1016/j.artmed.2021.102020; Colciencias, “Plan Nacional de CTeI para el desarrollo del sector Tecnologías de la Información TIC 2017 - 2022,,” Bogotá, Colombia, 2017; República de Colombia, “Plan de Desarrollo Nacional 2018-2022 “Pacto por Colombia, pacto por la equidad”,” Bogotá, Colombia, 2018; Colciencias, “Política Nacional de Ciencia e Innovación para el Desarrollo Sostenible Libro Verde 2030,” Bogotá, Colombia, 2018.; J. C. Riaño Rojas, “Desarrollo de una metodología como soporte para la detección de enfermedades vasculares del tejido conectivo a través de imágenes capilaroscópicas,” Ph.D. dissertation, Universidad Nacional de Colombia, Bogotá, Colombia, 2010; T. T. Tang, J. A. Zawaski, K. N. Francis, A. A. Qutub, and M. W. Gaber, “Image-based classification of tumor type and growth rate using machine learning: a preclinical study,” Scientific reports, vol. 9, no. 1, pp. 1–10, 2019. [Online]. Available: https://www.nature.com/articles/s41598-019-48738-5; C. A. Pedraza Bonilla and L. Rodríguez Mújica, “Método para la estimación de maleza en cultivos de lechuga utilizando aprendizaje profundo e imágenes multiespectrales,” Master’s thesis, Universidad Nacional de Colombia., Bogotá, Colombia, 2016.; C. Barrios Pérez, “Zonificación agroecológica para el cultivo de arroz de riego (Oryza Sativa L.) en Colombia,” Master’s thesis, Universidad Católica de Colombia., Palmira, Colombia, 2016; A. F. Montenegro and C. D. Parada, “Diseño e implementación de un sistema de detección de malezas en cultivos cundiboyacenses,” Master’s thesis, Universidad Católica de Colombia., Bogotá, Colombia, 2015; M. Minsky and S. Papert, “An introduction to computational geometry,” Cambridge tiass., HIT, vol. 479, p. 480, 1969.; G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of control, signals and systems, vol. 2, no. 4, pp. 303–314, 1989. [Online]. Available: https://link.springer.com/article/10.1007/BF02551274; D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” nature, vol. 323, no. 6088, pp. 533–536, 1986. [Online]. Available: https://www.nature.com/articles/323533a0; Y. Yu, X. Si, C. Hu, and J. Zhang, “A review of recurrent neural networks: LSTM cells and network architectures,” Neural computation, vol. 31, no. 7, pp. 1235–1270, 2019. [Online]. Available: https://ieeexplore.ieee.org/document/8737887; N. Li, S. Liu, Y. Liu, S. Zhao, and M. Liu, “Neural speech synthesis with transformer network,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, 2019, pp. 6706–6713; G. Parascandolo, H. Huttunen, and T. Virtanen, “Taming the waves: sine as activation function in deep neural networks,” 2017. [Online]. Available: https://openreview.net/forum?id=Sks3zF9eg; J. Heredia-Juesas and J. A. Martínez-Lorenzo, “Consensus function from an Lp− norm regularization term for its use as adaptive activation functions in neural networks,” arXiv e-prints, pp. arXiv–2206, 2022. [Online]. Available: https://arxiv.org/abs/2206.15017; A. D. Jagtap, Y. Shin, K. Kawaguchi, and G. E. Karniadakis, “Deep kronecker neural networks: A general framework for neural networks with adaptive activation functions,” Neurocomputing, vol. 468, pp. 165–180, 2022. [Online]. Available: https://doi.org/10.1016/j.neucom.2021.10.036; D. Devikanniga, K. Vetrivel, and N. Badrinath, “Review of meta-heuristic optimization based artificial neural networks and its applications,” in Journal of Physics: Conference Series, vol. 1362, no. 1. IOP Publishing, 2019, p. 012074. [Online]. Available: https://iopscience.iop.org/article/10.1088/1742-6596/1362/1/012074/meta; N. Gupta, M. Khosravy, N. Patel, S. Gupta, and G. Varshney, “Evolutionary artificial neural networks: comparative study on state-of-the-art optimizers,” in Frontier applications of nature inspired computation. Springer, 2020, pp. 302–318. [Online]. Available: https://link.springer.com/chapter/10.1007/978-981-15-2133-1 14; J. Duchi, E. Hazan, and Y. Singer, “Adaptive subgradient methods for online learning and stochastic optimization.” Journal of machine learning research, vol. 12, no. 7, 2011. [Online]. Available: https://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf; D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014. [Online]. Available: https://arxiv.org/abs/1412.6980; M. D. Zeiler, “ADADELTA: an adaptive learning rate method,” arXiv preprint arXiv:1212.5701, 2012. [Online]. Available: https://arxiv.org/abs/1212.5701; S. Ruder, “An overview of gradient descent optimization algorithms,” arXiv preprint arXiv:1609.04747, 2016. [Online]. Available: https://arxiv.org/abs/1609.04747; P. Netrapalli, “Stochastic gradient descent and its variants in machine learning,” Journal of the Indian Institute of Science, vol. 99, no. 2, pp. 201–213, 2019. [Online]. Available: https://link.springer.com/article/10.1007/s41745-019-0098-4; S. Lawrence, C. L. Giles, and A. C. Tsoi, “Lessons in neural network training: Overfitting may be harder than expected,” in Proceedings of the Fourteenth National Conference on Artificial Intelligence, 1997, pp. 540–545. [Online]. Available: https://clgiles.ist.psu.edu/papers/AAAI-97.overfitting.hard_to_do.pdf; X.-x. Wu and J.-g. Liu, “A new early stopping algorithm for improving neural network generalization,” in 2009 Second International Conference on Intelligent Computation Technology and Automation, vol. 1. IEEE, 2009, pp. 15–18. [Online]. Available: https://ieeexplore.ieee.org/document/5287721; H. Liang, S. Zhang, J. Sun, X. He, W. Huang, K. Zhuang, and Z. Li, “Darts+: Improved differentiable architecture search with early stopping,” arXiv preprint arXiv:1909.06035, 2019. [Online]. Available: https://arxiv.org/abs/1909.06035; L. Prechelt, “Early stopping-but when?” in Neural Networks: Tricks of the trade. Springer, 1998, pp. 55–69. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-642-35289-8 5; M. Mahsereci, L. Balles, C. Lassner, and P. Hennig, “Early stopping without a validation set,” arXiv preprint arXiv:1703.09580, 2017. [Online]. Available: https://arxiv.org/abs/1703.09580; C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of big data, vol. 6, no. 1, pp. 1–48, 2019. [Online]. Available: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0197-0; A. Mikolajczyk and M. Grochowski, “Data augmentation for improving deep learning in image classification problem,” in 2018 International Interdisciplinary PhD Workshop (IIPhDW). IEEE, 2018, pp. 117–122. [Online]. Available: https://ieeexplore.ieee.org/document/8388338; K. el Hindi and A.-A. Mousa, “Smoothing decision boundaries to avoid overfitting in neural network training,” Neural Network World, vol. 21, no. 4, p. 311, 2011. [Online]. Available: https://www.researchgate.net/publication/272237391_Smoothing_decision_boundaries_to_avoid_overfitting_in_neural_network_training; H. Jabbar and R. Z. Khan, “Methods to avoid over-fitting and under-fitting in supervised machine learning (comparative study),” Computer Science, Communication and Instrumentation Devices, vol. 70, 2015; K.-j. Kim, “Artificial neural networks with evolutionary instance selection for financial forecasting,” Expert Systems with Applications, vol. 30, no. 3, pp. 519–526, 2006. [Online]. Available: https://doi.org/10.1016/j.eswa.2005.10.007; N. Srivastava, “Improving neural networks with dropout,” Master’s thesis, University of Toronto, 2013. [Online]. Available: http://www.cs.toronto.edu/∼nitish/msc thesis.pdf; N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The journal of machine learning research, vol. 15, no. 1, pp. 1929–1958, 2014. [Online]. Available: https://jmlr.org/papers/v15/srivastava14a.html; J. Ba and B. Frey, “Adaptive dropout for training deep neural networks,” Advances in neural information processing systems, vol. 26, 2013. [Online]. Available: https://proceedings.neurips.cc/paper/2013/file/7b5b23f4aadf9513306bcd59afb6e4c9-Paper.pdf; B. Ko, H.-G. Kim, K.-J. Oh, and H.-J. Choi, “Controlled dropout: A different approach to using dropout on deep neural network,” in 2017 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2017, pp. 358–362. [Online]. Available: https://ieeexplore.ieee.org/document/7881693; D. Molchanov, A. Ashukha, and D. Vetrov, “Variational dropout sparsifies deep neural networks,” in International Conference on Machine Learning. PMLR, 2017, pp. 2498–2507. [Online]. Available: https://arxiv.org/abs/1701.05369; G. Zhang, C. Wang, B. Xu, and R. Grosse, “Three mechanisms of weight decay regularization,” in International Conference on Learning Representations, 2018. [Online]. Available: https://www.researchgate.net/publication/328598833_Three_Mechanisms_of_Weight_Decay_Regularization; S. J. Nowlan and G. E. Hinton, “Simplifying neural networks by soft weight sharing,” in The Mathematics of Generalization. CRC Press, 2018, pp. 373–394. [Online]. Available: https://ieeexplore.ieee.org/document/6796174; R. Ghosh and M. Motani, “Network-to-network regularization: Enforcing occam’s razor to improve generalization,” Advances in Neural Information Processing Systems, vol. 34, pp. 6341–6352, 2021. [Online]. Available: https://proceedings.neurips.cc/paper/2021/file/321cf86b4c9f5ddd04881a44067c2a5a-Paper.pdf; B. Neal, S. Mittal, A. Baratin, V. Tantia, M. Scicluna, S. Lacoste-Julien, and I. Mitliagkas, “A modern take on the bias-variance tradeoff in neural networks,” arXiv preprint arXiv:1810.08591, 2018. [Online]. Available: https://arxiv.org/abs/1810.08591; P. Nakkiran, G. Kaplun, Y. Bansal, T. Yang, B. Barak, and I. Sutskever, “Deep double descent: Where bigger models and more data hurt,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2021, no. 12, p. 124003, 2021. [Online]. Available: https://iopscience.iop.org/article/10.1088/1742-5468/ac3a74/meta; Z. Yang, Y. Yu, C. You, J. Steinhardt, and Y. Ma, “Rethinking bias-variance trade-off for generalization of neural networks,” in International Conference on Machine Learning. PMLR, 2020, pp. 10 767–10 777. [Online]. Available: http://proceedings.mlr.press/v119/yang20j/yang20j.pdf; Y. Dar, V. Muthukumar, and R. G. Baraniuk, “A farewell to the bias-variance tradeoff? an overview of the theory of overparameterized machine learning,” arXiv preprint arXiv:2109.02355, 2021. [Online]. Available: https://arxiv.org/abs/2109.02355; B. Ghojogh and M. Crowley, “The theory behind overfitting, cross validation, regularization, bagging, and boosting: tutorial,” arXiv preprint arXiv:1905.12787, 2019. [Online]. Available: https://arxiv.org/abs/1905.12787; Y. Yoshida and T. Miyato, “Spectral norm regularization for improving the generalizability of deep learning,” arXiv preprint arXiv:1705.10941, 2017. [Online]. Available: https://arxiv.org/abs/1705.10941; Y. Tsuzuku, I. Sato, and M. Sugiyama, “Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks,” Advances in neural information processing systems, vol. 31, 2018. [Online]. Available: https://proceedings.neurips.cc/paper/2018/file/485843481a7edacbfce101ecb1e4d2a8-Paper.pdf; H. Li, J. Li, X. Guan, B. Liang, Y. Lai, and X. Luo, “Research on overfitting of deep learning,” in 2019 15th International Conference on Computational Intelligence and Security (CIS). IEEE, 2019, pp. 78–81. [Online]. Available: https://ieeexplore.ieee.org/document/9023664; A. Gavrilov, A. Jordache, M. Vasdani, and J. Deng, “Convolutional neural networks: Estimating relations in the ising model on overfitting,” in 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC). IEEE, 2018, pp. 154–158. [Online]. Available: https://ieeexplore.ieee.org/document/8482067; L. Deng, “The mnist database of handwritten digit images for machine learning research,” IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 141–142, 2012. [Online]. Available: https://ieeexplore.ieee.org/document/6296535; H. Xiao, K. Rasul, and R. Vollgraf, “Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,” arXiv preprint arXiv:1708.07747, 2017. [Online]. Available: https://arxiv.org/abs/1708.07747; F. Hutter, H. H. Hoos, and K. Leyton-Brown, “Sequential model-based optimization for general algorithm configuration,” in International conference on learning and intelligent optimization. Springer, 2011, pp. 507–523. [Online]. Available: https://ml.informatik.uni-freiburg.de/wp-content/uploads/papers/11-LION5-SMAC.pdf; M. López-Ibáñez, J. Dubois-Lacoste, L. Pérez Cáceres, M. Birattari, and T. Stutzle, “The irace package: Iterated racing for automatic algorithm configuration,” Operations Research Perspectives, vol. 3, pp. 43–58, 2016. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2214716015300270; R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Annals of eugenics, vol. 7, no. 2, pp. 179–188, 1936; D. Dua and C. Graff, “UCI machine learning repository,” 2017. [Online]. Available: http://archive.ics.uci.edu/ml; I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in International Conference on Learning Representations, 2015. [Online]. Available: https://arxiv.org/abs/1412.6572; A. Kurakin, I. J. Goodfellow, and S. Bengio, “Adversarial examples in the physical world,” in Artificial intelligence safety and security. Chapman and Hall/CRC, 2018, pp. 99–112. [Online]. Available: https://openreview.net/pdf?id=S1OufnIlx; Z. Chen, Q. Li, and Z. Zhang, “Towards robust neural networks via close-loop control,” arXiv preprint arXiv:2102.01862, 2021. [Online]. Available: https://arxiv.org/abs/2102.01862; L. Bottcher, N. Antulov-Fantulin, and T. Asikis, “AI Pontryagin or how artificial neural networks learn to control dynamical systems,” Nature Communications, vol. 13, no. 1, pp. 1–9, 2022. [Online]. Available: https://www.nature.com/articles/s41467-021-27590-0; J. Zhuang, N. C. Dvornek, S. Tatikonda, and J. S. Duncan, “MALI: A memory efficient and reverse accurate integrator for neural ODEs,” arXiv preprint arXiv:2102.04668, 2021. [Online]. Available: https://arxiv.org/abs/2102.04668; C. Rackauckas, M. Innes, Y. Ma, J. Bettencourt, L. White, and V. Dixit, “Diffeqflux.jl-a julia library for neural differential equations,” arXiv preprint arXiv:1902.02376, 2019. [Online]. Available: https://arxiv.org/abs/1902.02376; D. M. Grobman, “Homeomorphism of systems of differential equations,” Doklady Akademii Nauk SSSR, vol. 128, no. 5, pp. 880–881, 1959.; P. Hartman, “A lemma in the theory of structural stability of differential equations,” Proceedings of the American Mathematical Society, vol. 11, no. 4, pp. 610–620, 1960. [Online]. Available: https://www.ams.org/journals/proc/1960-011-04/S0002-9939-1960-0121542-7/S0002-9939-1960-0121542-7.pdf; Ministerio de ciencia, tecnología e innovación. Minciencias, “Guía técnica para el reconocimiento de actores del SNCTeI,” 2021. [Online]. Available: https://minciencias.gov.co/sites/default/files/upload/reconocimiento/m601pr05g07_guia_tecnica_para_el_reconocimiento_del_centro_de_desarrollo_tecnologico_cdt_v00_0.pdf; https://repositorio.unal.edu.co/handle/unal/84211; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

Dostupnost: https://repositorio.unal.edu.co/handle/unal/84211
https://repositorio.unal.edu.co/

Clasificación de semioquímicos asociados a coleópteros del suborden Polyphaga mediante redes neuronales artificiales ; Classification of semiochemicals associated with coleoptera of the Polyphaga suborder by means of artificial neural networks

Autoři: Valencia Colman, Laura Sofía Daza Caicedo, Edgar Eduardo Grupo de Química Teórica

Přispěvatelé: Valencia Colman, Laura Sofía Daza Caicedo, Edgar Eduardo Grupo de Química Teórica

Témata: 540 - Química y ciencias afines, Descriptores Moleculares, Análisis de Componentes Principales, Bosques Aleatorios, Boruta-SHAP, C-means, Mapas Autoorganizados de Kohonen, Perceptrón Multicapa, Molecular Descriptors, Principal Component Analysis, Random Forests, Kohonen Self Organizing Maps, Multilayer Perceptron

Popis souboru: xii, 55 páginas; application/pdf

Relation: RedCol; LaReferencia; Abdi, H. & Williams, L. J. Principal component analysis Wiley interdisciplinary reviews: computational statistics, Wiley Online Library, 2010, 2, 433-459; Allison, J. D.; Borden, J. H. & Seybold, S. J. A review of the chemical ecology of the Cerambycidae (Coleoptera) Chemoecology, Springer, 2004, 14, 123-150; Allouche, A.-R. Gabedit—A graphical user interface for computational chemistry softwares Journal of computational chemistry, Wiley Online Library, 2011, 32, 174-182; Assembly, G. The International Committee on Bionomenclature, 2011; Bakthavatsalam, N. Semiochemicals Ecofriendly pest management for food security, Elsevier, 2016, 563-611; Bezdek, J. C.; Ehrlich, R. & Full, W. FCM: The fuzzy c-means clustering algorithm Computers & geosciences, Elsevier, 1984, 10, 191-203; Black, P. E. & others Algorithms and Theory of Computation Handbook Dictionary of algorithms and data structures, CRC Press LLC, 1999; Blomquist, G. & Vogt, R. (Eds.) Insect Pheromone Biochemistry and Molecular Biology, Elsevier Academic Press, 2003; Brownlee, J. Data preparation for machine learning: data cleaning, feature selection, and data transforms in Python, Machine Learning Mastery, 2020; Burns, J. A. & Whitesides, G. M. Feed-forward neural networks in chemistry: mathematical systems for classification and pattern recognition, Chemical Reviews, ACS Publications, 1993, 93, 2583-2601; Cai, D.; Zhang, C. & He, X. Unsupervised feature selection for multi-cluster data, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010, 333-342; Cebeci, Z.; Yildiz, F.; Kavlak, A.; Cebeci, C. & Onder, H. ppclust: Probabilistic and Possibilistic Cluster Analysis, R package version 0.1, 2019, 3; Chen, R.-C.; Dewi, C.; Huang, S.-W. & Caraka, R. E. Selecting critical features for data classification based on machine learning methods, Journal of Big Data, Springer, 2020, 7, 1-26; Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns, Front. Chem., 2019, 7, 809.; David, L.; Thakkar, A.; Mercado, R. & Engkvist, O. Molecular representations in AI-driven drug discovery: a review and practical guide, Journal of Cheminformatics, BioMed Central, 2020, 12, 1-22; Delgadillo, D. Feromonas. Lo que el viento se llevó, Revista Casa del Tiempo, 2005, 3; Dong, J.; Cao, D.-S.; Miao, H.-Y.; Liu, S.; Deng, B.-C.; Yun, Y.-H.; Wang, N.-N.; Lu, A.-P.; Zeng, W.-B. & Chen, A. F. ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation, Journal of cheminformatics, BioMed Central, 2015, 7, 60; Effrosynidis, D. & Arampatzis, A. An evaluation of feature selection methods for environmental data, Ecological Informatics, Elsevier, 2021, 61, 101224; El-Sayed, A. M. The pherobase: database of insect pheromones and semiochemicals, 2022; Ezzat, S. M.; Jeevanandam, J.; Egbuna, C.; Merghany, R. M.; Akram, M.; Daniyal, M.; Nisar, J. & Sharif, A. Semiochemicals: A Green Approach to Pest and Disease Control, Natural Remedies for Pest, Disease and Weed Control, Elsevier, 2020, 81-89; Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; Li, X.; Caricato, M.; Marenich, A. V.; Bloino, J.; Janesko, B. G.; Gomperts, R.; Mennucci, B.; Hratchian, H. P.; Ortiz, J. V.; Izmaylov, A. F.; Sonnenberg, J. L.; Williams-Young, D.; Ding, F.; Lipparini, F.; Egidi, F.; Goings, J.; Peng, B.; Petrone, A.; Henderson, T.; Ranasinghe, D.; Zakrzewski, V. G.; Gao, J.; Rega, N.; Zheng, G.; Liang, W.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Throssell, K.; Montgomery Jr., J. A.; Peralta, J. E.; Ogliaro, F.; Bearpark, M. J.; Heyd, J. J.; Brothers, E. N.; Kudin, K. N.; Staroverov, V. N.; Keith, T. A.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A. P.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Millam, J. M.; Klene, M.; Adamo, C.; Cammi, R.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Farkas, O.; Foresman, J. B. & Fox, D. J. Gaussian16 Revision C.01 2016; Genuer, R.; Poggi, J.-M. & Tuleau-Malot, C. Variable selection using random forests, Pattern recognition letters, Elsevier, 2010, 31, 2225-2236; Géron, A. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly Media, 2019; Ghosh, S. & Dubey, S. K. Comparative analysis of k-means and fuzzy c-means algorithms International, Journal of Advanced Computer Science and Applications, Citeseer, 2013, 4; Gitau, C.; Bashford, R.; Carnegie, A. & Gurr, G. A review of semiochemicals associated with bark beetle (Coleoptera: Curculionidae: Scolytinae) pests of coniferous trees: a focus on beetle interactions with other pests and their associates, Forest Ecology and Management, Elsevier, 2013, 297, 1-14; Grinberg, M. Flask web development: developing web applications with python, O'Reilly Media, Inc., 2018; Janet, J. P. & Kulik, H. J. Machine Learning in Chemistry, American Chemical Society, 2020; Jaramillo-Noreña, J.; León, G. D. S.; Rodríguez, V. P.; Garzón, D. Q.; Cuartas, M. Á. Z. & Arroyave, M. G. Capitulo 7: Manejo integrado de plagas, Tecnología para el cultivo de tomate bajo condiciones protegidas, Corporación Colombiana de Investigación Agropecuaria, 2013; Judd, W. S.; Campbell, C. S.; Kellogg, E. A.; Stevens, P. F. & Donoghue, M. J. Plant systematics: a phylogenetic approach, Ecología mediterranea, 1999, 25, 215; Kalousis, A.; Prados, J. & Hilario, M. Stability of feature selection algorithms: a study on high-dimensional spaces, Knowledge and information systems, Springer, 2007, 12, 95-116; Kasabov, N. K. Time-space, spiking neural networks and brain-inspired artificial intelligence, Springer, 2019; Kassambara, A. Multivariate Analysis II: Practical Guide to Principal Component Methods in R, Scotts Valley, CA: CreateSpace Independent Publishing Platform.[Google Scholar], 2017; Kassambara, A. Practical guide to principal component methods in R: PCA, M (CA), FAMD, MFA, HCPC, factoextra, Sthda, 2017, 2; Keany, E. BorutaShap, MIT License, 2021; Kohonen, T. Self-organized formation of topologically correct feature maps, Biological cybernetics, Springer, 1982, 43, 59-69; Kohonen, T. The self-organizing map, Proceedings of the IEEE, IEEE, 1990, 78, 1464-1480; Kramer, O. Scikit-learn, Machine learning for evolution strategies, Springer, 2016, 45-53; Kuhn, M. & Johnson, K. Feature engineering and selection: A practical approach for predictive models, CRC Press, 2019; Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package, Journal of statistical software, 2010, 36, 1-13; Kuzma, T. & Farkaš, I. Embedding Complexity of Learned Representations in Neural Networks, International Conference on Artificial Neural Networks, 2019, 518-528; Lal, T. N.; Chapelle, O.; Weston, J. & Elisseeff, A. Embedded methods, Feature extraction, Springer, 2006, 137-165; Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions, Advances in neural information processing systems, 2017, 30; Maltarollo, V. G.; Honório, K. M. & da Silva, A. B. F. Suzuki, K. (Ed.) Applications of Artificial Neural Networks in Chemical Problems, Chap.10, Artificial Neural Networks, IntechOpen, 2013; Mauri, A.; Consonni, V. & Todeschini, R. Molecular descriptors, Springer International Publishing, 2017; McHugh, M. L. Interrater reliability: the kappa statistic, Biochemia medica, Medicinska naklada, 2012, 22, 276-282; Mishra, S. S.; Shroff, S.; Sahu, J. K.; Naik, P. P. & Baitharu, I. Insect Pheromones and Its Applications in Management of Pest Population, Natural Materials and Products from Insects: Chemistry and Applications, Springer, 2020, 121-136; Mitchell, T. M. & others Machine learning, McGraw-hill New York, 1997; Moretto Cosson, K. y. A. Pollination of Amorphophallus barthlottii and A. abyssinicus subsp. akeassii (Araceae) by dung beetles (Insecta: Coleoptera: Scarabaeoidea), Association Catharsius, 2019; Morgan, E. D. Biosynthesis in insects, Royal society of chemistry, 2010; Moriwaki, H.; Tian, Y.-S.; Kawashita, N. & Takagi, T. Mordred: a molecular descriptor calculator, Journal of cheminformatics, BioMed Central, 2018, 10, 1-14; Mustaqeem, A.; Anwar, S. M. & Majid, M. Multiclass classification of cardiac arrhythmia using improved feature selection and SVM invariants, Computational and mathematical methods in medicine, Hindawi, 2018, 2018; MySQL, A. MySQL, 2001; Odell, S. G.; Lazo, G. R.; Woodhouse, M. R.; Hane, D. L. & Sen, T. Z. The art of curation at a biological database: principles and application, Current Plant Biology, Elsevier, 2017, 11, 2-11; Pardo-Locarno, L.; González, J.; Pérez, C.; Yepes, F. & Fernández, C. Escarabajos de importancia agrícola (Coleoptera: Melolonthidae) en la región Caribe colombiana: Registros y propuestas de manejo, Boletín Del Museo Entomológico Francisco Luis Gallejo, 2012, 4, 7-23; Peterson, M. A.; Dobler, S.; Larson, E. L.; Juárez, D.; Schlarbaum, T.; Monsen, K. J. & Francke, W. Profiles of cuticular hydrocarbons mediate male mate choice and sexual isolation between hybridising Chrysochus (Coleoptera: Chrysomelidae), Chemoecology, Springer, 2007, 17, 87-96; Piñeiro Gomez, J. Diseño de bases de datos relacionales, Ediciones Paraninfo, SA, 2014; Pla, L. Análisis multivariado: método de componentes principales, OEA (Organizacion de los Estados Americanos), 1986; Raices, M. Comunicación química en larvas de Rhinella arenarum. Caracterización del comportamiento antipredatorio y de las señales de alarma, Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales, 2018; Raju, M.; Gopi, V. P. & Anitha, V. Multi-class Classification of Alzheimer's Disease using 3DCNN Features and Multilayer Perceptron, 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2021, 368-373; Romero-Frías, A.; Murata, Y.; Simões Bento, J. M. & Osorio, C. (1R, 2S, 6R)-Papayanal: a new male-specific volatile compound released by the guava weevil Conotrachelus psidii (Coleoptera: Curculionidae), Bioscience, Biotechnology, and Biochemistry, Oxford University Press, 2016, 80, 848-855; Romero-Frías, A. A.; Sinuco, D. C. & Bento, J. M. S. Male-specific volatiles released by the big avocado seed weevil Heilipus lauri Boheman (Coleoptera: Curculionidae), Journal of the Brazilian Chemical Society, SciELO Brasil, 2019, 30, 158-163; Romero-López, A. A.; Reyes-Chilpa, R.; Pérez-Flores, F. J.; Lugo-García, G. A. & Maldonado-Rodríguez, J. I. Chemicals in the Genital Chamber of Two Mexican Species of Phyllophaga, Southwestern Entomologist, Society of Southwestern Entomologists, 2019, 44, 457 - 464; Samuel, A. L. Some studies in machine learning using the game of checkers, IBM Journal of research and development, IBM, 1959, 3, 210-229; Sandri, M. & Zuccolotto, P. Variable selection using random forests, Data analysis, classification and the forward search, Springer, 2006, 263-270; Sharma, A.; Sandhi, R. K. & Reddy, G. V. A Review of Interactions between Insect Biological Control Agents and Semiochemicals, Insects, Multidisciplinary Digital Publishing Institute, 2019, 10, 439; Shibata, E.; Sato, S.; Sakuratani, Y.; Sugimoto, T.; Kimura, F. & Ito, F. Cerambycid beetles (Coleoptera) lured to chemicals in forests of Nara Prefecture, central Japan, Annals of the Entomological Society of America, Oxford University Press Oxford, UK, 1996, 89, 835-842; Silva, W. D.; Millar, J. G.; Hanks, L. M.; Costa, C. M.; Leite, M. O.; Tonelli, M. & Bento, J. M. S. Interspecific cross-attraction between the South American cerambycid beetles Cotyclytus curvatus and Megacyllene acuta is averted by minor pheromone components, Journal of chemical ecology, Springer, 2018, 44, 268-275; Smart, L.; Aradottir, G. & Bruce, T. Role of semiochemicals in integrated pest management, Integrated Pest Management, Elsevier, 2014, 93-109; Sneath, P. H. Thirty years of numerical taxonomy, Systematic Biology, Society of Systematic Biologists, 1995, 44, 281-298; Solorio-Fernández, S.; Carrasco-Ochoa, J. A. & Mart\inez-Trinidad, J. F. A review of unsupervised feature selection methods, Artificial Intelligence Review, Springer, 2020, 53, 907-948; Spurlock, J. Bootstrap: responsive web development, O'Reilly Media, Inc., 2013; Stanczyk, S.; Champion, B. & Leyton, R. Theory and practice of relational databases, CRC Press, 2003; Syakur, M.; Khotimah, B.; Rochman, E. & Satoto, B. D. Integration k-means clustering method and elbow method for identification of the best customer profile cluster, IOP conference series: materials science and engineering, 2018, 336, 012017; Tauler, R.; Walczak, B. & Brown, S. D. Comprehensive chemometrics: chemical and biochemical data analysis, Elsevier, 2009; Thai, V. D.; Hoan, N. Q. & others Improving Feature Map Quality of SOM Based on Adjusting the Neighborhood Function, International Journal of Computer Science and Information Security, LJS Publishing, 2016, 14, 746; Todeschini, R. & Consonni, V. Handbook of molecular descriptors, John Wiley & Sons, 2008; Touzet, H. Tree edit distance with gaps, Information Processing Letters, Citeseer, 2003, 85, 123-129; Uriarte, E. A. & Mart\in, F. D. Topology preservation in SOM, International journal of applied mathematics and computer sciences, Citeseer, 2005, 1, 19-22; Vettigli, G. MiniSom: minimalistic and NumPy-based implementation of the Self Organizing Map, 2013; Vidal Medina, V. Señales químicas entre el escarabajo-plaga Strategus aloeus (Coleoptera: Scarabaeidae: Dynastinae) y la palma de aceite (Elaeis guineensis Jacq.), Universidad Nacional de Colombia, Universidad Nacional de Colombia, 2021, 120; Warner, J. & Sexauer, J. JDWarner/scikit-fuzzy: Scikit-Fuzzy version 0.4. 2 Nov, 2019; Wei, J. N.; Duvenaud, D. & Aspuru-Guzik, A. Neural networks for the prediction of organic chemistry reactions, ACS central science, ACS Publications, 2016, 2, 725-732; https://repositorio.unal.edu.co/handle/unal/82182; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

Dostupnost: https://repositorio.unal.edu.co/handle/unal/82182
https://repositorio.unal.edu.co/

no0 (0000), 1–9 NUMERICAL MODELING OF BUOYANT TURBULENT MIXING LAYERS

Autoři: Ac. Bennis T. Chacón Rebollo The Pennsylvania State University CiteSeerX Archives

Přispěvatelé: Ac. Bennis T. Chacón Rebollo The Pennsylvania State University CiteSeerX Archives

Zdroj: http://perso.univ-rennes1.fr/roger.lewandowski/Bol_Sema.pdf.

Témata: Clasificación por materias AMS, 0123 1234

Popis souboru: application/pdf

Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.573.9957

Dostupnost: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.573.9957
http://perso.univ-rennes1.fr/roger.lewandowski/Bol_Sema.pdf

Algoritmo para la clasificación en línea de fonemas de habla silenciosa utilizando un sistema embebido ; Algorithm for online classification of silent speech phonemes using an embedded system

Autoři: Caballero López, Julian David Bacca Rodríguez, Jan Villamizar Delgado, Sergio Iván a další

Přispěvatelé: Caballero López, Julian David Bacca Rodríguez, Jan Villamizar Delgado, Sergio Iván a další

Témata: 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores, Procesamiento Automatizado de Datos, Electronic Data Processing, EMD, MEMD, SVD, PLV, LDA, MULTICLASS CLASSIFIERS, CLASIFICADORES MULTICLASE, Programación informática, Computer programming

Popis souboru: 66 páginas; application/pdf

Relation: RedCol; LaReferencia; S. U. Arias, “Principales retos del Derecho Internacional Humanitario (DIH) en el contexto de postconflicto en Colombia,” 2019.; “Víctimas de Minas Antipersonal y Municiones sin Explosionar.” http://www.accioncontraminas.gov.co/estadisticas/Paginas/victimas-minasantipersonal.aspx (accessed Feb. 10, 2020).; Kim. Norton, “A brief history of prosthetics,” InMotion, vol. 17, no. 7, pp. 11-3- undefined, 2007.; S. Daniela García and V. María José Espinoza, “Avances en prótesis: una mirada al presente y al futuro,” Revista Médica Clínica Las Condes, vol. 25, no. 2, pp. 281– 285, Mar. 2014, doi:10.1016/S0716-8640(14)70039-2.; “History %7C AOPA – AMERICAN ORTHOTIC & PROSTHETIC ASSOCIATION.” https://www.aopanet.org/about-aopa/history/ (accessed Jul. 11, 2021).; J. L. Brito, M. X. Quinde, D. Cuzco, and J. I. Calle, “Estudio del estado del arte de las prótesis de mano,” 2013, Accessed: Jul. 11, 2021. [Online]. Available: http://dspace.ups.edu.ec/handle/123456789/8447; H. A. ROMO., J. C. REALPE, and P. E. JOJOA, “ANÁLISIS DE SEÑALES EMG SUPERFICIALES Y SU APLICACIÓN EN CONTROL DE PRÓTESIS DE MANO,” Avances en Sistemas e Informática, vol. 4, no. 1, Jan. 2007, Accessed: Jul. 11, 2021. [Online]. Available: https://revistas.unal.edu.co/index.php/avances/article/view/9725; A. A. Adewuyi, L. J. Hargrove, and T. A. Kuiken, “Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control,” Frontiers in Neurorobotics, vol. 10, p. 15, Oct. 2016, doi:10.3389/FNBOT.2016.00015.; E. Park and S. G. Meek, “Fatigue compensation of the electromyographic signal for prosthetic control and force estimation,” IEEE Transactions on Biomedical Engineering, vol. 40, no. 10, 1993, doi:10.1109/10.247800.; A. Kübler, V. K. Mushahwar, L. R. Hochberg, and J. P. Donoghue, “BCI Meeting 2005 - Workshop on clinical issues and applications,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 131–134, Jun. 2006, doi:10.1109/TNSRE.2006.875585.; A.-B. Suleiman, T. A. Fathi, A.-B. R. Suleiman, and A.-H. Fatehi, “FEATURES EXTRACTION TECHNIQES OF EEG SIGNAL FOR BCI APPLICATIONS FEATURES EXTRACTION TECHNIQES OF EEG SIGNAL FOR BCI APPLICATIONS”, Accessed: Jul. 11, 2021. [Online]. Available: https://www.researchgate.net/publication/228450475; C. Park, D. Looney, N. ur Rehman, A. Ahrabian, and D. P. Mandic, “Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 1, Jan. 2013, doi:10.1109/TNSRE.2012.2229296.; M. Rohm et al., “Hybrid brain–computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury,” Artificial Intelligence in Medicine, vol. 59, no. 2, Oct. 2013, doi:10.1016/j.artmed.2013.07.004.; F. Cincotti et al., “Non-invasive brain–computer interface system: Towards its application as assistive technology,” Brain Research Bulletin, vol. 75, no. 6, Apr. 2008, doi:10.1016/j.brainresbull.2008.01.007.; J. H. Friedman, “Multivariate Adaptive Regression Splines,” https://doi.org/10.1214/aos/1176347963, vol. 19, no. 1, pp. 1–67, Mar. 1991, doi:10.1214/AOS/1176347963.; N. Rehman and D. P. Mandic, “Multivariate empirical mode decomposition,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 466, no. 2117, May 2010, doi:10.1098/rspa.2009.0502.; A. Hemakom, V. Goverdovsky, D. Looney, and D. P. Mandic, “Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain–computer interface applications,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, no. 2065, Apr. 2016, doi:10.1098/rsta.2015.0199.; S. Zhao and F. Rudzicz, “Classifying phonological categories in imagined and articulated speech,” ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2015-August, pp. 992–996, Aug. 2015, doi:10.1109/ICASSP.2015.7178118.; S. I. Villamizar Delgado, “Development of algorithms to improve the technical efficiency of capturing, processing, and identification of EEG signals in the word imagery task,” Feb. 2020, Accessed: Jul. 11, 2021. [Online]. Available: https://repositorio.unal.edu.co/handle/unal/77829; N. E. Huang et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998, doi:10.1098/RSPA.1998.0193.; Z. Wu and N. E. Huang, “A study of the characteristics of white noise using the empirical mode decomposition method,” Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 460, no. 2046, pp. 1597–1611, Jun. 2004, doi:10.1098/RSPA.2003.1221.; G. Rilling, P. Flandrin, and P. Gonçalvès, “ON EMPIRICAL MODE DECOMPOSITION AND ITS ALGORITHMS”.; G. Rilling, P. Flandrin, P. Goncalves, and J. M. Lilly, “Bivariate empirical mode decomposition,” IEEE Signal Processing Letters, vol. 14, no. 12, pp. 936–939, Dec. 2007, doi:10.1109/LSP.2007.904710.; N. Ur Rehman and D. P. Mandic, “Empirical mode decomposition for trivariate signals,” IEEE Transactions on Signal Processing, vol. 58, no. 3 PART 1, pp. 1059– 1068, Mar. 2010, doi:10.1109/TSP.2009.2033730.; J. Fleureau, A. Kachenoura, L. Albera, J. C. Nunes, and L. Senhadji, “Multivariate empirical mode decomposition and application to multichannel filtering,” Signal Processing, vol. 91, no. 12, pp. 2783–2792, Dec. 2011, doi:10.1016/J.SIGPRO.2011.01.018.; X. Lang et al., “Fast Multivariate Empirical Mode Decomposition,” IEEE Access, vol. 6, pp. 65521–65538, 2018, doi:10.1109/ACCESS.2018.2877150.; H. C. Andrews and C. L. Patterson, “Singular Value Decomposition (SVD) Image Coding,” IEEE Transactions on Communications, vol. 24, no. 4, pp. 425–432, 1976, doi:10.1109/TCOM.1976.1093309.; C. Moore, “APPLICATION OF SINGULAR VALUE DECOMPOSITION TO THE DESIGN, ANALYSIS, AND CONTROL OF INDUSTRIAL PROCESSES.,” Proceedings of the American Control Conference, pp. 643–650, 1986, doi:10.23919/ACC.1986.4789019.; R. Izmailov, D. Bassu, A. McIntosh, L. Ness, and D. Shallcross, “Application of multiscale singular vector decomposition to vessel classification in overhead satellite imagery,” Seventh International Conference on Digital Image Processing (ICDIP 2015), vol. 9631, p. 963108, Jul. 2015, doi:10.1117/12.2196925.; “Understanding Singular Value Decomposition and its Application in Data Science %7C by Reza Bagheri %7C Towards Data Science.” https://towardsdatascience.com/understanding-singular-value-decomposition-andits-application-in-data-science-388a54be95d (accessed Jul. 11, 2021).; “Una introducción a los Árboles de Decisión %7C DABIA.” https://www.grupodabia.com/post/2020-05-19-arbol-de-decision/ (accessed Jul. 11, 2021).; “6 AdaBoost %7C Modelos Predictivos.” https://fhernanb.github.io/libro_mod_pred/adaboost.html (accessed Jul. 11, 2021).; M. Mandal and A. Mukhopadhyay, “An Improved Minimum Redundancy Maximum Relevance Approach for Feature Selection in Gene Expression Data,” Procedia Technology, vol. 10, pp. 20–27, Jan. 2013, doi:10.1016/J.PROTCY.2013.12.332.; S. Ramírez-Gallego et al., “Fast-mRMR: Fast Minimum Redundancy Maximum Relevance Algorithm for High-Dimensional Big Data,” International Journal of Intelligent Systems, vol. 32, no. 2, pp. 134–152, Feb. 2017, doi:10.1002/INT.21833.; P. Bugata and P. Drotar, “On some aspects of minimum redundancy maximum relevance feature selection,” Science China Information Sciences 2019 63:1, vol. 63, no. 1, pp. 1–15, Dec. 2019, doi:10.1007/S11432-019-2633-Y.; A. Atyabi, S. Fitzgibbon, and D. Powers, “The impact of Biasing on Overlapping windows : An EEG study,” Jul. 2012.; A. Atyabi, S. P. Fitzgibbon, and D. M. W. Powers, “Multiplication of EEG Samples through Replicating, Biasing, and Overlapping,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7670 LNAI, pp. 209–219, 2012, doi:10.1007/978-3-642-35139- 6_20.; A. Atyabi, S. Fitzgibbon, and D. M. W. Powers, “Biasing the overlapping and nonoverlapping sub-windows of EEG recording,” Jun. 2012. doi:10.1109/IJCNN.2012.6252465.; https://repositorio.unal.edu.co/handle/unal/82171; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

Dostupnost: https://repositorio.unal.edu.co/handle/unal/82171
https://repositorio.unal.edu.co/

Comprehensive Analysis of Classification Algorithms in Machine Learning: Perspectives, Comparisons and Applications ; Análisis Integral de Algoritmos de Clasificación en Aprendizaje Automático: Perspectivas, Comparaciones y Aplicaciones

Autoři: Romero Ibarra, José Luis

Zdroj: Serie Científica de la Universidad de las Ciencias Informáticas; Vol. 18 Núm. 1 (2025): Enero-Marzo; 183-204 ; 2306-2495

Popis souboru: application/pdf

Relation: https://publicaciones.uci.cu/index.php/serie/article/view/1807/1441; https://publicaciones.uci.cu/index.php/serie/article/view/1807

Dostupnost: https://publicaciones.uci.cu/index.php/serie/article/view/1807

Estudio comparativo de algoritmos de clasificación de imágenes basados en análisis de datos funcionales. Caso de estudio en superficies de fractura de elementos mecánicos ; Comparative study of image classification algorithms based on functional data analysis. Case study on fracture surfaces of mechanical elements

Autoři: Espejo Mora, Edgar Bohorquez Castañeda, Martha Patricia Estadística Espacial a další

Přispěvatelé: Espejo Mora, Edgar Bohorquez Castañeda, Martha Patricia Estadística Espacial a další

Témata: 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores, 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería, Aprendizaje supervisado, Fractografía, Análisis de datos funcionales, Autocorrelación espacial, Supervised learning, Fractography, Functional Data Analysis, Spatial autocorrelation, classification algorithm, artificial neural network, strength of materials, machine learning, red neuronal artificial, resistencia de materiales, aprendizaje automático

Popis souboru: xii, 87 páginas; application/pdf

Relation: [Alonso et al., 2012] Andrés M. Alonso, David Casado, Juan Romo, Supervised classification for functional data: A weighted distance approach, Computational Statistics & Data Analysis, Volume 56, Issue 7, 2012, Pages 2334-2346, ISSN 0167-9473, https://doi.org/10.1016/j.csda.2012.01.013.; [Ashraf et al., 2012] Hemeida, Ashraf & Hassan, Moatamad. (2022). Image classification based deep learning: A Review. Aswan University Journal of Sciences and Technology. 2. 10.21608/aujst.2022.259887.; [Blanquero et al., 2019] Rafael Blanquero, Emilio Carrizosa, Asunción Jiménez-Cordero, Belén Martín-Barragán, Variable selection in classification for multivariate functional data, Information Sciences, Volume 481, 2019, Pages 445-462, ISSN 0020-0255, https://doi.org/10.1016/j.ins.2018.12.060.; [Chen et al., 2021] Chen, L.; Li, S.; Bai, Q.; Yang, J.; Jiang, S.; Miao, Y. Review of Image Classification Algorithms Based on Convolutional Neural Networks. Remote Sens. 2021, 13, 4712. https://doi.org/10.3390/rs13224712.; [Delaigle et al., 2012] Delaigle, Aurore & Hall, Peter. (2012). Achieving near Perfect Classification for Functional Data. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 74. 267 - 286. 10.1111/j.1467-9868.2011.01003.x.; [Dryden et al., 2009] Ian L. Dryden, Alexey Koloydenko, Diwei Zhou "Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging," The Annals of Applied Statistics, Ann. Appl. Stat. 3(3), 1102-1123, (September 2009); [Febrero et al., 2012] Febrero-Bande M, Oviedo de la Fuente M (2012). “Statistical Computing in Functional Data Analysis: The R Package fda.usc.” Journal of Statistical Software, 51(4), 1–28. https://www.jstatsoft.org/v51/i04/.; [Ghigliet et al., 2017] Ghiglietti, Andrea & Ieva, Francesca & Paganoni, Anna. (2017). Statistical inference for stochastic processes: Two-sample hypothesis tests. Journal of Statistical Planning and Inference. 180. 49-68. 10.1016/j.jspi.2016.08.004.; [Jiaohua et al., 2020] Jiaohua Qin, Wenyan Pan, Xuyu Xiang, Yun Tan, Guimin Hou, A biological image classification method based on improved CNN, Ecological Informatics, Volume 58, 2020, 101093, ISSN 1574-9541, https://doi.org/10.1016/j.ecoinf.2020.101093.; [Krishna et al., 2018] krishna, M & Neelima, M & Mane, Harshali & Matcha, Venu. (2018). Image classification using Deep learning. International Journal of Engineering & Technology. 7. 614. 10.14419/ijet.v7i2.7.10892.; [Krizhevsky et al., 2012] Krizhevsky, Alex & Sutskever, Ilya & Hinton, Geoffrey. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems. 25. 10.1145/3065386.; [Kumar et al., 2012] Anil Kumar, C.P. Gandhi, Yuqing Zhou, Rajesh Kumar, Jiawei Xiang, Improved deep convolution neural network (CNN) for the identification of defects in the centrifugal pump using acoustic images, Applied Acoustics, Volume 167, 2020, 107399, ISSN 0003-682X, https://doi.org/10.1016/j.apacoust.2020.107399.; [Llop et al., 2008] P. Llop, L. Forzani, R. Fraiman, On local times, density estimation and supervised classification from functional data, Journal of Multivariate Analysis, Volume 102, Issue 1, 2011, Pages 73-86, ISSN 0047-259X, https://doi.org/10.1016/j.jmva.2010.08.002.; [Nilsback et al., 2008] M. -E. Nilsback and A. Zisserman, "Automated Flower Classification over a Large Number of Classes," 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, Bhubaneswar, India, 2008, pp. 722-729, doi:10.1109/ICVGIP.2008.47.; [Oviedo et al., 2011] Manuel Oviedo de la Fuente, Manuel Febrero Bande, Localización: X Congreso SGAPEIO, Congreso Galego de Estatística e Investigación de Operacións: Pazo de Congresos e Exposicións, Pontevedra, 3, 4 y 5 de novembro de 2011, 2011, ISBN 978-84-938642-2-4.; [Pedregosa et al., 2011] Pedregosa, F., Varoquaux, Ga"el, Gramfort, A., Michel, V., Thirion, B., Grisel, O., … others. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(Oct), 2825–2830.; [R Core Team] R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.; [Radhesyam et al., 2020] Radhesyam Vaddi, Prabukumar Manoharan, Hyperspectral image classification using CNN with spectral and spatial features integration, Infrared Physics & Technology, Volume 107, 2020, 103296, ISSN 1350-4495, https://doi.org/10.1016/j.infrared.2020.103296.; [Ramsay et al., 2005] J. O. Ramsay, B. W. Silverman, Functional Data Analysis, Springer, 2005, New York, ISBN 978-0-387-40080-8, https://doi.org/10.1007/b98888.; [Sabat-Tomala et al., 2020] Sabat-Tomala, A.; Raczko, E.; Zagajewski, B. Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data. Remote Sens. 2020, 12, 516. https://doi.org/10.3390/rs12030516; [Sheykhmousa et al., 2020] M. Sheykhmousa, M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, P. Ghamisi and S. Homayouni, "Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 6308-6325, 2020, doi:10.1109/JSTARS.2020.3026724.; [Shuoyang et al., 2021] Shuoyang Wang, Zuofeng Shang, Guanqun Cao and Jun S. Liu, Optimal classification for functional data, 2021, arXiv:2103.00569 [stat.ME]; [Van Rossum et al., 1995] Van Rossum G and Drake Jr FL. Python reference manual. Centrum voor Wiskunde en Informatica Amsterdam; 1995.; [Waqar et al., 2019] M. Waqar Akram, Guiqiang Li, Yi Jin, Xiao Chen, Changan Zhu, Xudong Zhao, Abdul Khaliq, M. Faheem, Ashfaq Ahmad, CNN based automatic detection of photovoltaic cell defects in electroluminescence images, Energy, Volume 189, 2019, 116319, ISSN 0360-5442, https://doi.org/10.1016/j.energy.2019.116319.; [Watanabe et al., 2019] Akihiko Watanabe, Naoto Hirose, Hyoungseop Kim, Ichiro Omura, Convolutional neural network (CNNs) based image diagnosis for failure analysis of power devices, Microelectronics Reliability, Volumes 100–101, 2019, 113399, ISSN 0026-2714, https://doi.org/10.1016/j.microrel.2019.113399.; [Xinyu et al., 2023] Xinyu Huang, Ziyang Pan, A Functional Data Classification Model Utilizing Functional Mahalanobis Distance and Regenerative Kernel Methods. Journal of Electronics and Information Science (2023) Vol. 8: 104-110. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2023.080613.; [Yeonjoo et al., 2019] Yeonjoo Park, Douglas G. Simpson, Robust probabilistic classification applicable to irregularly sampled functional data, Computational Statistics & Data Analysis, Volume 131, 2019, Pages 37-49, ISSN 0167-9473, https://doi.org/10.1016/j.csda.2018.08.001.; [Yinfeng et al., 2016] Yinfeng Meng, Jiye Liang, Yuhua Qian, Comparison study of orthonormal representations of functional data in classification, Knowledge-Based Systems, Volume 97, 2016, Pages 224-236, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2015.12.016.; [Zhao et al., 2020] Yudi Zhao, Kuangrong Hao, Haibo He, Xuesong Tang, Bing Wei, A visual long-short-term memory based integrated CNN model for fabric defect image classification, Neurocomputing, Volume 380, 2020, Pages 259-270, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2019.10.067.; https://repositorio.unal.edu.co/handle/unal/86524; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

Dostupnost: https://repositorio.unal.edu.co/handle/unal/86524
https://repositorio.unal.edu.co/

Clasificación de series de tiempo con datos funcionales y técnicas de Machine Learning: una aproximación para el Índice de Desarrollo Humano

Autoři: Rivera Gómez, Fredy Alexander Pineda Rios, Wilmer orcid:0000-0001-7774-951X a další

Přispěvatelé: Rivera Gómez, Fredy Alexander Pineda Rios, Wilmer orcid:0000-0001-7774-951X a další

Témata: Classification, FGLM, FGSAM, FGKAM, FPCA, Time Series, Functional Data, Functional Machine Learning, Human Development, Functional Principal Component Analysis, Estadística, Estadística Aplicada, Datos Estadísticos, Clasificación, Machine learning funcional, ACPF, Series De Tiempo, Datos Funcionales, Desarrollo Humano, Análisis De Componentes Principales Funcionales

Geografické téma: CRAI-USTA Bogotá

Popis souboru: application/pdf

Relation: Akinbobola, T. O. and Saibu, M. O. O. (2004). Income inequality, unemployment, and poverty in nigeria: a vector autoregressive approach. The Journal of Policy Reform, 7(3):175– 183.; Bagnall, A., Lines, J., Bostrom, A., Large, J., and Keogh, E. (2017). The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data mining and knowledge discovery, 31(3):606–660.; Bagnall, A., Lines, J., Hills, J., and Bostrom, A. (2015). Time-series classification with cote: The collective of transformation-based ensembles. IEEE Transactions on Knowledge and Data Engineering, 27(9):2522–2535.; Buhlmann, P. and Hothorn, T. (2007). Boosting algorithms: Regularization, prediction and model fitting.; de Boor, C. (1978). A Practical Guide to Spline, volume Volume 27.; de las naciones unidas, O. (2021). Población.; Deprez, P., Shevchenko, P. V., and W¨uthrich, M. V. (2017). Machine learning techniques for mortality modeling. European Actuarial Journal, 7(2):337–352.; DEVBAY. (2020). Time series classification – an overview.; Faouzi, J. (2022). Time series classification: A review of algorithms and implementations. Machine Learning (Emerging Trends and Applications).; Febrero-Bande, M. and Gonz˜alez-Manteiga, W. (2011). Generalized Additive Models for Functional Data, volume 22, pages 91–96.; Febrero-Bande, M. and Oviedo de la Fuente, M. (2012). Statistical computing in functional data analysis: The R package fda.usc. Journal of Statistical Software, 51(4):1–28.; Golovkine, S. (2021). M´ethodes statistiques pour donn´ees fonctionnelles multivari´ees. PhD thesis.; Grenander, U. (1950). Stochastic processes and statistical inference. Arkiv f¨or matematik, 1(3):195–277.; Hernández-Sampieri, R. and Torres, C. P. M. (2018). Metodología de la investigación, volume 4. McGraw-Hill Interamericana Méxicoˆ eD. F DF.; Hyndman, R. J. and Booth, H. (2008). Stochastic population forecasts using functional data models for mortality, fertility and migration. International Journal of Forecasting, 24(3):323–342.; Hyndman, R. J. and Shang, H. L. (2009). Forecasting functional time series. Journal of the Korean Statistical Society, 38(3):199–211.; Khan, Y. A., Abbas, S. Z., and Truong, B.-C. (2020). Machine learning-based mortality rate prediction using optimized hyper-parameter. Computer Methods and Programs in Biomedicine, 197:105704.; Kokoszka, P. and Reimherr, M. (2017). Introduction to functional data analysis. Chapman and Hall/CRC.; Lee, R. (2000). The lee-carter method for forecasting mortality, with various extensions and applications. North American actuarial journal, 4(1):80–91.; Lee, R. D. and Carter, L. R. (1992). Modeling and forecasting u.s. mortality. Journal of the American Statistical Association, 87(419):659–671.; López, J. F. (2019). Guía para calcular e interpretar el IDH.; Maierhofer, T. and Pfisterer, F. (2017). Classifunc: classification of functional data. R package version 0.1. 0, URL https://CRAN. R-project. org/package= classiFunc.; Mark, R. (2018). Python implementation of KNN and DTW classification algorithm.; Medina, F. (2001). Consideraciones sobre el índice de gini. Technical report, Naciones unidas.; Moller, A., Tutz, G., and Gertheiss, J. (2016). Random forests for functional covariates. Journal of Chemometrics, 30(12):715–725.; Mundial, B. (2021). Esperanza de vida al nacer, total (años).; Mundial, G. B. (2023). Datos de libre acceso del banco mundial.; Pfisterer, F., Beggel, L., Sun, X., Scheipl, F., and Bischl, B. (2019). Benchmarking time series classification–functional data vs machine learning approaches. arXiv preprint ar- Xiv:1911.07511.; PNUD (2019). Informe sobre desarrollo humano 2019. Technical report, Programa de las Naciones Unidas para el Desarrollo.; PNUD (2020). Informe sobre desarrollo humano 2020. Technical report, Programa de las Naciones Unidas para el Desarrollo.; Programme, U. N. D. (2023). HUMAN DEVELOPMENT DATA.; Ramsay, J. O. (1982). When the data are functions. Psychometrika, 47(4):379–396.; Ramsay, J. O. and Silverman, B. W. (2005). Functional data analysis. Springer Series in Statistics, New York: Springer Verlag.; Rao, C. R. (1958). Some statistical methods for comparison of growth curves. Biometrics, 14(1):1–17.; RCODER. (2023). Listas en R.; Rossi, F. and Villa, N. (2006). Support vector machine for functional data classification. Neurocomputing, 69(7):730–742. New Issues in Neurocomputing: 13th European Symposium on Artificial Neural Networks.; Shang, H. L. (2015). Statistically tested comparisons of the accuracy of forecasting methods for age-specific and sex-specific mortality and life expectancy. Population Studies, 69(3):317–335.; Shang, H. L. (2016). Mortality and life expectancy forecasting for a group of populations in developed countries: a multilevel functional data method. The Annals of Applied Statistics, 10(3):1639–1672.; Shang, H. L., Smith, P. W., Bijak, J., and Wisniowski, A. (2013). A functional data analysis approach for forecasting population: a case study for the united kingdom.; Swamynathan, M. (2019). Mastering machine learning with python in six steps: A practical implementation guide to predictive data analytics using python. Apress.; Wang, J.-L., Chiou, J.-M., and Muller, H.-G. (2015). Review of functional data analysis. arXiv preprint arXiv:1507.05135.; Rivera Gómez, F. A. (2023). Clasificación de series de tiempo con datos funcionales y técnicas de Machine Learning: una aproximación para el Índice de Desarrollo Humano. [Trabajo de Maestría, Universidad Santo Tomás]. Repositorio Institucional.; http://hdl.handle.net/11634/53659; reponame:Repositorio Institucional Universidad Santo Tomás; instname:Universidad Santo Tomás; repourl:https://repository.usta.edu.co

Dostupnost: http://hdl.handle.net/11634/53659

Evaluación del campo de esfuerzos mediante el análisis, descripción y clasificación de la dinámica temporal de secuencias de imágenes de fotoelasticidad ; Stress field evaluation by the analysis, description, and classification of temporal dynamics in photoelasticity image sequences

Autoři: Briñez de león, Juan Carlos Restrepo Martínez, Alejandro Branch Bedoya, John William a další

Přispěvatelé: Briñez de león, Juan Carlos Restrepo Martínez, Alejandro Branch Bedoya, John William a další

Témata: 000 - Ciencias de la computación, información y obras generales::003 - Sistemas, Digital photoelasticity, Birefringence, Color fringe patterns, Stress field, Digital image sequence processing, Pattern recognition, Computational hybrid methods, Fotoelasticidad digital, Birrefringencia, Patrones de franjas de color, Campo de esfuerzos, Procesamiento digital de secuencias de imágenes, Reconocimiento de patrones, Métodos híbridos computacionales

Popis souboru: application/pdf

Relation: Briñez-de León, J. C., Restrepo-Martínez, A., & Branch-Bedoya, J. W. (2019). Computational analysis of Bayer colour filter arrays and demosaicking algorithms in digital photoelasticity. Optics and Lasers in Engineering, 122, 195-208.; Toro, H. F., Briñez-de León, J. C., Martinez, A. R., & Bedoya, J. W. B. (2018). Fringe patterns recognition in digital photoelasticity images using texture features and multispectral wavelength analysis. Optical Engineering, 57(9), 093105.; Briñez-de León, J. C., Alejandro Restrepo Martínez, John W. Branch, (2018). Computational hybrid phase shifting technique applied to digital photoelasticity, In Optik - International Journal for Light and Electron Optics, Volume 157, Pages 287-297, ISSN 0030-4026; Pérez, U., Camilo, J., Motta, G. C., Briñez-de León, J. C., & Restrepo-Martínez, A. (2017). Validación del uso de fotoelasticidad como herramienta para los cursos de Mecánica de Sólidos. Revista EIA, 14(28), 117-131; Briñez-de León, J. C.; Fandiño Toro, Hermes A; Restrepo Martínez, Alejandro; Branch Bedoya, John W., (2017). Análisis de resolución en imágenes de fotoelasticidad: caso carga dinámica. Visión Electrónica. Vol 1. No. 1, Universidad Distrital Francisco José Caldas; Fandiño Toro, Hermes A; Briñez-de León, J. C.; Restrepo Martínez, Alejandro; Branch Bedoya, John W., (2017). Análisis de campos de esfuerzos utilizando fotoelasticidad visible e infrarroja. Visión Electrónica. Vol 1. No. 1, Universidad Distrital Francisco José Caldas; Briñez-de León, J. C., Alejandro Restrepo, John W. Branch y Carlos Madrigal. Desenvolvimiento de fase RGB aplicado a secuencias de imágenes de fotoelasticidad capturadas de la tracción de películas plásticas. XIV Encuentro Nacional De Óptica V Conferencia Andina y del Caribe En Óptica y sus Aplicaciones ENO - CANCOA 2015. Cali - Colombia. 16-20 de Noviembre de 2015; Briñez-de León, J. C., Alejandro Restrepo, John W. Branch. Evaluación Temporal de los Ordenes de Franjas de Color Utilizando Análisis de Saturación en Secuencias de Imágenes de Fotoelasticidad. Décimo segundo Congreso Iberoamericano de Ingeniería Mecánica (CIBIM XII- 2015), Guayaquil-Ecuador. Noviembre 10-13 de 2015; Fernando Melendez, Briñez-de León, J. C., Alejandro Restrepo, John W. Branch. Identificación de variaciones del efecto de la temperatura en la deformación de películas plásticas analizando el comportamiento temporal de la fotoelasticidad. XIV Encuentro Nacional De Óptica V Conferencia Andina y del Caribe En Óptica y sus Aplicaciones ENO - CANCOA 2015. Cali- Colombia. 16-20 de Noviembre de 2015; Briñez-de León, J. C., A. R. Martínez and J. W. B. Bedoya, "High stress concentration analysis using RGB intensity changes in dynamic photoelasticity videos," 2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA), Bucaramanga, 2016, pp. 1-7.doi:10.1109/STSIVA.2016.7743324; Briñez-de León, J. C., Alejandro Restrepo M.; John W. Branch; Time-space analysis in photoelasticity images using recurrent neural networks to detect zones with stress concentration. Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 99712P (September 28, 2016); doi:10.1117/12.2237373; Briñez-de León, J. C., Hermes Alexander Fandiño-Toro, Alejandro Restrepo-Martínez, John W. Branch. Evaluación de la pérdida de resolución en imágenes de fotoelasticidad debido al incremento de la carga. VIII Congreso Internacional de Ingeniería Mecánica y Mecatrónica y IV de Materiales, Energía y Medioambiente, Medellín, Colombia. 2017/4/26; Briñez-de León, J. C., D. A. Patiño Cortes, A. Restrepo Martínez, and J. W. Branch Bedoya, "Computational Detection of Salient Information to Identify High Stress and Ambiguity Regions in Digital Photoelasticity Images," in Imaging and Applied Optics 2017 (3D, AIO, COSI, IS, MATH, pcAOP), OSA Technical Digest (online) (Optical Society of America, 2017), paper IM4E.2; Briñez-de León, J. C., Alejandro Restrepo M., John W. Branch, "Computational reduction of the image sets required in conventional phase shifting methods applied to digital photoelasticity" Proc. SPIE 10395, Optics and Photonics for Information Processing XI, 103950K (24 August 2017); doi:10.1117/12.2273431; Hermes Fandiño Toro, Briñez-de León, J. C., Alejandro Restrepo Martínez, John W. Branch Bedoya, "Texture analysis integrated to infrared light sources for identifying high fringe concentrations in digital photoelasticity," Proc. SPIE 10396, Applications of Digital Image Processing XL, 103962D (19 September 2017); doi:10.1117/12.2273258; Juan Camilo Urango Pérez, Guillermo Carmen Motta, Briñez-de León, J. C., Alejandro Restrepo Martinez. Validation of the photoelasticity method as a tool for the enhancement of learning and design processes in solid mechanics. Congreso Internacional de Formación y Modelación en Ciencias Básicas. Universidad de Medellín. 2017. Página 217. ISBN-ebook: 978-958-8992-46-7; Briñez-de León, J. C., H. A. Fandiño Toro, A. Restrepo M, and J. W. Branch, "Bayer and demosaicking effect for imaging the stress field in digital photoelasticity," in Imaging and Applied Optics 2018 (3D, AO, AIO, COSI, DH, IS, LACSEA, LS&C, MATH, pcAOP), OSA Technical Digest (Optical Society of America, 2018), paper IW2B.3.; Briñez-de León, J. C., Fandiño, H. A., Restrepo, A., & Branch, J. W. (2018, September). Computational analysis of stress map variations by industrial light sources and load additions in digital photoelasticity. In Optics and Photonics for Information Processing XII (Vol. 10751, p. 107510G). International Society for Optics and Photonics; H. F. Toro, Briñez-de León, J. C., A. Restrepo Martínez, and J. W. Branch Bedoya, "Relevance analysis for texture descriptors in studies of dynamic photoelasticity," in Imaging and Applied Optics 2018 (3D, AO, AIO, COSI, DH, IS, LACSEA, LS&C, MATH, pcAOP), OSA Technical Digest (Optical Society of America, 2018), paper JM4A.37; Briñez-de León, J. C., Martínez, A. R., & Bedoya, J. W. B. (2019, June). Fast Fourier Transform as Color Variation Descriptor for Imaging the Stress Field from Photoelasticity Videos. In Imaging Systems and Applications (pp. JW2A-46). Optical Society of America; Toro, H. F., Briñez-de León, J. C., RestrepoMartínez, A., & Branch, J. W. (2019, June). Texture analysis for evaluating the Bayer and demosaicking effects in photoelasticity images. In Computational Optical Sensing and Imaging (pp. JW2A-50). Optical Society of America; Restrepo-Martinez, A., & Briñez-de León, J. C., (2019, September). Dynamic color descriptor based Frenet-Serret to classify stress zones from pixel variations recorded in photoelasticity videos. In Optics and Photonics for Information Processing XIII (Vol. 11136, p. 111360G). International Society for Optics and Photonics; Briñez-de León, J. C., Mery, D., Restrepo, A., & Branch, J. W. (2019, September). One-dimensional local binary pattern based color descriptor to classify stress values from photoelasticity videos. In Optics and Photonics for Information Processing XIII (Vol. 11136, p. 1113607). International Society for Optics and Photonics.; H. J. Jiménez, “Comportamiento mecánico y microestructural de la aleación AlMgSi para conductores eléctricos,” Rev. UIS Ing., vol. 18, no. 2, pp. 199–211, 2019.; S. Sazesh, A. Ghassemi, R. Ebrahimi, and M. Khodaei, “Experimental and Numerical Analysis of Titanium/HA FGM for Dental Implantation,” Int. J. Adv. Des. Manuf. Technol., vol. 10, no. 1, pp. 57–74, 2017.; K. Ramesh, “Experimental Stress Analysis,” J. Appl. Mech., vol. 33, no. 1, p. 237, 2011.; M. Akay and N. Aslan, “Numerical and experimental stress analysis of a polymeric composite hip joint prosthesis,” J. Biomed. Mater. Res., vol. 31, no. 2, pp. 167–182, 1996.; J. F. Doyle, Modern Experimental Stress Analysis: Completing the Solution of Partially Specified Problems. 2005.; K. Ramesh, T. Kasimayan, and B. Neethi Simon, “Digital photoelasticity - A comprehensive review,” J. Strain Anal. Eng. Des., vol. 46, no. 4, pp. 245–266, 2011.; J. C. Briñez, A. Restrepo, and F. López, “Métricas de similitud aplicadas para el análisis de imágenes de fotoelasticidad,” Dyna, vol. 80, no. 179, pp. 42–50, 2013.; J. C. Ye, Y. Han, and E. Cha, “Deep convolutional framelets: A general deep learning framework for inverse problems,” SIAM J. Imaging Sci., vol. 11, no. 2, pp. 991–1048, 2018.; K. Jin, M. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Trans. iIage Process., vol. 26, no. 9, pp. 4509–4522, 2017.; R. Montanini, M. Scafidi, G. Staiti, A. Marcianò, L. D’Acquisto, and G. Oteri, “Misfit evaluation of dental implant-supported metal frameworks manufactured with different techniques: Photoelastic and strain gauge measurements,” J. Eng. Med., vol. 230, no. 12, pp. 1106–1116, 2016.; C. C and E. Gabrielli, “Photoelasticity and DIC as optical techniques for monitoring masonry specimens under mechanical loads,” J. Phys. Conf. Ser., vol. 778, no. 1, pp. 1–14, 2017.; F. C. Li and A. Kishen, “Deciphering dentin tissue biomechanics using digital moiré interferometry: A narrative review,” Opt. Lasers Eng., vol. 107, no. March, pp. 273–280, 2018.; S. Yoneyama and S. Arikawa, “Instantaneous phase-stepping interferometry based on a pixelated micro-polarizer array,” Theor. Appl. Mech. Lett., vol. 6, no. 4, pp. 162–166, 2016.; C. S. Narayanamurthy, G. Pedrini, and W. Osten, “Digital holographic photoelasticity,” Appl. Opt., vol. 56, no. 13, pp. F213–F217, 2017.; R. Subramanyam and K. Ramesh, “Photoelastic study on the effect of flow induced residual stresses on fracture parameters,” Theor. Appl. Fract. Mech., vol. 85, pp. 320–327, 2016.; T. Nikova and E. Stoykova, “Design of a photoelastic measurement of principal stresses by a phase-shifting method,” Phys. Scr., vol. T162, no. January, pp. 1–5, 2014.; S. Alsiya, C. J. Lekshmi, B. P. J. Priya, and R. C. Mehta, “Image processing algorithm for fringe analysis in photoelasticity,” Sch. J. Eng. Technol., vol. 4, no. 7, pp. 325–328, 2016.; J. A. Quiroga and J. A. Gómez-Pedrero, “Application of principal component analysis in phase-shifting photoelasticity,” Opt. Express, vol. 24, no. 6, p. 5984, 2016.; D. Mishra, K. Muralidhar, and P. Munshi, “Performance evaluation of fringe thinning algorithms for interferometric tomography,” Opt. Lasers Eng., vol. 30, no. 3–4, pp. 229–249, 1998.; J. Carazo-Alvarez, S. J. Haake, and E. A. Patterson, “Completely automated photoelastic fringe analysis,” Opt. Lasers Eng., vol. 21, no. 3, pp. 133–149, 1994.; W. Shang, X. Ji, and X. Yang, “Study on several problems of automatic full-field isoclinic parameter measurement by digital phase shifting photoelasticity,” Optik., vol. 126, no. 19, pp. 1981–1985, 2015.; M. Ramji and K. Ramesh, “Whole field evaluation of stress components in digital photoelasticity-Issues, implementation and application,” Opt. Lasers Eng., vol. 46, no. 3, pp. 257–271, 2008.; M. Ramji and K. Ramesh, “A new six-step phase shifting technique using mixed-polariscope in digital photoelasticity,” Key Eng. Mater., vol. 326–328, pp. 35–38, 2009.; A. Ajovalasit, S. Barone, and G. Petrucci, “A method for reducing the influence of quarter-wave plate errors in phase stepping photoelasticity,” J. Strain Anal. Eng. Des., vol. 33, no. 3, pp. 207–216, 2002.; M. Solaguren-Beascoa Fernández, “Data Acquisition Techniques in Photoelasticity,” Exp. Tech., vol. 35, no. 6, pp. 71–79, 2011.; P. Magalhães, F. Vieira, C. Magalhães, J. Ribeiro, and I. Rios, “Numerical method to digital photoelasticity using plane polariscope,” Opt. Express, vol. 24, no. 12, p. 12617, 2016.; A. Ajovalasit, G. Petrucci, and M. Scafidi, “Measurement of edge residual stresses in glass by the phase-shifting method,” Opt. Lasers Eng., vol. 49, no. 5, pp. 652–657, 2011.; J. R. Lesniak, M. J. Zickel, C. S. Welch, and D. F. Johnson, “An innovative polariscope for photoelastic stress analysis,” Proc. Sem Spring Conf. Exp. Mech., pp. 219–224, 1997.; S. Yoneyama and K. Moriwaki, “Simultaneous observation of phase-stepped photoelastic fringes using a pixelated microretarder array,” Opt. Eng., vol. 45, no. 8, p. 083604, 2006.; E. Compain and B. Drevillon, “High-frequency modulation of the four states of polarization of light with a single phase modulator,” Rev. Sci. Instrum., vol. 69, no. 4, pp. 1574–1580, 1998.; S. Sircar and K. Bhattacharya, “Measurement of birefringence using polarization phase-shifting Mach–Zehnder interferometer,” Opt. Eng., vol. 54, no. 11, p. 113112, 2015.; K. Ashokan and K. Ramesh, “A novel approach for ambiguity removal in isochromatic phasemap in digital photoelasticity,” Meas. Sci. Technol., vol. 17, no. 11, pp. 2891–2896, 2006.; M. Ramji and K. Ramesh, “Adaptive quality guided phase unwrapping algorithm for whole-field digital photoelastic parameter estimation of complex models,” Strain, vol. 46, no. 2, pp. 184–194, 2010.; P. Siegmann, F. Díaz-Garrido, and E. A. Patterson, “Robust approach to regularize an isochromatic fringe map,” Appl. Opt., vol. 48, no. 22, p. E24, 2009.; P. Pinit, “Automated Detection of Singularities from Orientation Map of Isoclinics in,” 21st Conf. Mech. Eng. Netw. Thail., no. October, 2007.; K. Ramesh, M. P. Hariprasad, and V. Ramakrishnan, “Robust multidirectional smoothing of isoclinic parameter in digital photoelasticity,” Opt. Eng., vol. 54, no. 8, p. 081205, 2015.; C. Buckberry and D. Towers, “New approaches to the full-field analysis of photoelastic stress patterns,” Opt. Lasers Eng., vol. 24, no. 5–6, pp. 415–428, 1996.; M.-J. Huang, “Isoclinic ambiguity unwrapping of circular ring under diametric dompression,” ICEM 14 – 14th Int. Conf. Exp. Mech., vol. 6, p. 32002, 2010.; J. Wu and M. Huang, “Isochromatic photoelastic phase map unwrapping: temporal versus spatial approach,” Opt. Eng., vol. 54, no. 8, p. 081207, 2015.; K. Ramesh and D. K. Tamrakar, “Improved determination of retardation in digital photoelasticity by load stepping,” Opt. Lasers Eng., vol. 33, no. 6, pp. 387–400, 2000.; A. D. Nurse, “Load-stepping photoelasticity: New developments using temporal phase unwrapping,” Opt. Lasers Eng., vol. 38, no. 1–2, pp. 57–70, 2002.; V. Ramakrishnan and K. Ramesh, “Scanning schemes in white light Photoelasticity – Part I: Critical assessment of existing schemes,” Opt. Lasers Eng., vol. 92, pp. 129–140, 2017.; V. Ramakrishnan and K. Ramesh, “Scanning schemes in white light photoelasticity – Part II: Novel fringe resolution guided scanning scheme,” Opt. Lasers Eng., vol. 92, pp. 141–149, 2017.; M. P. Haripras and K. Ramesh, “Analysis of contact zones from whole field isochromatics using reflection photoelasticity,” Opt. Lasers Eng., vol. 105, no. September 2017, pp. 86–92, 2018.; K. Ramesh and A. Pandey, “An improved normalization technique for white light photoelasticity,” Opt. Lasers Eng., vol. 109, no. February, pp. 7–16, 2018.; B. N. Simon, T. Kasimayan, and K. Ramesh, “The influence of ambient illumination on colour adaptation in three fringe photoelasticity,” Opt. Lasers Eng., vol. 49, no. 2, pp. 258–264, 2011.; K. Ramesh, M. P. Hariprasad, and S. Bhuvanewari, “Digital photoelastic analysis applied to implant dentistry,” Opt. Lasers Eng., vol. 87, pp. 204–213, 2016.; A. Pandey and K. Ramesh, “Development of a new normalization technique for twelve fringe photoelasticity (TFP),” Conf. Proc. Soc. Exp. Mech. Ser., vol. 12, pp. 177–180, 2019.; J. A. Quiroga, M. Servin, and J. L. Marroquin, “Regularized phase tracking technique for demodulation of isochromatics from a single tricolour image,” Meas. Sci. Technol., vol. 13, no. 1, pp. 132–140, 2002.; G. S. Grewal and V. N. Dubey, “Inverse problem of photoelastic fringe mapping using neural networks,” Meas. Sci. Technol., vol. 18, no. 5, pp. 1361–1366, 2007.; L. Roy and A. J. Rosakis, “An experimental study of impact-induced failure events in homogeneous layered materials using dynamic photoelasticity and high-speed photography,” Opt. Lasers Eng., vol. 40, no. 4, pp. 263–288, 2003.; W. C. Wang and Y. H. Tsai, “Digital dynamic photoelastic and numerical stress analyses of a strip,” J. Vib. Control, vol. 12, no. 8, pp. 927–938, 2006.; A. Asundi, M. R. Sajan, and L. Tong, “Dynamic photoelasticity using TDI imaging,” Opt. Lasers Eng., vol. 38, no. 1–2, pp. 3–16, 2002.; F. Huang and A. Sugimoto, Image and Video Technology – PSIVT 2013 Workshops, no. October. 2013.; L. Wang, Y. Ju, H. Xie, G. Ma, L. Mao, and K. He, “The mechanical and photoelastic properties of 3D printable stress-visualized materials,” Sci. Rep., vol. 7, no. 1, pp. 1–9, 2017.; Y. Ju, L. Wang, H. Xie, G. Ma, Z. Zheng, and L. Mao, “Visualization and Transparentization of the Structure and Stress Field of Aggregated Geomaterials Through 3D Printing and Photoelastic Techniques,” Rock Mech. Rock Eng., vol. 50, no. 6, pp. 1383–1407, 2017.; A. Ajovalasit, G. Petrucci, and M. Scafidi, “Review of RGB photoelasticity,” Opt. Lasers Eng., vol. 68, pp. 58–73, 2015.; Hung, K-M., and C-C. Ma. "Theoretical analysis and digital photoelastic measurement of circular disks subjected to partially distributed compressions." Experimental mechanics 43.2 (2003): 216-224.; Voloshin, Arkady S., and C. P. Burger. "Half-fringe photoelasticity: a new approach to whole-field stress analysis." Experimental Mechanics 23.3 (1983): 304-313.; Ramesh, K., and D. Sreedhar. "Optically enhanced tiling (OET) in digital fringe pattern analysis." Strain 34.4 (1998): 127-130.; Hecker, F. W., and H. Abeln. "Digital Phase-shifting photoelasticity." Optics and the Information Age. Vol. 813. International Society for Optics and Photonics, 1987.; Asundi, Anand, Liu Tong, and Chai Gin Boay. "Dynamic phase-shifting photoelasticity." Applied Optics 40.22 (2001): 3654-3658.; Su, Xianyu, Anand Krishna Asundi, and M. R. Sajan. "Phase unwrapping in photoelasticity." International Conference on Experimental Mechanics: Advances and Applications. Vol. 2921. International Society for Optics and Photonics, 1997.; Ekman, Matthew J., and Andrew D. Nurse. "Completely automated determination of two-dimensional photoelastic parameters using load stepping." Optical Engineering 37 (1998).; Surendra, Kamadi Vara Naga, and KR Yogendra Simha. "Digital Image Analysis around isotropic points for photoelastic pattern recognition." Optical Engineering 54.8 (2015): 081209.; https://repositorio.unal.edu.co/handle/unal/78194

Dostupnost: https://repositorio.unal.edu.co/handle/unal/78194

Detección y clasificación de antracnosis en mango usando imágenes hiperespectrales y técnicas de aprendizaje profundo ; Detection and classification of mango anthracnose using hyperspectral imaging and deep learning techniques

Autoři: Montenegro Bermudez, Andres Felipe Prieto Ortiz, Flavio Augusto Grupo de Automática de la Universidad Nacional Gaunal

Přispěvatelé: Montenegro Bermudez, Andres Felipe Prieto Ortiz, Flavio Augusto Grupo de Automática de la Universidad Nacional Gaunal

Témata: 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación, Antracnosis, Visión por computador, Agricultura de precisión, Inteligencia artificial, Imágenes hiperespectrales, Computer vision, Precision agriculture, Artificial intelligence, Hyperspectral imaging, Algoritmo, Algorithms

Popis souboru: xii, 38 páginas; application/pdf

Relation: https://docs.opencv.org/4.x/df/d9d/tutorialpycolorspaces:html; https://www.deccoiberica.es/que-es-la-antracnosis-y-como-afecta-a-los-cultivos/, 2018; https://cs231n.github.io/convolutional-networks/, 2021; E. Alegre, G. Pajares, and A. D. L. Escalera. Conceptos y Métodos en visión por computador. 2016; L. Annala, M. A. Eskelinen, J. Hamalainen, A. Riihinen, and I. Polonen. Practical approach for hyperspectral image processing in python. volume 42, pages 45{52. International Society for Photogrammetry and Remote Sensing, 4 2018. doi:10.5194/isprs-archives-XLII-3-45- 2018; A. Bannari, A. Pacheco, K. Staenz, H. McNairn, and K. Omari. Estimating and mapping crop residues cover on agricultural lands using hyperspectral and ikonos data. Remote Sensing of Environment, 104:447{459, 10 2006. ISSN 00344257. doi:10.1016/j.rse.2006.05.018; R. Bongiovanni, E. C. Mantovani, S. Best, and Alvaro Roel. Agricultura de precisión: Integrando conocimientos para una agricultura moderna y sustentable, 2006; G. Bradski and A. Kaehler. Learning OpenCV. 2008; J. Carrillo, J. Armando, S. Estrada, D. Rangel, A. Barajas, I. Zequera, R. Molar, Z. D. la Garza Ruiz, M. Vera, and E. Fentanes. Control biológico de antracnosis [colletotrichum gloeosporioides(penz.) penz. y sacc.] y su efecto en la calidad poscosecha delmango (mangifera indica l.) en sinaloa, méxico. Revista Mexicana de Fitopatología, page 10, 2005. ISSN 0185-3309. URL http://www.redalyc.org/articulo.oa?id=61223104; G. Casal, J. Dominguez, N. Sanchez, and J. Freire. Utilización del sensor de imagen airborne hyperspectralscanner (ahs) para la cartograf´ıa de bosques de sargassum muticumen la ría de vigo (galicia). Revista de teledetecci´on, page 6, 2008; J. D. P. Díaz. Aplicación de las técnicas de aprendizaje automático para la detección temprana de antracnosis en hojas de guanábana, 2020; A. Fazari, O. J. Pellicer-Valero, J. Gómez-Sanchıs, B. Bernardi, S. Cubero, S. Benalia, G. Zimbalatti, and J. Blasco. Application of deep convolutional neural networks for the detection of anthracnose in olives using vis/nir hyperspectral images. Computers and Electronics in Agriculture, 187, 8 2021. ISSN 01681699. doi:10.1016/j.compag.2021.106252; E. García and F. Flego. Agricultura de precisión; R. C. Gonzalez and R. E. R. E. Woods. Digital image processing. 2001. ISBN 0201180758; M. L. Guillen-Climent, H. Mas, A. Fernández-Landa, N. Algeet-Abarquero, and J. L.Tomé. Using hipersepctral images for decay detection in pinus halepensis (mill.) in the mediterranean forest. Revista de Teledeteccion, 2020:59{69, 2020. ISSN 19888740. doi:10.4995/raet.2020.13289; R. L. Lawrence, S. D. Wood, and R. L. Sheley. Mapping invasive plants using hyperspectral imagery and breiman cutler classifications (randomforest). Remote Sensing of Environment, 100:356{362, 2 2006. ISSN 00344257. doi:10.1016/j.rse.2005.10.014; B. A. Lemus Soriano and D. A. Pérez Aguilar. Manejo de la antracnosis del aguacate con biofungicidas. page 5, 2017; J. L. Perez. Imágenes hiperespectrales y sus aplicacionesen estudios de suelos, cultivos y bosques, en la era de la cuarta revolución industrial. REVISTA UD Y LA GEOMATICA ´ , pages 40{70, 2021. ISSN 2344-8407. URL http://revistas.udistrital.edu.co/ojs/index.php/UDGeo/index; S. Prasad and J. Chanussot. Advances in Computer Vision and Pattern Recognition Hyperspectral Image Analysis Advances in Machine Learning and Signal Processing. 2020. URL http://www.springer.com/series/4205; L. Ramirez. Desarrollo de un sistema para la identificación temprana de la antracnosis en frutos de mangobasado en visión de máquina, 2021; A. Roman-Gonzalez and N. I. Vargas-Cuentas. Análisis de imágenes hiperespectrales, 2015. URL https://www.researchgate.net/publication/259840849; J. Torres. Python Deep Learning. Primera edition, 2020; L. Urdaneta, M. Sanabria, D. Rodríguez, and M. P. de Camacaro. Antracnosis causada por colletotrichum acutatum simmonds en frutos de fresa en los estados lara y trujillo, venezuela, 2013; L. A. Valdés, D. L. C. Consuegra, A. Gómez, M. E. Carballo, M. Capote, I. González, J. M. Alvarez, and W. Rohde. Caracterización morfológica, cultural y patogénica de aislados de ´colletotrichum sp. produciendo antracnosis en mango (mangifera indica l.). La Granja, 26: 38, 9 2017. ISSN 1390-3799. doi:10.17163/lgr.n26.2017.04; S. Yu, S. Jia, and C. Xu. Convolutional neural networks for hyperspectral image classification. Neurocomputing, 219:88{98, 1 2017. ISSN 18728286. doi:10.1016/j.neucom.2016.09.010; https://repositorio.unal.edu.co/handle/unal/81735; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

Dostupnost: https://repositorio.unal.edu.co/handle/unal/81735
https://repositorio.unal.edu.co/

Análisis, desarrollo y puesta en marcha de un sistema de clasificación y control de calidad de embalajes por visión artificial ; Analysis, development and implementation of a packaging classification and quality control system using computer vision ; Anàlisi, desenvolupament i posada en marxa d un sistema de classificació i control de qualitat d embalatges per visió artificial

Autoři: Maestre Signes, Fernando Ivorra Martínez, Eugenio Departamento de Ingeniería de Sistemas y Automática a další

Přispěvatelé: Maestre Signes, Fernando Ivorra Martínez, Eugenio Departamento de Ingeniería de Sistemas y Automática a další

Témata: Control de calidad, Visión artificial, Cinta transportadora, Clasificación, PLC, Sensor óptico, INGENIERIA DE SISTEMAS Y AUTOMATICA, Grado en Ingeniería Electrónica Industrial y Automática-Grau en Enginyeria Electrònica Industrial i Automàtica

Popis souboru: application/pdf

Relation: https://riunet.upv.es/handle/10251/195821

Dostupnost: https://riunet.upv.es/handle/10251/195821