Výsledky vyhledávání - Autoencoder convolutional profundo
-
1
Autoři: a další
Zdroj: Brazilian Journal of Development. 8:40027-40042
-
2
Autoři:
Zdroj: Ciência e Natura; Vol. 47 (2025): Publicação contínua; e85042 ; Ciência e Natura; v. 47 (2025): Publicação contínua; e85042 ; 2179-460X ; 0100-8307
Témata: Machine learning, Deep convolutional autoencoder, Clustering, Pattern recognition, Precipitation time series, Aprendizado de máquina, Autoencoder convolucional profundo, Agrupamentos, Reconhecimento de padrões, Séries temporais de precipitação
Popis souboru: application/pdf; text/html
Relation: https://periodicos.ufsm.br/cienciaenatura/article/view/85042/65968; https://periodicos.ufsm.br/cienciaenatura/article/view/85042/66553; https://periodicos.ufsm.br/cienciaenatura/article/view/85042
-
3
Autoři: a další
Zdroj: IEEE Access, Vol 9, Pp 115700-115709 (2021)
Témata: Artificial intelligence, Biomedical Engineering, Gait Recognition, Health Professions, Physical Therapy, Sports Therapy and Rehabilitation, 02 engineering and technology, unsupervised learning, FOS: Medical engineering, Pattern recognition (psychology), freezing of gait, Engineering, denoising autoencoder, Health Sciences, Machine learning, Image (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Embedded system, Gait, Sensory Feedback, Gait Analysis and Fall Prevention in Elderly, Deep learning, Wearable computer, Autoencoder, Computer science, Dimensionality reduction, TK1-9971, 3. Good health, Gait Recognition for Human Identification, Thresholding, Physical medicine and rehabilitation, Analysis of Electromyography Signal Processing, Physical Sciences, Parkinson's disease, Medicine, Electrical engineering. Electronics. Nuclear engineering, Gait Analysis
Přístupová URL adresa: https://ieeexplore.ieee.org/ielx7/6287639/6514899/09514558.pdf
https://doaj.org/article/34fba6708ae448288f041a3d55b928c7
https://zuscholars.zu.ac.ae/cgi/viewcontent.cgi?article=5464&context=works
https://zuscholars.zu.ac.ae/works/4465/
https://dblp.uni-trier.de/db/journals/access/access9.html#NoorNWO21 -
4
Autoři:
Přispěvatelé:
Témata: 004(043.3), Aprendizaje profundo, Autocodificadores, Autocodificadores variacionales, Espacio latente, Espectrograma, Red convolucional, Deep Learning, Autoencoders, Variational Autoencoders, Latent space, Spectro- gram, Convolutional network, Informática (Informática), 33 Ciencias Tecnológicas
Popis souboru: application/pdf
Dostupnost: https://hdl.handle.net/20.500.14352/123949
-
5
Autoři: a další
Zdroj: Intelligent Systems with Applications, Vol 22, Iss, Pp 200387-(2024)
Témata: Artificial intelligence, Orthogonal polynomials, Convolutional neural network, Image Retrieval, Separable space, Pattern recognition (psychology), Mathematical analysis, Hahn moments, Cluster analysis, Discrete orthogonal polynomials, Image Feature Retrieval and Recognition Techniques, Shape Matching and Object Recognition, FOS: Mathematics, Deep clustering, Discrete separable orthogonal moments, Feature Selection, Wilson polynomials, Spectral Clustering, Interest Point Detectors, Deep learning, QA75.5-76.95, Autoencoder, Kravchuk polynomials, Image clustering, Computer science, Algorithm, Krawtchouk moments, Combinatorics, Electronic computers. Computer science, Computer Science, Physical Sciences, Q300-390, Computer Vision and Pattern Recognition, Content-Based Image Retrieval, Face Recognition and Dimensionality Reduction Techniques, Cybernetics, Mathematics
Přístupová URL adresa: https://doaj.org/article/595d93787957438994da59abb3bd4045
-
6
Autoři:
Přispěvatelé:
Zdroj: O2, repositorio institucional de la UOC
Universitat Oberta de Catalunya (UOC)Témata: autoencoder, Bioinformática -- TFM, aprendizaje profundo, aprenentatge profund, deep learning, Bioinformàtica -- TFM, MRI, Bioinformatics -- TFM
Popis souboru: application/pdf
Přístupová URL adresa: http://hdl.handle.net/10609/127059
-
7
Autoři:
Zdroj: Gestão & Tecnologia de Projetos (Gestión y tecnología de proyectos); Vol. 19 Núm. 3 (2024): fluxo contínuo; 27-47 ; Gestão & Tecnologia de Projetos; v. 19 n. 3 (2024): fluxo contínuo; 27-47 ; Gestão & Tecnologia de Projetos (Design Management and Technology); Vol. 19 No. 3 (2024): fluxo contínuo; 27-47 ; 1981-1543
Témata: aprendizaje profundo, exploración del espacio de soluciones, estudios tridimensionales, autoencoder convucional, deep learning, exploration of the design space, convolutional autoencoder, three-dimensional studies, aprendizado profundo, exploração do espaço das soluções, autoencoder convolucional, estudos tridimensionais
Popis souboru: application/pdf
Relation: https://revistas.usp.br/gestaodeprojetos/article/view/227541/210913; https://revistas.usp.br/gestaodeprojetos/article/view/227541
-
8
Autoři: Issam Ismail Zaidkilani, Nadeem
Thesis Advisors: García García, Miguel Angel, Puig Valls, Domènec Savi
Zdroj: TDX (Tesis Doctorals en Xarxa)
Témata: Càncer de Mama - Imatges per Ultrasò, Anàlisi d'Imatges Mèdiques, Diagnòstic Assistit per Ordinador (CAD), Cáncer de Mama - Imágenes por Ultrasonido, Análisis de Imágenes Médicas, Diagnóstico Asistido por Computadora (CAD), Breast Cancer-Ultrasound, Medical Image Analysis, Computer-Aided Diagnosis (CAD), Ciències
Popis souboru: application/pdf
Přístupová URL adresa: http://hdl.handle.net/10803/695647
-
9
Autoři: a další
Zdroj: IEEE Access, Vol 8, Pp 86520-86535 (2020)
Témata: Artificial intelligence, Outlier Detection, Class (philosophy), Convolutional neural network, Anomaly detection, 02 engineering and technology, Clustering of Time Series Data and Algorithms, Pattern recognition (psychology), Clustering, Feature Extraction, Visual Object Tracking and Person Re-identification, Anomaly Detection in High-Dimensional Data, Artificial Intelligence, Image (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Pattern Discovery, feature extraction, deep learning, Deep learning, Autoencoder, Computer science, convolutional autoencoder, Contextual image classification, one-class classification, TK1-9971, Algorithm, compact embedding, Computer Science, Physical Sciences, Signal Processing, Foreground Segmentation, Computer vision, Electrical engineering. Electronics. Nuclear engineering, Computer Vision and Pattern Recognition, Convolutional code, Decoding methods, Embedding
-
10
Autoři:
Přispěvatelé:
Zdroj: O2, repositorio institucional de la UOC
Universitat Oberta de Catalunya (UOC)Témata: autoencoder, aprendizaje profundo, Mineria de dades -- TFM, aprenentatge profund, Minería de datos -- TFM, deep learning, Data mining -- TFM, MRI, IRM
Popis souboru: application/pdf
Přístupová URL adresa: http://hdl.handle.net/10609/121266
-
11
Alternate Title: Modelo semisupervisado de aprendizaje profundo para la clasificación de limones. (Spanish)
Autoři: a další
Zdroj: Revista Ingeniería e Investigación; dic2024, Vol. 44 Issue 3, p1-14, 14p
-
12
Alternate Title: EXPLORING DEEP GENERATIVE MODELS FOR IMPROVED DATA GENERATION IN HYPERTROPHIC CARDIOMYOPATHY. (English)
Autoři:
Zdroj: Ingenius, Revista Ciencia y Tecnología; Jul-Dec2025, Issue 34, p116-125, 10p
-
13
Autoři:
Témata: System identification, Deep learning, Generative modeling, Nonlinear dynamic systems, Identificación del sistema, Aprendizaje profundo, Modelado generativo, Sistemas dinámicos no lineales
Popis souboru: 17 pàginas; application/pdf
Relation: 17; 22; Intelligent Systems with Applications; Abdullahi, S. B., & Chamnongthai, K. (2022). Intelligent fuzzy network for dynamic sign words recognition from spatial features. In 2022 19th international conference on elec trical engineering/electronics, computer, telecommunications and information technology (ECTI-CON) (pp. 1–4). IEEE. Abdullahi, S. B., Muangchoo, K., Abubakar, A. B., Ibrahim, A. H., & Aremu, K. O. (2021). Data-driven ai-based parameters tuning using grid partition algorithm for predicting climatic effect on epidemic diseases. IEEE Access, 9, 55388–55412. Abdullahi, S. B., Ibrahim, A. H., Abubakar, A. B., & Kambheera, A. (2022). Optimizing Hammerstein-Wiener model for forecasting confirmed cases of Covid-19. IAENG In ternational Journal of Applied Mathematics, 52(1). Abdullahi, S. B., Khunpanuk, C., Bature, Z. A., Chiroma, H., Pakkaranang, N., Abubakar, A. B., & Ibrahim, A. H. (2022). Biometric information recognition using artificial intelligence algorithms: A performance comparison. IEEE Access, 10, 49167–49183. Abualigah, L., Ekinci, S., Izci, D., & Zitar, R. A. (2023). Modified elite opposition-based artificial hummingbird algorithm for designing fopid controlled cruise control system. Intelligent Automation & Soft Computin Aljamaan, I., & Al-Naib, I. (2021). Prediction of blood glucose level using nonlinear sys tem identification approach. IEEE Access, 10, 1936–1945. Andersson, C., Ribeiro, A. H., Tiels, K., Wahlstrom, N., & Schon, T. B. (2019). Deep con volutional networks in system identification. In Proceedings of the IEEE conference on decision and control 2019-Decem (pp. 3670–3676). Retrieved from arXiv :1909 .01730. Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828. Blalock, D., Gonzalez Ortiz, J. J., Frankle, J., & Guttag, J. (2020). What is the state of neu ral network pruning? In Proceedings of machine learning and systems 2 (pp. 129–146). Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control. John Wiley & Sons. Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016). Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the Na tional Academy of Sciences, 113(15), 3932–3937. Brunton, S. L., Noack, B. R., & Koumoutsakos, P. (2020). Machine learning for fluid me chanics. Annual Review of Fluid Mechanics, 52, 477–508. Chiuso, A., & Pillonetto, G. (2019). System identification: A machine learning perspective. Annual Review of Control, Robotics, and Autonomous Systems, 2(1), 281–304. https:// doi .org /10 .1146 /annurev -control -053018 -023744. Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and accurate deep network learning by exponential linear units (elus). Retrieved from arXiv preprint arXiv :1511 . 07289. Cotter, N. (1990). The Stone-Weierstrass theorem and its application to neural networks. IEEE Transactions on Neural Networks, 1(4), 290–295. https://doi .org /10 .1109 /72 . 80265. Cuomo, S., Di Cola, V. S., Giampaolo, F., Rozza, G., Raissi, M., & Piccialli, F. (2022). Scientific machine learning through physics–informed neural networks: Where we are and what’s next. Journal of Scientific Computing, 92(3), 88. Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. MCSS. Mathematics of Control, Signals and Systems, 2(4), 303–314. Dalla Libera, A., & Pillonetto, G. (2022). Deep prediction networks. Neurocomputing, 469, 321–329. De la Rosa, E., & Yu, W. (2016). Randomized algorithms for nonlinear system identifica tion with deep learning modification. Information Sciences, 364, 197–212. De la Rosa, E., & Yu, W. (2018). Data-driven fuzzy modeling using restricted Boltzmann machines and probability theory. IEEE Transactions on Systems, Man, and Cybernetics, Systems, 50(7), 2316–2326. De La Rosa, E., Yu, W., & Li, X. (2017). Nonlinear system modeling with deep neural net works and autoencoders algorithm. In 2016 IEEE international conference on systems, man, and cybernetics, SMC 2016 -conference proceedings (pp. 2157–2162). Doersch, C. (2016). Tutorial on variational autoencoders. Retrieved from arXiv :1606 . 05908. http://arxiv .org /abs /1606 .05908. Dong, A., Starr, A., & Zhao, Y. (2023). Neural network-based parametric system identifi cation: A review. International Journal of Systems Science, 54(13), 2676–2688. Duriez, T., Brunton, S. L., & Noack, B. R. (2017). Machine learning control-taming nonlinear dynamics and turbulence. Springer. Feng, S., & Chen, C. P. (2019). Nonlinear system identification using a simplified fuzzy broad learning system: Stability analysis and a comparative study. Neurocomputing, 337, 274–286. Genc, S. (2017). Parametric system identification using deep convolutional neu ral networks. In 2017 international joint conference on neural networks (IJCNN) (pp. 2112–2119). IEEE. Retrieved from http://ieeexplore .ieee .org /document / 7966110/. Grino, R., Cembrano, G., & Torras, C. (2000). Nonlinear system identification using additive dynamic neural networks-two on-line approaches. IEEE Transactions on Cir cuits and Systems. I, Fundamental Theory and Applications, 47(2), 150–165. https:// doi .org /10 .1109 /81 .828569. Habib, M. K., Ayankoso, S. A., & Nagata, F. (2021). Data-driven modeling: Concept, techniques, challenges and a case study. In 2021 IEEE international conference on mechatronics and automation (ICMA) (pp. 1000–1007). IEEE. Haghighat, E., & Juanes Sciann, R. (2021). A keras/tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks. Computer Methods in Applied Mechanics and Engineering, 373, Article 113552. Han, S., Pool, J., Tran, J., & Dally, W. (2015). Learning both weights and connections for efficient neural network. Advances in Neural Information Processing Systems, 28. Han, Z., & Narendra, K. S. (2011). New concepts in adaptive control using multiple mod els. IEEE Transactions on Automatic Control, 57(1), 78–89. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359–366. https://doi .org /10 .1016 / 0893 -6080(89 )90020 -8. Hu, G., Guo, Y., Wei, G., & Abualigah, L. (2023). Genghis Khan shark optimizer: A novel nature-inspired algorithm for engineering optimization. Advanced Engineering Infor matics, 58, Article 102210. Hu, G., Zheng, Y., Abualigah, L., & Hussien, A. G. (2023). Detdo: An adaptive hybrid dandelion optimizer for engineering optimization. Advanced Engineering Informatics, 57, Article 102004. Iten, R., Metger, T., Wilming, H., Del Rio, L., & Renner, R. (2020). Discovering physi cal concepts with neural networks. Tech. Rep. arXiv :1807 .10300v2. Retrieved from https://arxiv .org /pdf /1807 .10300 .pdf. Karagoz, R., & Batselier, K. (2020). Nonlinear system identification with regularized ten sor network b-splines. Automatica, 122, Article 109300. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. Retrieved from arXiv preprint arXiv :1412 .6980. Kingma, D. P., & Welling, M. (2014). Auto-encoding variational Bayes. In 2nd international conference on learning representations, ICLR 2014 - conference track proceedings (Ml) (pp. 1–14). Retrieved from arXiv :1312 .6114. Kingma, D. P., & Welling, M. (2019). An introduction to variational autoencoders. Foun dations and Trends in Machine Learning, 12(4), 307–392. https://doi .org /10 .1561 / 2200000056. Retrieved from arXiv :1906 .02691. Klus, S., Nüske, F., Peitz, S., Niemann, J.-H., Clementi, C., & Schütte, C. (2020). Data driven approximation of the Koopman generator: Model reduction, system identifica tion, and control. Physica D. Nonlinear Phenomena, 406, Article 132416. Lin, M., Cheng, C., Peng, Z., Dong, X., Qu, Y., & Meng, G. (2021). Nonlinear dynamical system identification using the sparse regression and separable least squares methods. Journal of Sound and Vibration, 505, Article 116141. Liu, Y., Dong, H., Wang, X., & Han, S. (2019). Time series prediction based on temporal convolutional network. In 2019 IEEE/ACIS 18th international conference on computer and information science (ICIS) (pp. 300–305). IEEE. Ljung, L. (1998). System identification: Theory for the user. Pearson Education. Retrieved from https://books .google .com .co /books ?id =fYSrk4wDKPsC. Ljung, L. (2010). Perspectives on system identification. Annual Reviews in Control, 34(1), 1–12. Ljung, L., & Glad, T. (1994). Modeling of dynamic systems. Prentice-Hall, Inc. Ljung, L., Andersson, C., Tiels, K., & Schön, T. B. (2020). Deep learning and system identifi cation. IFAC-PapersOnLine: Vol. 53. Elsevier B.V. (pp. 1175–1181). Lopez, M., & Yu, W. (2017). Nonlinear system modeling using convolutional neu ral networks. In 2017 14th international conference on electrical engineering, com puting science and automatic control (CCE) (pp. 1–5). IEEE. Retrieved from http:// ieeexplore .ieee .org /document /8108894/. Marconato, A., Sjöberg, J., Suykens, J., & Schoukens, J. (2012). Identification of the silver box benchmark using nonlinear state-space models. IFAC Proceedings Volumes, 45(16), 632–637. Maroli, J. M., Özgüner, Ü., & Redmill, K. (2019). Nonlinear system identification us ing temporal convolutional networks: A silverbox study. IFAC-PapersOnLine, 52(29), 186–191. Masti, D., & Bemporad, A. (2018). Learning nonlinear state-space models using deep au toencoders. In 2018 IEEE conference on decision and control (CDC) (pp. 3862–3867). IEEE. Mishra, V. K., Markovsky, I., Fazzi, A., & Dreesen, P. (2021). Data-driven simulation for NARX systems. In 2021 29th European signal processing conference (EUSIPCO) (pp. 1055–1059). IEEE. Misyris, G. S., Venzke, A., & Chatzivasileiadis, S. (2020). Physics-informed neural net works for power systems. In 2020 IEEE power & energy society general meeting (PESGM) (pp. 1–5). IEEE. Molchanov, P., Tyree, S., Karras, T., Aila, T., & Kautz, J. (2016). Pruning convolutional neural networks for resource efficient inference. Retrieved from arXiv preprint arXiv : 1611 .06440. Montáns, F. J., Chinesta, F., Gómez-Bombarelli, R., & Kutz, J. N. (2019). Data-driven mod eling and learning in science and engineering. Comptes Rendus. Mécanique, 347(11), 845–855. https://doi .org /10 .1016 /j .crme .2019 .11 .009. Narendra, K., & Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1(1), 4–27. https:// doi .org /10 .1109 /72 .80202. http://ieeexplore .ieee .org /document /80202/. Peeters, L., Beintema, G. I., Forgione, M., & Schoukens, M. (2022). NARX identification using derivative-based regularized neural networks. In 2022 IEEE 61st conference on decision and control (CDC) (pp. 1515–1520). IEEE. Peters, T. (2019). Data-driven science and engineering: Machine learning, dynamical systems, and control: by S.L. Brunton and J.N. Kutz, 2019, Cambridge, Cambridge University Press, 472 pp., £ 49.99 (hardback), ISBN 9781108422093. level: postgraduate. scope: text book. Pillonetto, G., Aravkin, A., Gedon, D., Ljung, L., Ribeiro, A. H., & Schön, T. B. (2023). Deep networks for system identification: A survey. Retrieved from arXiv preprint arXiv :2301 .12832. Punjani, A., & Abbeel, P. (2015). Deep learning helicopter dynamics models. In 2015 IEEE international conference on robotics and automation (ICRA) (pp. 3223–3230). IEEE. http://ieeexplore .ieee .org /document /7139643/. Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2018). Multistep neural networks for data-driven discovery of nonlinear dynamical systems. Retrieved from arXiv preprint arXiv :1801 .01236. Schoukens, J., & Ljung, L. (2019). Nonlinear system identification: A user-oriented road map. IEEE Control Systems Magazine, 39(6), 28–99. Schoukens, J., Suykens, J., & Ljung, L. (2009). Wiener-Hammerstein benchmark. In Proc. of the 15th IFAC symposium on system identification (SYSID 2009). Schoukens, M., & Scheiwe, F. G. (2016). Modeling nonlinear systems using a Volterra feedback model. In Workshop on nonlinear system identification benchmarks. Schoukens, M., & Tiels, K. (2017). Identification of block-oriented nonlinear systems start ing from linear approximations: A survey. Automatica, 85, 272–292. Schoukens, M., Mattsson, P., Wigren, T., & Noël, J. (2016). Cascaded tanks benchmark combining soft and hard nonlinearities. In 2016 workshop on nonlinear system identifi cation benchmarks (pp. 20–23) Simidjievski, N., Todorovski, L., Kocijan, J., & Džeroski, S. (2020). Equation discovery for nonlinear system identification. IEEE Access, 8, 29930–29943. Stiasny, J., Chevalier, S., & Chatzivasileiadis, S. (2021). Learning without data: Physics informed neural networks for fast time-domain simulation. In 2021 IEEE international conference on communications, control, and computing technologies for smart grids (Smart GridComm) (pp. 438–443). IEEE. Thill, M., Konen, W., & Bäck, T. (2020). Time series encodings with temporal convolutional networksIn Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). LNCS: Vol. 12438 (pp. 161–173). Todorovic, N., & Klan, P. (2006). State of the art in nonlinear dynamical system iden tification using artificial neural networks. In 2006 8th seminar on neural network applications in electrical engineering (pp. 103–108). IEEE. http://ieeexplore .ieee .org / document /4147175/. Wang, Y. (2017). A new concept using LSTM neural networks for dynamic system iden tification. In 2017 American control conference (ACC) (pp. 5324–5329). IEEE. http:// ieeexplore .ieee .org /document /7963782/. Weber, D., & Gühmann, C. (2021). Non-autoregressive vs autoregressive neural networks for system identification. IFAC-PapersOnLine, 54(20), 692–698. Westwick, D. T., Hollander, G., Karami, K., & Schoukens, J. (2018). Using decoupling methods to reduce polynomial NARX models. IFAC-PapersOnLine, 51(15), 796–801. Yu, D., Wang, Y., Liu, H., Jermsittiparsert, K., & Razmjooy, N. (2019). System identification of pem fuel cells using an improved Elman neural network and a new hybrid optimization algorithm. Energy Reports, 5, 1365–1374. https:// doi .org /10 .1016 /j .egyr .2019 .09 .039. https://www .sciencedirect .com /science / article /pii /S2352484719305396. Yu, W., & Pacheco, M. (2019). Impact of random weights on nonlinear system identification using convolutional neural networks. Information Sciences, 477, 1–14. https://doi .org /10 .1016 /j .ins .2018 .10 .019. https://linkinghub .elsevier .com / retrieve /pii /S0020025518308168. Zadeh, L. A. (1962). From circuit theory to system theory. Proceedings of the IRE, 50(5), 856–865. Zhou, H., Ibrahim, C., & Pan, W. (2019). A sparse Bayesian deep learning approach for identification of cascaded tanks benchmark. Retrieved from arXiv preprint arXiv : 1911 .06847; López Sotelo, J. A. y Paniagua Jaramillo, J. L. (2024). Nonlinear system identification using modified variational autoencoders. Intelligent systems with applications. Vol. 22. 17 p. https://doi.org/10.1016/j.iswa.2024.200344; https://hdl.handle.net/10614/16288; https://doi.org/10.1016/j.iswa.2024.200344; Universidad Autónoma de Occidente; Respositorio Educativo Digital UAO; https://red.uao.edu.co/
-
14
Autoři:
Zdroj: Ingenius; No. 34 (2025): july-december; 116-125 ; Ingenius; Núm. 34 (2025): julio-diciembre; 116-125 ; Ingenius; n. 34 (2025): julio-diciembre; 116-125 ; 1390-860X ; 1390-650X ; 10.17163/34
Témata: Data Generation, Diffusion models, Generative Adversarial networks, Variational autoencoders, generación de datos, modelos de difusión, redes generativas adversarias, codificadores automáticos variacionales
Popis souboru: application/pdf; text/html; application/zip
Relation: https://revistas.ups.edu.ec/index.php/ingenius/article/view/9310/10057; https://revistas.ups.edu.ec/index.php/ingenius/article/view/9310/10058; https://revistas.ups.edu.ec/index.php/ingenius/article/view/9310/10059; https://revistas.ups.edu.ec/index.php/ingenius/article/view/9310/10060; https://revistas.ups.edu.ec/index.php/ingenius/article/view/9310/10061; https://revistas.ups.edu.ec/index.php/ingenius/article/view/9310
-
15
Alternate Title: Revisión sistemática: avances recientes en técnicas de aprendizaje profundo para el reconocimiento de rasgos facials. (Spanish)
Autoři:
Zdroj: Data & Metadata; 2025, Vol. 4, p1-24, 24p
-
16
Autoři: a další
Přispěvatelé: a další
Zdroj: Energy Reports, Vol 8, Iss, Pp 9933-9945 (2022)
Energy ReportsTémata: Artificial neural network, Artificial intelligence, Cybersecurity, Electricity Price and Load Forecasting Methods, 0211 other engineering and technologies, Geometry, Convolutional neural network, Deep learning, False Data Injection Attack, Load forecasting, Smart grid, Noise (video), 02 engineering and technology, Quantum mechanics, 7. Clean energy, Real-time computing, Electric power system, Engineering, Deep Learning, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Image (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Grid, Data mining, Electricity Price Forecasting, Electricity Theft Detection in Smart Grids, Physics, Load Forecasting, Autoencoder, Security Challenges in Smart Grid Systems, Power (physics), Computer science, TK1-9971, Control and Systems Engineering, False Data Injection Attacks, Electrical engineering, Physical Sciences, Electrical engineering. Electronics. Nuclear engineering, Short-Term Forecasting, Mathematics
Popis souboru: application/pdf
Přístupová URL adresa: https://doaj.org/article/f01bd2a4cfd744dea6ed6c9cc943be55
http://hdl.handle.net/11467/6111 -
17
Alternate Title: Mejorando la ortopedia y medicina deportiva con el control de exoesqueletos de miembros inferiores en rehabilitación utilizando la clasificación de señales de electromiografía basada en aprendizaje profundo. (Spanish)
Autoři:
Zdroj: Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación; 2024, Issue 61, p616-625, 10p
-
18
Autoři: a další
Zdroj: Biomed Eng
BioMedical Engineering OnLine, Vol 17, Iss 1, Pp 1-19 (2018)Témata: Pulmonary and Respiratory Medicine, CT Screening, 0301 basic medicine, Radiology, Nuclear Medicine and Imaging, Artificial intelligence, Tumor Staging, Metric (unit), Chest X-Ray, Economics, Diagnosis and Treatment of Lung Cancer, Receiver operating characteristic, Convolutional neural network, Workload, Noise (video), Signal-To-Noise Ratio, Pattern recognition (psychology), Radiomics in Medical Imaging Analysis, Machine Learning, Automation, Cancer Imaging, 03 medical and health sciences, Health Sciences, Machine learning, Medical technology, Image Processing, Computer-Assisted, Image (mathematics), Humans, R855-855.5, Noise reduction, Lung, 0303 health sciences, Research, Deep learning, Transfer Learning, Autoencoder, Applications of Deep Learning in Medical Imaging, Computer science, 3. Good health, Computer aided diagnosis, Operating system, Operations management, ROC Curve, Chest screening, Medicine, Emergency medicine, Radiography, Thoracic, Triage
Přístupová URL adresa: https://biomedical-engineering-online.biomedcentral.com/track/pdf/10.1186/s12938-018-0496-2
https://pubmed.ncbi.nlm.nih.gov/29792208
https://doaj.org/article/0651ceb9a36e483794499d2f0818af09
https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/s12938-018-0496-2
http://europepmc.org/articles/PMC5966927
https://link.springer.com/content/pdf/10.1186/s12938-018-0496-2.pdf
https://link.springer.com/article/10.1186/s12938-018-0496-2
https://pubmed.ncbi.nlm.nih.gov/29792208/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5966927 -
19
Autoři:
Přispěvatelé:
Zdroj: O2, repositorio institucional de la UOC
Universitat Oberta de Catalunya (UOC)Témata: machine learning, aprendizaje profundo, Aprenentatge automàtic -- TFM, image analysis, análisis de imágenes, autoencoders, convolutional neural networks, aprendizaje automático, underwater environments, deep learning, entornos submarinos, redes neuronales convolucionales, Machine learning -- TFM
Popis souboru: application/pdf
Přístupová URL adresa: http://hdl.handle.net/10609/150314
-
20
Autoři: a další
Přispěvatelé: a další
Zdroj: IEEE Access, Vol 12, Pp 15037-15049 (2024)
Témata: FOS: Computer and information sciences, Exploit, Artificial intelligence, Sociology and Political Science, bagged CNN, convolutional neural network, Social Sciences, fi=Tietotekniikka|en=Computer Science, Boosting (machine learning), Detection and Prevention of Phishing Attacks, Social media, Characterization and Detection of Android Malware, Computer security, Ensemble learning, Machine learning, boosted CNN, Information retrieval, Polymer chemistry, 10. No inequality, Preprocessor, Fake News, Natural language processing, 4. Education, multi-modal fake news, The Spread of Misinformation Online, Deep learning, Autoencoder, Rumor Detection, 16. Peace & justice, Computer science, TK1-9971, Encoder, World Wide Web, Chemistry, Operating system, classification, Fake news, Computer Science, Physical Sciences, Signal Processing, 8. Economic growth, Misinformation, Internet privacy, Electrical engineering. Electronics. Nuclear engineering, Modal, Information Systems
Popis souboru: fi=kokoteksti; en=fulltext; true
Přístupová URL adresa: https://doaj.org/article/e62e84da4e1a4312834825ff5dfbf74c
Nájsť tento článok vo Web of Science 
Full Text Finder 
Full Text