Suchergebnisse - "Machine learning classification algorithms"

Leveraging Explainable Artificial Intelligence (XAI) for Expert Interpretability in Predicting Rapid Kidney Enlargement Risks in Autosomal Dominant Polycystic Kidney Disease (ADPKD)

Autoren: Latifa Dwiyanti Hidetaka Nambo Nur Hamid

Quelle: AI, Vol 5, Iss 4, Pp 2037-2065 (2024)

Schlagwörter: explainable artificial intelligence (XAI), machine learning classification algorithms, Electronic computers. Computer science, QA75.5-76.95, user-centered design, autosomal dominant polycystic kidney disease (ADPKD)

Zugangs-URL: https://doaj.org/article/e282dfc9f7094c5fb8ad9896f5e8118f

Morpho-Phraseological Based Classification of CEFR Italian L2 Learner Writing Proficiency

Autoren: Franzoni, Valentina Biondi, Giulio Milani, Alfredo

Quelle: IEEE Access, Vol 12, Pp 156433-156441 (2024)

Schlagwörter: complexity measures, language proficiency, text complexity, classification algorithms, Machine learning, L2 learners, Machine learning, Classification algorithms, Complexity measures, Text complexity, Language proficiency, L2 learners, NLP, Complexity theory, Syntactics, Linguistics, Feature extraction, Accuracy, Standards, Machine learning, Vocabulary, Europe, Current measurement, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Zugangs-URL: https://doaj.org/article/e42dfa0a8e3345d09e632dab078272b6
https://hdl.handle.net/11391/1586294
https://ieeexplore.ieee.org/document/10734098
https://doi.org/10.1109/access.2024.3485988

PREVISÃO DA SOP USANDO TÉCNICAS DE MACHINE LEARNING: UMA ANÁLISE COMPARATIVA DE MODELOS E APLICAÇÃO PRÁTICA ; PCOS PREDICTION USING MACHINE LEARNING TECHNIQUES: A COMPARATIVE ANALYSIS OF MODELS AND PRACTICAL APPLICATION ; PREDICCIÓN DEL SOP USANDO TÉCNICAS DE APRENDIZAJE AUTOMÁTICO: UN ANÁLISIS COMPARATIVO DE MODELOS Y APLICACIÓN PRÁCTICA

Autoren: Silva, Luiz Fernando da Cunha Silva, Wesley dos Santos Gonçalves, Samara Martins Nascimento et al.

Quelle: RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218; Vol. 6 No. 6 (2025): CLICK HERE TO ACCESS THE ARTICLES; e666546 ; RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218; Vol. 6 Núm. 6 (2025): ACERCA DE LA REVISTA; e666546 ; RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218; v. 6 n. 6 (2025): CLIQUE AQUI PARA ACESSAR OS ARTIGOS; e666546 ; RECIMA21 -Revista Científica Multidisciplinar - ISSN 2675-6218; Vol. 6 N.º 6 (2025): CLIQUE AQUI PARA ACESSAR OS ARTIGOS; e666546 ; 2675-6218

Schlagwörter: Algoritmos de Classificação, Machine Learning, Saúde Reprodutiva, Síndrome dos Ovários Policísticos, Algorítmos de classificação, Polycystic Ovary Syndrome. Reproductive Health. Machine Learning. Classification Algorithms, Síndrome de Ovario Poliquístico. Salud Reproductiva. Aprendizaje Automático. Algoritmos de Clasificación

Dateibeschreibung: application/pdf

Relation: https://recima21.com.br/index.php/recima21/article/view/6546/4415; https://recima21.com.br/index.php/recima21/article/view/6546

Verfügbarkeit: https://recima21.com.br/index.php/recima21/article/view/6546
https://doi.org/10.47820/recima21.v6i6.6546

Análisis espacio-temporal de florecimientos algales nocivos en un lago-cráter tropical usando datos MODIS (2003-2020)

Autoren: Cortés-Macías, Lizette Zareh Rivera-Caicedo, Juan Pablo Cepeda-Morales, Jushiro et al.

Weitere Verfasser: Cortés-Macías, Lizette Zareh Rivera-Caicedo, Juan Pablo Cepeda-Morales, Jushiro et al.

Quelle: RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Universitat Politècnica de València (UPV)
Revista de Teledetección, Iss 62, Pp 39-55 (2023)

Schlagwörter: modis, Geography (General), Florecimientos algales nocivos, Machine Learning classification algorithms, Harmful algal bloom, algoritmos de clasificación de aprendizaje automático, 15. Life on land, Turquoise lake, Lago turquesa, lago turquesa, MODIS, Algoritmos de clasificación de aprendizaje automático, 13. Climate action, G1-922, 14. Life underwater, florecimientos algales nocivos

Dateibeschreibung: application/pdf

Zugangs-URL: https://riunet.upv.es/handle/10251/199310
https://doaj.org/article/cde2c39394304123bac3b0ba817882a7
https://hdl.handle.net/10251/199310
https://doi.org/10.4995/raet.2023.19673

Acoustic Sensing and Supervised Machine Learning for In Situ Classification of Semi-Autogenous (SAG) Mill Feed Size Fractions Using Different Feature Extraction Techniques

Autoren: Kwaku Boateng Owusu William Skinner Richmond K. Asamoah et al.

Weitere Verfasser: Kwaku Boateng Owusu William Skinner Richmond K. Asamoah et al.

Quelle: Powders, Vol 2, Iss 2, Pp 299-322 (2023)
Powders; Volume 2; Issue 2; Pages: 299-322

Schlagwörter: Chemistry, acoustic sensing, 0205 materials engineering, supervised machine learning classification algorithms, semi-autogenous mill, feature extraction, mill feed size fraction, 0211 other engineering and technologies, 02 engineering and technology, QD1-999

Dateibeschreibung: application/pdf

Zugangs-URL: https://doaj.org/article/a6432784a7ba4e6abefbacdc6e991d87
https://hdl.handle.net/11541.2/33942

Design of an Intrusion Detection Model for IoT-Enabled Smart Home

Autoren: Deepti Rani Nasib Singh Gill Preeti Gulia et al.

Quelle: IEEE Access, Vol 11, Pp 52509-52526 (2023)

Schlagwörter: Machine learning classification algorithms, ensemble classifiers, gradient boosting algorithms, light gradient boosting machines (LGBM), and intrusion detection systems (IDS), Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Dateibeschreibung: electronic resource

Relation: https://ieeexplore.ieee.org/document/10124866/; https://doaj.org/toc/2169-3536

Zugangs-URL: https://doaj.org/article/a1a91a94b0ba488e8a2e6fd531fda6d0

Battle of the Algorithms: An Exposé on Classification Techniques in Machine Learning

Autoren: T .Aditya Sai Srinivas B. Thulasi Thanmai A. David Donald et al.

Schlagwörter: machine learning, classification algorithms, real-life use cases, unique projects, comparison, performance evaluation, practicality, Python, 11. Sustainability

Building machine-learning models for reducing the severity of bicyclist road traffic injuries

Autoren: Slava Birfir Amir Elalouf Tova Rosenbloom

Quelle: Transportation Engineering, Vol 12, Iss , Pp 100179- (2023)

Schlagwörter: Transportation engineering, Bicyclist injury severity, Machine-learning classification algorithms, TA1001-1280

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2666691X23000192; https://doaj.org/toc/2666-691X

Zugangs-URL: https://doaj.org/article/b225feee33cf4b5786161ca5c0c021ea

Identification of fraudulent messages using artificial intelligence methods

Autoren: Survila, Kernius

Schlagwörter: sms spam, machine learning, classification algorithms, data preprocessing, web application

Dateibeschreibung: application/pdf

Zugangs-URL: https://repository.vu.lt/VU:ELABAETD229556706&prefLang=en_US

Machine Learning at the Service of Survival Analysis: Predictions Using Time-to-Event Decomposition and Classification Applied to a Decrease of Blood Antibodies against COVID-19

Autoren: Lubomír Štěpánek Filip Habarta Ivana Malá et al.

Quelle: Mathematics ; Volume 11 ; Issue 4 ; Pages: 819

Schlagwörter: time-to-event variable decomposition, time-to-event variable prediction, machine-learning classification algorithms, COVID-19, antibody blood level decrease, multivariate logistic regression, naïve Bayes classifier, support vector machines, decision trees and random forests, artificial neural networks

Dateibeschreibung: application/pdf

Relation: E3: Mathematical Biology; https://dx.doi.org/10.3390/math11040819

Verfügbarkeit: https://doi.org/10.3390/math11040819

Using Knowledge Discovery to Enhance Classification Techniques for Detect Malaria-Infected Red Blood Cells

Autoren: Alkrimi, J. A. Toma, A. Mohammed, R. S. et al.

Quelle: International Journal of Scientific Research in Network Security and Communication 8(1):1-6. 2020

Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS

Autoren: Mehak Arora Stephen C. Zambrzycki Joshua M. Levy et al.

Quelle: Metabolites, Vol 12, Iss 232, p 232 (2022)

Schlagwörter: DART-MS, ambient plasma ionization, solid phase micro-extraction, machine learning classification algorithms, K-means clustering, imbalanced learning, Microbiology, QR1-502

Relation: https://www.mdpi.com/2218-1989/12/3/232; https://doaj.org/toc/2218-1989; https://doaj.org/article/7239b35896ca44f3b4610dfc424c5e27

Verfügbarkeit: https://doi.org/10.3390/metabo12030232
https://doaj.org/article/7239b35896ca44f3b4610dfc424c5e27

Detecting the Most Important Classes from Software Systems with Self Organizing Maps

Autoren: Elena-Manuela MANOLE

Quelle: Studia Universitatis Babes-Bolyai: Series Informatica, Vol 66, Iss 1 (2021)

Schlagwörter: self organizing maps, artificial neural networks, machine learning, classification algorithms, search based software engineering, software visualisation, key class detection, condensing class diagrams, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: http://193.231.18.162/index.php/subbinformatica/article/view/1173; https://doaj.org/toc/2065-9601

Zugangs-URL: https://doaj.org/article/851e5d997cef41f9bf10991d5326671f

A real data-driven clustering approach for countries based on happiness score

Autoren: Chakraborty, Aditya Tsokos, Chris P.

Schlagwörter: ddc:330, C00, C02, C19, C40, C49, C65, Y91, Y10, Clustering Algorithms, Subjective Well Being (SWB), Stability Measures, Machine Learning Classification Algorithms, Economic Indicators

Relation: http://hdl.handle.net/10419/281616

Verfügbarkeit: http://hdl.handle.net/10419/281616
https://doi.org/10.24818/EA/2021/S15/1031

Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms

Autoren: Nicholas Fiorentini Massimo Losa

Quelle: Infrastructures, Vol 5, Iss 61, p 61 (2020)

Schlagwörter: crash severity, machine learning classification algorithms, random undersampling the majority class, random classification tree, k-nearest neighbor, random forest, Technology

Relation: https://www.mdpi.com/2412-3811/5/7/61; https://doaj.org/toc/2412-3811; https://doaj.org/article/b3da26cdab2944208f5a8313c9b6364f

Verfügbarkeit: https://doi.org/10.3390/infrastructures5070061
https://doaj.org/article/b3da26cdab2944208f5a8313c9b6364f

Comparison and analysis of logistic regression, neural networks and naïve bayes machine learning algorithms for predicting child labor in Turkey

Autoren: Bingöl, Mehmet Rohat Çilingirtürk, Ahmet Mete Marmara Üniversitesi et al.

Weitere Verfasser: Bingöl, Mehmet Rohat Çilingirtürk, Ahmet Mete Marmara Üniversitesi et al.

Schlagwörter: Random Forest, Lojistik Regresyon, Logistic Regression, Naïve Bayes, Rastgele Orman, Çocuk İşçiliği Machine Learning Classification Algorithms, Child Labor, Makine Öğrenmesi Sınıflandırma Algoritmaları

Dateibeschreibung: IX, 73 sayfa : grafik, şekil

Zugangs-URL: https://katalog.marmara.edu.tr/veriler/cokluortam/cokluortam/F/F/B/F/C/6513f76e3b2c7.pdf
https://hdl.handle.net/11424/295864

Comparison and analysis of logistic regression, neural networks and naïve bayes machine learning algorithms for predicting child labor in Turkey ; Loji̇sti̇k regresyon, naïve bayes ve rastgele orman maki̇ne öğreni̇mi̇ algori̇tmalarının karşılaştırılması ve anali̇zi̇ : Türki̇ye'de çocuk i̇şçi̇li̇ği̇ni̇n öngörülmesi̇ örneği̇

Autoren: Bingöl, Mehmet Rohat Çilingirtürk, Ahmet Mete Marmara Üniversitesi et al.

Weitere Verfasser: Bingöl, Mehmet Rohat Çilingirtürk, Ahmet Mete Marmara Üniversitesi et al.

Schlagwörter: Makine Öğrenmesi Sınıflandırma Algoritmaları, Lojistik Regresyon, Naïve Bayes, Rastgele Orman, Çocuk İşçiliği Machine Learning Classification Algorithms, Logistic Regression, Random Forest, Child Labor

Dateibeschreibung: IX, 73 sayfa : grafik, şekil

Relation: https://katalog.marmara.edu.tr/veriler/yordambt/cokluortam/1F/6513f76e3b2c7.pdf; https://hdl.handle.net/11424/295864

Verfügbarkeit: https://hdl.handle.net/11424/295864
https://katalog.marmara.edu.tr/veriler/yordambt/cokluortam/1F/6513f76e3b2c7.pdf

Analysis of Road Traffic Accidents to Identify Major causes of Accidents using Machine Learning Techniques: in The case of Addis Ababa City

Autoren: Tarikwa Tesfa

Schlagwörter: 11. Sustainability, 1. No poverty, Road traffic accident, Data analysis, Machine learning, Classification algorithms, Multi-class classification, Imbalance data, 3. Good health

Zugangs-URL: https://nadre.ethernet.edu.et/record/19664

Application of classification machine learning algorithms for characterizing nutrient transport in a clay plain agricultural watershed.

Autoren: Elsayed A Rixon S Levison J et al.

Quelle: Journal of environmental management [J Environ Manage] 2023 Nov 01; Vol. 345, pp. 118924. Date of Electronic Publication: 2023 Sep 06.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Academic Press Country of Publication: England NLM ID: 0401664 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-8630 (Electronic) Linking ISSN: 03014797 NLM ISO Abbreviation: J Environ Manage Subsets: MEDLINE

MeSH-Schlagworte: Algorithms* , Machine Learning*, Clay ; Nutrients ; Ontario ; Phosphorus

A preliminary study in classification of the severity of spine deformation in adolescents with lumbar/thoracolumbar idiopathic scoliosis using machine learning algorithms based on lumbosacral joint efforts during gait.

Autoren: Samadi B Raison M Mahaudens P et al.

Quelle: Computer methods in biomechanics and biomedical engineering [Comput Methods Biomech Biomed Engin] 2023 Sep; Vol. 26 (11), pp. 1341-1352. Date of Electronic Publication: 2022 Sep 10.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Informa Healthcare Country of Publication: England NLM ID: 9802899 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-8259 (Electronic) Linking ISSN: 10255842 NLM ISO Abbreviation: Comput Methods Biomech Biomed Engin Subsets: MEDLINE

MeSH-Schlagworte: Scoliosis*/diagnostic imaging , Scoliosis*/surgery , Kyphosis*, Humans ; Adolescent ; Spine ; Gait ; Joints ; Algorithms ; Lumbar Vertebrae/diagnostic imaging ; Lumbar Vertebrae/surgery ; Thoracic Vertebrae/surgery