Search Results - Computing methodologies Machine learning Machine learning approaches Stochastic games~

Information maximization for a broad variety of multi-armed bandit games

Authors: Barbier--Chebbah, Alex Vestergaard, Christian, L. Masson, Jean-Baptiste et al.

Contributors: Barbier--Chebbah, Alex Vestergaard, Christian, L. Masson, Jean-Baptiste et al.

Source: https://pasteur.hal.science/pasteur-04997815 ; 2025.

Subject Terms: Statistical Inference, Infomax, Multi-armed Bandit, Decision and information theory, Exploration-exploitation, 02.50.Tt89.70.+c, MESH: Decision Theory Information Theory, MESH: Information Theory, MESH: Algorithms, JEL: C - Mathematical and Quantitative Methods/C.C7 - Game Theory and Bargaining Theory/C.C7.C73 - Stochastic and Dynamic Games • Evolutionary Games • Repeated Games, ACM: G.: Mathematics of Computing/G.1: NUMERICAL ANALYSIS/G.1.2: Approximation/G.1.2.6: Minimax approximation and algorithms, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.2: Design Methodology, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]

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Machine Learning for Basketball Game Outcomes: NBA and WNBA Leagues.

Authors: Alves, João M. Barbosa, Ramiro S.

Source: Computation; Oct2025, Vol. 13 Issue 10, p230, 31p

Subject Terms: MACHINE learning, BASKETBALL, FORECASTING, ARTIFICIAL intelligence, WOMEN'S basketball, DEEP learning, ECONOMIC competition

Company/Entity: WOMEN'S National Basketball Association, NATIONAL Basketball Association

RAVE-HD: A Novel Sequential Deep Learning Approach for Heart Disease Risk Prediction in e-Healthcare.

Authors: Khan, Muhammad Jaffar Raza, Basit Faheem, Muhammad

Source: Diagnostics (2075-4418); Nov2025, Vol. 17 Issue 22, p2866, 50p

Subject Terms: HEART diseases, DEEP learning, MEDICAL screening, FEATURE selection, MACHINE learning, PREDICTION models, DIGITAL health, STATISTICAL bias

A multimodal approach to psychological resilience prediction in football players: Integrating biomechanical analysis, physiological feedback, and machine learning.

Authors: Wu, Chen

Source: Molecular & Cellular Biomechanics; 2025, Vol. 22 Issue 5, p1-16, 16p

Subject Terms: PSYCHOLOGICAL resilience, BIOMECHANICS, PREDICTION models, SOCCER players, MATHEMATICAL optimization, PHYSIOLOGY, STRESS management, MACHINE learning

Predicting the Landscape Epidemiology of Foot-and-Mouth Disease in Endemic Regions: An Interpretable Machine Learning Approach.

Authors: Alkhamis, Moh A. Abouelhassan, Hamad Alateeqi, Abdulaziz et al.

Source: Viruses (1999-4915); Oct2025, Vol. 17 Issue 10, p1383, 20p

Subject Terms: FOOT & mouth disease, EPIDEMIOLOGY, LANDSCAPES, RISK assessment, ANIMAL health, MACHINE learning, PREDICTION models

Geographic Terms: MIDDLE East, ARABIAN Peninsula

Stochastic Multiscale Modeling of Electrical Conductivity of Carbon Nanotube Polymer Nanocomposites: An Interpretable Machine Learning Approach.

Authors: Elaskalany, Mostafa Behdinan, Kamran

Source: Advanced Engineering Materials; Dec2024, Vol. 26 Issue 23, p1-13, 13p

Subject Terms: MACHINE learning, ARTIFICIAL neural networks, POLYMERIC nanocomposites, ELECTRIC conductivity, MULTISCALE modeling, CARBON nanotubes

Capturing the complexity of human strategic decision-making with machine learning.

Authors: Zhu, Jian-Qiao Peterson, Joshua C. Enke, Benjamin et al.

Source: Nature Human Behaviour; Oct2025, Vol. 9 Issue 10, p2114-2120, 7p

Virtual reality-assisted visualization of flood susceptibility using optimized machine learning models.

Authors: Razavi-Termeh, Seyed Vahid Safari Bazargani, Jalal Sadeghi-Niaraki, Abolghasem et al.

Source: Applied Water Science; Oct2025, Vol. 15 Issue 10, p1-21, 21p

Subject Terms: VIRTUAL reality, MACHINE learning, OPTIMIZATION algorithms, FLOOD risk, ENVIRONMENTAL mapping, GEOGRAPHICAL positions, RANDOM forest algorithms

Geographic Terms: FARS (Iran)

Strategic Decision-Making in SMEs: A Review of Heuristics and Machine Learning for Multi-Objective Optimization.

Authors: Molina-Abril, Gines Calvet, Laura Juan, Angel A. et al.

Source: Computation; Jul2025, Vol. 13 Issue 7, p173, 22p

Subject Terms: MACHINE learning, MULTI-objective optimization, ORGANIZATIONAL resilience, SMALL business, DIGITAL transformation, STRATEGIC planning, MATHEMATICAL optimization, HEURISTIC

Graph Fourier Deep Learning for Spatiotemporal and Hydrogeological Interpretation of Groundwater Levels in the Yellow River Basin.

Authors: Wu, Zhenjiang Yao, Yingying Guo, Shuitao et al.

Source: Water Resources Research; Nov2025, Vol. 61 Issue 11, p1-20, 20p

Subject Terms: HYDROGEOLOGY, WATER table, DEEP learning, WATERSHEDS, SPATIOTEMPORAL processes, CLIMATE change, PREDICTION models

Geographic Terms: YELLOW River (China), CHINA

Unravelling emotions: exploring deep learning approaches for EEG-based emotion recognition with current challenges and future recommendations.

Authors: Abgeena, Abgeena Garg, Shruti

Source: Cognitive Neurodynamics; 10/23/2025, Vol. 19 Issue 1, p1-32, 32p

Positive–Unlabeled Learning-Based Hybrid Models and Interpretability for Groundwater Potential Mapping in Karst Areas.

Authors: Bi, Benteng Li, Jingwen Luo, Tianyu et al.

Source: Water (20734441); May2025, Vol. 17 Issue 10, p1422, 26p

Subject Terms: MACHINE learning, K-nearest neighbor classification, RANDOM forest algorithms, KARST, WATERSHEDS

Intelligent Real-Time Fitness Observation System Using IoT and Deep Learning.

Authors: kiran, Kasi Venkata Rao, T. Srinivasa

Source: Cuestiones de Fisioterapia; 2025, Vol. 54 Issue 3, p2651-2665, 15p

Subject Terms: ARTIFICIAL neural networks, MACHINE learning, ATHLETES' health, DEEP learning, WOUNDS & injuries

Advancements in Breast Cancer Detection: A Review of Global Trends, Risk Factors, Imaging Modalities, Machine Learning, and Deep Learning Approaches.

Authors: Rahman, Md. Atiqur Khan, M. Saddam Hossain Watanobe, Yutaka et al.

Source: BioMedInformatics; Sep2025, Vol. 5 Issue 3, p46, 70p

Reinforcement Learning for Dynamic Security Policy Enforcement in Communication Networks.

Authors: Raju, Konda Babu, M. chunchure, Basavaraj et al.

Source: Cuestiones de Fisioterapia; 2025, Vol. 54 Issue 4, p1304-1311, 8p

Subject Terms: MACHINE learning, SOFTWARE-defined networking, REINFORCEMENT learning, COMPUTER network security, CYBERTERRORISM

Different Time-Increment Rainfall Prediction Models: a Machine Learning Approach Using Various Input Scenarios.

Authors: Rahimi, Anas Yashooa, Noor Kh. Ahmed, Ali Najah et al.

Source: Water Resources Management; Mar2025, Vol. 39 Issue 4, p1677-1696, 20p

Subject Terms: WATER management, MACHINE learning, FLOOD forecasting, METEOROLOGICAL stations, ARTIFICIAL intelligence

Geographic Terms: MALAYSIA

Factors, Prediction, Explainability, and Simulating University Dropout Through Machine Learning: A Systematic Review, 2012–2024.

Authors: Quimiz-Moreira, Mauricio Delgadillo, Rosa Parraga-Alava, Jorge et al.

Source: Computation; Aug2025, Vol. 13 Issue 8, p198, 47p

Subject Terms: COLLEGE dropouts, MACHINE learning, SCHOOL dropouts, PREDICTION models, COMPUTER simulation

Leak Management in Water Distribution Networks Through Deep Reinforcement Learning: A Review.

Authors: Javed, Awais Wu, Wenyan Sun, Quanbin et al.

Source: Water (20734441); Jul2025, Vol. 17 Issue 13, p1928, 30p

Subject Terms: REINFORCEMENT learning, LEAK detection, RESOURCE allocation, MACHINE learning, AQUEDUCTS, ARTIFICIAL intelligence, LEAKAGE, WATER conservation

Deep Learning-Based Daily Streamflow Prediction Model for the Hanjiang River Basin.

Authors: Huang, Jianze Chen, Jialang Huang, Haijun et al.

Source: Hydrology (2306-5338); Jul2025, Vol. 12 Issue 7, p168, 19p

Subject Terms: STREAMFLOW, DEEP learning, LONG short-term memory, WATERSHEDS, FLOOD damage prevention, CONVOLUTIONAL neural networks, UNCERTAINTY (Information theory)

Geographic Terms: CHINA

Deep learning in microbiome analysis: a comprehensive review of neural network models.

Authors: Przymus, Piotr Rykaczewski, Krzysztof Martín-Segura, Adrián et al.

Source: Frontiers in Microbiology; 2025, p1-27, 27p

Subject Terms: MACHINE learning, ARTIFICIAL neural networks, NATURAL language processing, PATTERN recognition systems, DEEP learning, FEATURE extraction