Suchergebnisse - Computing methodologies Machine learning Learning paradigms Multi-task learning

A narrative review of deep learning applications in sports performance analysis: current practices, challenges, and future directions.

Autoren: Jia, Yunke Anida Abdullah, Norli Eliza, Hafiz et al.

Quelle: BMC Sports Science, Medicine & Rehabilitation; 8/27/2025, Vol. 17 Issue 1, p1-20, 20p

Schlagwörter: DEEP learning, PREDICTION models, ATHLETIC ability, HUMAN activity recognition, REAL-time computing, MOTION detectors, PREVENTION of injury

Non-Federated Multi-Task Split Learning for Heterogeneous Sources

Autoren: Zheng, Yilin Eryilmaz, Atilla

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2406.00150

Spatiotemporal Modeling of Surface Water–Groundwater Interactions via Multi-Task Transformer-Based Learning.

Autoren: Jing, Hao Tian, Yong Zheng, Chunmiao

Quelle: Hydrology (2306-5338); Nov2025, Vol. 12 Issue 11, p291, 26p

Schlagwörter: GROUNDWATER, TRANSFORMER models, SPATIOTEMPORAL processes, RIVER channels, MACHINE learning, WATERSHEDS, WATER management

Geografische Kategorien: CHINA

An asymmetric lightweight multi-task network based on KAN convolution.

Autoren: Meng, Fanyun Sun, Yongqiang Zhu, Zhenyu et al.

Quelle: Soft Computing - A Fusion of Foundations, Methodologies & Applications. Nov2025, Vol. 29 Issue 21/22, p5845-5857. 13p.

Schlagwörter: *COMPUTER vision, *IMAGE segmentation, *MACHINE learning, *DEPTH maps (Digital image processing), *MATHEMATICAL optimization

Multi-Task Learning on Networks

Autoren: Ponti, Andrea

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, I.6.0, Computer Science - Artificial Intelligence, G.1.6, G.3, Computer Science - Neural and Evolutionary Computing, I.2.1, Machine Learning (cs.LG), Methodology (stat.ME), Artificial Intelligence (cs.AI), Optimization and Control (math.OC), 68T01, 65K10, 05C82, FOS: Mathematics, Neural and Evolutionary Computing (cs.NE), Mathematics - Optimization and Control, Statistics - Methodology

Zugangs-URL: http://arxiv.org/abs/2112.04891

CHRONIC KIDNEY DISEASE PREDICTION INTEGRATING CATEGORIZATION AND NOVEL MACHINE LEARNING TECHNIQUES.

Autoren: Lalitha, Ponnam Devika, Potta Manga, Vadthya et al.

Quelle: Cuestiones de Fisioterapia; 2025, Vol. 54 Issue 3, p3588-3597, 10p

Schlagwörter: FISHER discriminant analysis, CHRONIC kidney failure, PRINCIPAL components analysis, SUPPORT vector machines, HAZARDOUS wastes

Mathematical Optimization in Machine Learning for Computational Chemistry.

Autoren: Zekić, Ana

Quelle: Computation; Jul2025, Vol. 13 Issue 7, p169, 23p

Schlagwörter: MATHEMATICAL optimization, MACHINE learning, COMPUTATIONAL chemistry, SUBGRADIENT methods, SURROGATE-based optimization, ACTIVE learning, MOLECULAR dynamics

A Public Health Approach to Automated Pain Intensity Recognition in Chest Pain Patients via Facial Expression Analysis for Emergency Care Prioritization.

Autoren: Wiryasaputra, Rita Tsan, Yu-Tse Zhang, Qi-Xiang et al.

Quelle: Diagnostics (2075-4418); Oct2025, Vol. 15 Issue 20, p2661, 21p

Schlagwörter: CHEST pain, PAIN measurement, MACHINE learning, EMERGENCY medical services, REAL-time computing, EMOTION recognition, OBJECT recognition (Computer vision), PUBLIC health

GENE CANCER CLASSIFICATION USING ENHANCED TRANSFERABLE REINFORCEMENT LEARNING AND ENHANCED RESNET ALGORITHM.

Autoren: B., Sanjay Krishna C., Agjelia Lydia

Quelle: Cuestiones de Fisioterapia; 2025, Vol. 54 Issue 3, p4693-4721, 29p

Schlagwörter: MACHINE learning, REINFORCEMENT learning, DEEP learning, FEATURE selection, TUMOR classification

Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.

Autoren: Bi, Xueyuan Wang, Yangyang Wang, Jihan et al.

Quelle: Pharmaceutics; Sep2025, Vol. 17 Issue 9, p1186, 30p

Schlagwörter: MACHINE learning, PHARMACOLOGY, POLYPHARMACY, ETHICAL problems, ONCOLOGY, DEEP learning

Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning

Autoren: Lee, Anton Zhang, Yaqian Gomes, Heitor Murilo et al.

Weitere Verfasser: Lee, Anton Zhang, Yaqian Gomes, Heitor Murilo et al.

Quelle: CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management ; https://hal.science/hal-04468374 ; CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, Feb 2023, Birmingham (UK), United Kingdom. pp.4038--4042, ⟨10.1145/3583780.3615236⟩

Schlagwörter: Computing methodologies, Machine learning, Learning paradigms, Multi-task learning, Lifelong machine learning, Class-incremental continual learning, Parameter isolation, Anomaly detection, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]

Geographisches Schlagwort: Birmingham (UK), United Kingdom

Relation: info:eu-repo/semantics/altIdentifier/arxiv/2310.20052; ARXIV: 2310.20052

Verfügbarkeit: https://hal.science/hal-04468374
https://doi.org/10.1145/3583780.3615236

Advanced Techniques For Facial Expression Analysis In Individuals With Mental Retardation: A Machine Learning Perspective.

Autoren: Panpaliya, Mayuri N. Tijare, Pritish A.

Quelle: African Journal of Biomedical Research; 2025 Supplement, Vol. 28, p2145-2152, 8p

Schlagwörter: MACHINE learning, DEEP learning, INTELLECTUAL disabilities, RESEARCH methodology, EMOTION recognition, FACE-to-face communication

HISTOPATHOLOGICAL IMAGE ANALYSIS, DEEP LEARNING AND THE INTEGRATION OF LARGE LANGUAGE MODELS FOR BIOMEDICAL KNOWLEDGE REASONING, DIAGNOSTIC REPORTING AND EXPLAINABILITY.

Autoren: Kumar, Sanjeev Kumar, Ankit Bhardwaj, Ankur et al.

Quelle: Biochemical & Cellular Archives; Oct2025, Vol. 25 Issue 2, p2175-2180, 6p

Schlagwörter: DEEP learning, HISTOPATHOLOGY, LANGUAGE models, DIAGNOSTIC examinations, CONVOLUTIONAL neural networks, TRANSFORMER models, MEDICAL specialties & specialists

Recent advances in deep learning for protein-protein interaction: a review.

Autoren: Cui, Jiafu Yang, Siqi Yi, Litai et al.

Quelle: BioData Mining; 6/16/2025, Vol. 18 Issue 1, p1-23, 23p

Schlagwörter: ARTIFICIAL neural networks, ARTIFICIAL intelligence, MACHINE learning, COMPUTATIONAL biology, CONVOLUTIONAL neural networks, DEEP learning

Reinforcement Learning for Dynamic Security Policy Enforcement in Communication Networks.

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

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

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

Multi-Label Multi-Task Deep Learning for Behavioral Coding

Autoren: James Gibson David C. Atkins Torrey A. Creed et al.

Quelle: IEEE Transactions on Affective Computing. 13:508-518

Schlagwörter: FOS: Computer and information sciences, Computer Science - Computation and Language, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Computation and Language (cs.CL)

Zugangs-URL: http://arxiv.org/pdf/1810.12349
https://pubmed.ncbi.nlm.nih.gov/36704750
http://arxiv.org/abs/1810.12349
https://arxiv.org/abs/1810.12349
http://ui.adsabs.harvard.edu/abs/2018arXiv181012349G/abstract
https://dblp.uni-trier.de/db/journals/corr/corr1810.html#abs-1810-12349
https://arxiv.org/pdf/1810.12349.pdf
https://ieeexplore.ieee.org/abstract/document/8894557
https://arxiv.org/abs/1810.12349
https://arxiv.org/pdf/1810.12349

Deep Learning Personalization Methods in Peer-to-peer Heterogeneous Systems ; Metode personalizacije dubokog učenja u istorazinskim heterogenim sustavima

Autoren: Šajina, Robert Ipšić, Ivo Tanković, Nikola

Weitere Verfasser: Šajina, Robert Ipšić, Ivo Tanković, Nikola

Schlagwörter: DRUŠTVENE ZNANOSTI. Informacijske i komunikacijske znanosti, SOCIAL SCIENCES. Information and Communication Sciences, Računalna znanost i tehnologija. Računalstvo. Obrada podataka, Computer science and technology. Computing. Data processing, info:eu-repo/classification/udc/004(043.3), peer-to-peer, non-IID, machine learning, natural language processing, personalization, multi-task, istorazinsko učenje, neovisno i nejednako distribuirani skupovi podataka, strojno učenje, obrada prirodnog jezika, personalizacija, višezadaćno učenje

Dateibeschreibung: application/pdf

Relation: https://www.unirepository.svkri.uniri.hr/islandora/object/infri:1394; https://urn.nsk.hr/urn:nbn:hr:195:812527; https://www.unirepository.svkri.uniri.hr/islandora/object/infri:1394/datastream/PDF

Verfügbarkeit: https://www.unirepository.svkri.uniri.hr/islandora/object/infri:1394
https://urn.nsk.hr/urn:nbn:hr:195:812527
https://www.unirepository.svkri.uniri.hr/islandora/object/infri:1394/datastream/PDF

The Role of Artificial Intelligence in Bathing Water Quality Assessment: Trends, Challenges, and Opportunities.

Autoren: Khan, M Usman Saeed Battamo, Ashenafi Yohannes Ravindar, Rajendran et al.

Quelle: Water (20734441); Nov2025, Vol. 17 Issue 21, p3176, 32p

Schlagwörter: ARTIFICIAL intelligence, WATER quality, PREDICTION models, MACHINE learning, REMOTE sensing devices, ENVIRONMENTAL monitoring, PUBLIC health, DATA integration

Advances in cardiovascular signal analysis with future directions: a review of machine learning and deep learning models for cardiovascular disease classification based on ECG, PCG, and PPG signals.

Autoren: Fuadah, Yunendah Nur Lim, Ki Moo

Quelle: Biomedical Engineering Letters; Jul2025, Vol. 15 Issue 4, p619-660, 42p

Artificial Intelligence (AI) in Pharmaceutical Formulation and Dosage Calculations.

Autoren: Joshi, Sameer Sheth, Sandeep

Quelle: Pharmaceutics; Nov2025, Vol. 17 Issue 11, p1440, 21p

Schlagwörter: ARTIFICIAL intelligence, MACHINE learning, PHARMACOKINETICS, NATURAL language processing, EXCIPIENTS, PHARMACEUTICAL arithmetic, INDIVIDUALIZED medicine, DEEP learning