Search Results - Computing methodologies Machine learning Learning paradigms Supervised learning~

Improving Coronary Artery Disease Diagnosis in Cardiac MRI with Self-Supervised Learning.

Authors: Khalid, Usman Kaya, Mehmet Alhajj, Reda

Source: Diagnostics (2075-4418); Oct2025, Vol. 15 Issue 20, p2618, 22p

Subject Terms: CORONARY artery disease, CARDIAC magnetic resonance imaging, MACHINE learning, SUPERVISED learning, MEDICAL software, CLASSIFICATION algorithms

Machine Learning-Based Prediction of Three-Year Heart Failure and Mortality After Premature Ventricular Contraction Ablation.

Authors: Lin, Chung-Yu Lai, Yu-Te Chuang, Chien-Wei et al.

Source: Diagnostics (2075-4418); Nov2025, Vol. 17 Issue 21, p2693, 18p

Subject Terms: HEART failure, CATHETER ablation, RISK assessment, MACHINE learning, DEATH forecasting, SUPERVISED learning, ARRHYTHMIA

Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.

Authors: Akbarein, Hesameddin Taaghi, Mohammad Hussein Mohebbi, Mahyar et al.

Source: Veterinary Medicine & Science. May2025, Vol. 11 Issue 3, p1-29. 29p.

Subjects: Machine learning, Artificial intelligence, Veterinary medicine, Resource allocation, Medical research

A Self-Supervised Pre-Trained Transformer Model for Accurate Genomic Prediction of Swine Phenotypes.

Authors: Xiang, Weixi Li, Zhaoxin Sun, Qixin et al.

Source: Animals (2076-2615); Sep2025, Vol. 15 Issue 17, p2485, 17p

Subject Terms: DEEP learning, TRANSFORMER models, GENETIC profile, ANIMAL breeding, HERITABILITY, SUPERVISED learning, SWINE breeds

Contrastive self-supervised learning for neurodegenerative disorder classification.

Authors: Gryshchuk, Vadym Singh, Devesh Teipel, Stefan et al.

Source: Frontiers in Neuroinformatics; 2025, p1-13, 13p

Subject Terms: MACHINE learning, CONVOLUTIONAL neural networks, SUPERVISED learning, FRONTOTEMPORAL lobar degeneration, ALZHEIMER'S disease

Machine-learning for quantitative histopathology of piglet intestinal tissues: challenges with limited training data.

Authors: Becker, Cecilie Brandt Hansen, Mette Sif Nielsen, Søren Saxmose et al.

Source: Frontiers in Veterinary Science. 2025, p1-11. 11p.

Exploring internal representations of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects.

Authors: Kataoka, Asaki Nagano, Yoshihiro Oizumi, Masafumi

Source: Frontiers in Computational Neuroscience; 2025, p1-17, 17p

Evaluating the efficacy of bioelectrical impedance analysis using machine learning models for the classification of goats exposed to Haemonchosis.

Authors: Siddique, Aftab Batchu, Phaneendra Shaik, Arshad et al.

Source: Frontiers in Veterinary Science. 2025, p1-11. 11p.

Embracing Computer Vision for Diagnostic Maxillofacial Imaging -- An Artificial Intelligence Machine Learning (AIML)Pilot Project.

Authors: Akadiri, Oladimeji Adeniyi Yarhere, Kesiena Seun

Source: Nigerian Medical Journal; 2025, Vol. 66 Issue 3, p973-982, 10p

Subject Terms: ARTIFICIAL intelligence, MACHINE learning, CLINICAL decision making, DIAGNOSIS, COMPUTER vision, JAWS, DIGITAL health, COMPUTER-assisted image analysis (Medicine)

Synergizing Intuitive Physics and Big Data in Deep Learning: Can We Obtain Process Insights While Maintaining State‐Of‐The‐Art Hydrological Prediction Capability?

Authors: He, Leilei Shi, Liangsheng Song, Wenxiang et al.

Source: Water Resources Research; Dec2024, Vol. 60 Issue 12, p1-22, 22p

Subject Terms: ARTIFICIAL neural networks, ARTIFICIAL intelligence, SUPERVISED learning, DEEP learning, PHYSICAL laws

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

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

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

Subject Terms: MACHINE learning, PHARMACOLOGY, POLYPHARMACY, ETHICAL problems, ONCOLOGY, DEEP learning

A systematic review of deep learning-based object detection methods for crop grading

Authors: Xiangjian Zhu Ling Ren Miao Yang et al.

Source: CABI Agriculture and Bioscience.

Quantum-Inspired Data Embedding for Unlabeled Data in Sparse Environments: A Theoretical Framework for Improved Semi-Supervised Learning without Hardware Dependence

Authors: Shawn Ray

Source: Sakarya University Journal of Computer and Information Sciences, Vol 7, Iss 3, Pp 470-481 (2024)
Volume: 7, Issue: 3470-481
Sakarya University Journal of Computer and Information Sciences

Subject Terms: Yazılım Mühendisliği (Diğer), Quantum-Inspired Data Embedding, High-Dimensional Data, Entangled Representations, quantum-inspired data embedding, QA75.5-76.95, Information technology, 02 engineering and technology, T58.5-58.64, Computer Software, entangled representations, high-dimensional data, Electronic computers. Computer science, 0202 electrical engineering, electronic engineering, information engineering, Software Engineering (Other), Bilgisayar Yazılımı

File Description: application/pdf

Access URL: https://doaj.org/article/a35cfe1c3b6b4520896484eabe94d85b
https://dergipark.org.tr/tr/pub/saucis/issue/87942/1564497

MedMAE: A Self-Supervised Backbone for Medical Imaging Tasks.

Authors: Gupta, Anubhav Osman, Islam Shehata, Mohamed S. et al.

Source: Computation; Apr2025, Vol. 13 Issue 4, p88, 15p

Subject Terms: COMPUTER-assisted image analysis (Medicine), DIAGNOSTIC imaging, AUTOENCODERS, TRANSFORMER models, VISUAL learning, DEEP learning

Beyond conventional methods: A cost-benefit analysis of industry 5.0 inspired machinery for solid waste management in the digital circular economy.

Authors: Govindaraj, Manoj Gnanasekaran, Chandramowleeswaran Ramasamy, Mariyappan Muniyappan Sengodan et al.

Source: AIP Conference Proceedings; 2024, Vol. 3192 Issue 1, p1-15, 15p

Subject Terms: SOLID waste management, CIRCULAR economy, REINFORCEMENT learning, COST benefit analysis, WASTE management, DEEP learning

Supervised machine learning computing paradigm to measure melting and dissipative effects in entropy induced Darcy–Forchheimer flow with ternary-hybrid nanofluids.

Authors: Qureshi, Hamid Shah, Zahoor Raja, Muhammad Asif Zahoor et al.

Source: Numerical Heat Transfer: Part B -- Fundamentals; 2025, Vol. 86 Issue 12, p4168-4189, 22p

Subject Terms: ENTROPY, NANOFLUIDS, SUPERVISED learning, THERMOPHYSICAL properties, HEAT balance (Engineering), NUMERICAL analysis, FLUID flow, ARTIFICIAL neural networks

AFedSLL-LDL: a framework based-on federated self-supervised learning and lightweight deep learning for attack detection in serverless edge computing.

Authors: Bazargani, Mahdi Esfahani, Sina Tarkesh Heidari, Behnam et al.

Source: Cluster Computing; Dec2025, Vol. 28 Issue 15, p1-22, 22p

Subject Terms: EDGE computing, FEDERATED learning, SUPERVISED learning, ENERGY consumption, ANOMALY detection (Computer security), DATA privacy, INTERNET of things, DEEP learning

COVID-19 Data Analysis: The Impact of Missing Data Imputation on Supervised Learning Model Performance.

Authors: Mello-Román, Jorge Daniel Martínez-Amarilla, Adrián

Source: Computation; Mar2025, Vol. 13 Issue 3, p70, 23p

Subject Terms: ARTIFICIAL neural networks, COVID-19 pandemic, SUPPORT vector machines, MISSING data (Statistics), MACHINE learning

Don't push the button! Exploring data leakage risks in machine learning and transfer learning.

Authors: Apicella, Andrea Isgrò, Francesco Prevete, Roberto

Source: Artificial Intelligence Review; Nov2025, Vol. 58 Issue 11, p1-58, 58p

Subject Terms: MACHINE learning, TRANSFER of training, EVALUATION methodology, SUPERVISED learning, DATA security failures

Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach.

Authors: Tabassum, Sumaira Khan, M. Jawad Iqbal, Javaid et al.

Source: Frontiers in Computational Neuroscience; 2025, p1-17, 17p

Subject Terms: CONVOLUTIONAL neural networks, CHROMOSOME abnormalities, ALZHEIMER'S disease, PARKINSON'S disease, HYBRID systems, DEEP learning