IEEE

Dynamic Classification: Leveraging Self-Supervised Classification to Enhance Prediction Performance

Abstract: In this study, we propose an innovative dynamic classification algorithm aimed at achieving zero missed detections and minimal false positives, critical in safety-critical domains (e.g., ...
Tech Xplore

Reinforcement learning accelerates model-free training of optical AI systems

Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...

AI revolutionises sleep disorder diagnosis: IIITH leads breakthroughs

Hyderabad: Artificial Intelligence (AI) is transforming the way sleep disorders are diagnosed, with researchers at the ...
Learn With Jay on MSNOpinion

Supervised learning made easy: Real-world example explained

In this video, we will study Supervised Learning with Examples. We will also look at types of Supervised Learning and its ...
Scientific Research Publishing

Multimodal Digital Phenotyping for Bipolar Disorder: Robust Mood-State Classification and Early Relapse Risk Monitoring ()

Bipolar Disorder, Digital Phenotyping, Multimodal Learning, Face/Voice/Phone, Mood Classification, Relapse Prediction, T-SNE, Ablation Share and Cite: de Filippis, R. and Al Foysal, A. (2025) ...
Analytics Insight

Top Java Libraries for AI and Machine Learning in 2025

Overview: In 2025, Java is expected to be a solid AI and machine-learning language.Best Java libraries for AI in 2025 can ease building neural networks, predict ...
IEEE

Time Series Classification Based on Supervised Contrastive Learning and Homoscedastic Uncertainty

Abstract: In recent years, contrastive learning (CL) frameworks have been widely applied to multivariate time series classification (MTSC) tasks. However, existing methods lack task-specific guidance, ...