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., ...

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) ...
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, ...
The New York Times

Paris Paralympics 2024: How the classification system works

PR1, W1, T51, F58, SL4, KL3, SM11. This is not a test to crack a code. But you will see a series of letter and number combinations while engaging with the Paralympics in Paris. At the Olympics, there ...
Frontiers

Paired competing neurons improving STDP supervised local learning in spiking neural networks

Direct training of Spiking Neural Networks (SNNs) on neuromorphic hardware has the potential to significantly reduce the energy consumption of artificial neural network training. SNNs trained with ...