IEEE

Feature Selection for Battery Lifetime Prediction using Explainable Machine Learning

Abstract: Accurate battery lifetime estimation is crucial for health management and system safety. Data-driven research yields extensive feature sets, yet optimal feature selection is often impeded by ...
Journal of Medical Internet Research

Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review

Passive sensing via wearable devices and smartphones, combined with machine learning (ML), enables objective, continuous, and noninvasive mental health monitoring. Objective: This study aimed to ...
IEEE

Hybrid Deep Machine Learning Feature Selection for High-Dimensional Cybersecurity Data

Abstract: The rapid increase in cyber threats has heightened the demand for Intrusion Detection Systems (IDS) that are both accurate and efficient. While deep learning models outperform traditional ...
entrackr

Flipkart Minutes introduces ‘XtraSaver’ feature to select users

Flipkart’s quick commerce arm, Flipkart Minutes, has begun showing a new savings feature called XtraSaver to a limited set of users, based on in-app observations and user-shared screenshots. The ...
Medical Xpress

Machine learning can predict patients' responses to antidepressants—while disentangling drug and placebo effects

Depression is one of the most widespread mental health disorders worldwide, affecting approximately 4% of the global population. It is characterized by a persistent low mood, disruptions in typical ...
Frontiers

Reinforcement Learning-Guided Optimization of ECG Features for Enhanced Heart Disease Prediction using Stacked CNNs

The final, formatted version of the article will be published soon. Accurate and timely heart disease diagnosis through intelligent ECG signal processing is essential to reducing death rates and ...