Machine learning sounds math-heavy, but modern tools make it far more accessible. Here’s how I built models without deep math ...

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 ...

Objective: This study evaluated the feasibility of using a smartphone app to predict mental health risks in non-clinical adolescents by integrating active and passive data streams within a machine ...

Abstract: Jupyter notebooks have become central in data science, integrating code, text and output in a flexible environment. With the rise of machine learning (ML), notebooks are increasingly used ...

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Machine learning for health data science, fuelled by proliferation of data and reduced computational ...

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...

The Perspective by Tiwary et al. (8) offers a comprehensive overview of generative AI methods in computational chemistry. Approaches that generate new outputs (e.g., inferring phase transitions) by ...

With the accelerating pace of urbanization, the issue of air pollution has become increasingly severe. Notably, carbon monoxide (CO), as a prevalent harmful gas, poses potential threats to both human ...