Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

Abstract: Using machine learning applied to multimodal physiological data allows the classification of cognitive workload (low, moderate, or high load) during task performance. However, current ...

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 ...

Integrating deep learning in optical microscopy enhances image analysis, overcoming traditional limitations and improving classification and segmentation tasks.

Abstract: Advancements in machine learning (ML) have facilitated the prediction of key aspects of human locomotion, particularly in identifying subject gait trajectories essential for recognizing ...

AI chatbots, including commercial market leaders such as ChatGPT, Google Gemini, and Claude, dispense advice that heavily ...

Triage, Critical Clinical Workflow Process, Chest Pain, Electrocardiogram (EKG), Door-to-EKG (DTE) Time Share and Cite: ...