IEEE

Enhanced Traffic Prediction through Machine Learning Techniques

Abstract: Effective traffic management is a cornerstone of sustainable urban living, as cities face mounting challenges from increasing population density, unpredictable weather conditions, and social ...
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 ...
GitHub

thuzarwin/traffic-prediction-1

Then edit main.py so that it uses your own parameters for the network. It will attempt to store hyperparameter results in mongodb. You can use show_results.py to view them. Please keep in mind this ...
IEEE

Road Traffic Congestion Prediction Using Discrete Event Simulation and Regression Machine Learning Models

Abstract: Road traffic management enters a new era with the automatic collection and analysis of big data. The traffic data could be collected continuously using various IoT sensors (light, video, etc ...
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 ...