From predictive road safety and telematics to automated communications and compliance, the industry is shifting towards ...

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 ...

The irreversible process of civilization has continuously tried to address the oldest issues of humanity. Car accidents are ...

AI is the broad goal of creating intelligent systems, no matter what technique is used. In comparison, Machine Learning is a specific technique to train intelligent systems by teaching models to learn ...

This project focuses on predicting delivery speed in a food delivery system using machine learning models. By analyzing various factors such as the distance between the restaurant and the customer, ...

Kristi Noem, the homeland security secretary, began handing out $10,000 checks to T.S.A. workers who “went above and beyond” during the shutdown, after the president recommended similar bonuses for ...

Introduction: Cardiovascular diseases (CVDs) are complex and affect a large part of the world’s population; early accurate and timely prediction is also complicated. Typically, predicting CVDs ...

DeepTrafficQ is a reinforcement learning-based traffic signal control system that uses Deep Q-Networks (DQN) to minimize vehicle waiting times at a 4-way intersection. By leveraging Q-learning with ...

Abstract: Traffic flow prediction plays a crucial role in Intelligent Transportation Systems (ITS), as it substantially enhances traffic management efficiency, alleviates congestion, and improves road ...

Introduction: Middle-aged and older adults are highly susceptible to depression. For this reason, early identification and intervention can substantially reduce its prevalence. This study innovatively ...