Lessons learned from developing an inferential model for predicting food insecurity yield essential insights and actionable ...

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 ...

Abstract: The research investigates predicting the virality of content on social media platforms using a stacking ensemble model with Random Forest, XGBoost, and Logistic Regression. Social media ...

Abstract: The rapid development of the modern world has increased the use of social media. One of the most popular social media platforms in Indonesia is X, where users can share tweets to express ...

Researchers conducted a systematic review to assess the risk of bias and applicability of prediction models for fear of recurrence in patients with cancer.

Artificial intelligence (AI) is emerging as a powerful tool to predict food consumption patterns and guide policy decisions, ...

Efficient identification of individuals at high cardiovascular disease (CVD) risk is essential for prevention in middle-aged and older adults. The body roundness index (BRI), derived from waist ...

The rapid uptake of supervised machine learning (ML) in clinical prediction modelling, particularly for binary outcomes based on tabular data, has sparked debate about its comparative advantage over ...

This project evaluates how effectively static features extracted from Windows Portable Executable (PE) files can distinguish ransomware from benign software using supervised machine learning. This ...