IEEE

Predicting Reels Addiction Using Machine Learning

The problem of Reels video addiction is on the rise, particularly among young people. Through the use of machine learning, this study aims to predict Reels video addiction. The dataset used in this ...
Frontiers

Salp swarm-optimized machine learning models for predicting preoperative aortic rupture risk in acute type a aortic dissection patients

Acute Type A aortic dissection (ATAAD) is characterized by acute onset and rapid progression, with aortic rupture due to dissection extension being the primary lethal mechanism. Timely identification ...
C&EN

Machine Learning for Predicting Environmental Mobility Based on Retention Behavior

Queensland Alliance for Environmental Health Sciences (QAEHS), 20 Cornwall Street, Woolloongabba, Brisbane, QLD 4102, Australia ARC Training Centre for Hyphenated Analytical Separation Technologies ...
IEEE

Optimized LSTM-based Machine Learning Framework for Real-Time Crime Hotspot Prediction

Abstract: The detection of crime hotspot can improve public safety, assist the law enforcement authorities in resource allocation, and deter crimes. The crime patterns, however, are often complex and ...
EurekAlert!

Geophysical-machine learning tool for continuous subsurface geomaterials characterization

Researchers utilize 2D electrical resistivity imaging and borehole data to estimate the N60-value of soils with k-means clustering technique Thailand's northern regions, characterized by complex ...
Frontiers

Interpretable machine learning for predicting early neurological deterioration in symptomatic intracranial atherosclerotic stenosis

1 Department of Neurology, Jiangyin Clinical College of Xuzhou Medical University, Jiangyin, Jiangsu, China 2 Department of Neurology, Jiangyin Fifth People's Hospital, Jiangyin, Jiangsu, China ...