Scientific Research Publishing

Early Alzheimer’s Disease Detection from Short Speech Samples Using Lightweight, Interpretable Linguistic Markers ()

Based Detection, Linguistic Biomarkers, Machine Learning, Explainable AI, Cognitive Decline Monitoring Share and Cite: de Filippis, R. and Al Foysal, A. (2025) Early Alzheimer’s Disease Detection from ...
Tech Xplore

Machine learning helps robots see clearly in total darkness using infrared

From disaster zones to underground tunnels, robots are increasingly being sent where humans cannot safely go. But many of ...
European Medical Journal

Machine Learning Shows Promise in Predicting RA Treatment Response

A scoping review shows machine learning models may help predict response to biologic and targeted synthetic DMARDs in ...
Opinion
Communications of the ACMOpinion

AI Rewrites the Rules Of Phishing, Cybercrime

AI is the ultimate force multiplier for cybercriminals, because it makes scams faster, cheaper, and more convincing at scale. It can automate tasks that criminals used to do manually—or would never ...
Analytics Insight

Data Science: Building Machine Learning Models, Harvard University

Harvard University presents its eight-week online course through edX, which imparts to students essential knowledge of ...
Science X Network

Advancing scalability, efficiency, and reliability of over-parameterized machine learning models

Science X is a network of high quality websites with most complete and comprehensive daily coverage of the full sweep of science, technology, and medicine news ...
Analytics Insight

10 Most Common Machine Learning Mistakes and Tips to Fix Them

Overview: Machine learning failures usually start before modeling, with poor data understanding and preparation.Clean data, ...
IEEE

Structured Regularization Using Approximate Morphology for Alzheimer's Disease Classification

Abstract: Structured regularization allows machine learning models to consider spatial relationships among parameters, leading to results that generalize better and are more interpretable compared to ...
IEEE

Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization

Abstract: Domain shift is a formidable issue in Machine Learning that causes a model to suffer from performance degradation when tested on unseen domains. Federated Domain Gener-alization (FedDG) ...