Machine learning can help predict whether people newly diagnosed with MS will experience disability worsening that occurs ...

de Filippis, R. and Al Foysal, A. (2026) Cross-Population Transfer Learning for Antidepressant Treatment Response Prediction: A SHAP-Based Explainability Approach Using Synthetic Multi-Ethnic Data.

Researchers from Tokyo Metropolitan University have developed a suite of algorithms to automate the counting of sister ...

Researchers from Tokyo Metropolitan University have developed a suite of algorithms to automate the counting of sister ...

Researchers from Tokyo Metropolitan University have developed a suite of algorithms to automate the counting of sister chromatid ...

This project implements an advanced Virtual Machine Placement (VMP) optimization system that leverages multi-objective genetic algorithms, machine learning predictions, blockchain technology, and ...

A research team introduces a fully automated, non-destructive phenotyping platform that combines X-ray fluorescence microscopy with computer vision and machine learning.

Abstract: Artificial intelligence has transformed healthcare through improved disease prediction and diagnostic accuracy. However, the prediction of osteoporosis remains challenging due to limitations ...

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 detecting cyber attacks using machine learning techniques. It employs various algorithms to analyze network traffic and identify potential threats in real-time.