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 ...

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

The compressive strength (CS) is the most important parameter in the design codes of reinforced concrete structures. The development of simple mathematical equations for the prediction of CS of ...

Researchers at Karolinska Institutet and KTH have developed a computational method that can reveal how cells change and specialize in the body. The study, which has been published in the journal PNAS, ...

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.

The workflow encompasses patient datacollection and screening, univariate regression analysis for initial variable selection, systematic comparison of 91 machine learning models,selection and ...