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 ...

Williams, A. and Louis, L. (2026) Cumulative Link Modeling of Ordinal Outcomes in the National Health Interview Survey Data: Application to Depressive Symptom Severity. Journal of Data Analysis and ...

Corporate leaders are racing to hire artificial intelligence talent, convinced that a few high-profile specialists can ...

Chad Beam provides the ins and outs of the implementation of GIS, advanced communication systems and predictive modeling to ensure that staffing, to whatever extent, is utilized most effectively. A ...

A predictive model identifies RA patients at risk of D2T-RA, using machine learning and real-world data for early intervention. Patient-reported outcomes, such as pain and fatigue, are stronger ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

Joseph Alderman et al argue that predictive models in healthcare lack adequate oversight and regulation. They highlight the potential risks to patients and call for improved governance to ensure the ...

Abstract: The future of agriculture depends on our ability to make informed, data-driven decisions about land use and crop management. This paper presents a comprehensive, humancentered approach to ...

A machine learning web application that predicts resale flat prices in Singapore using historical HDB data. Built with Scikit-learn and Streamlit, the app helps buyers and sellers estimate market ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. In this research work authors have experimentally validated a blend of Machine ...