A new synthesis of seismic research shows that artificial intelligence, when combined with physical principles, is rapidly ...

Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...

Adnan and colleagues evaluated machine learning models’ ability to screen for Parkinson’s disease using self-recorded smile videos. 2. The models achieved high sensitivity and specificity among ...

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 ...

AI models using ultrasound imaging demonstrate superior diagnostic performance for ovarian cancer compared to sonographers, with higher sensitivity and specificity. The study emphasizes the need for ...

In the vast expanse of our universe, one question has captivated humanity for generations: are we alone? This profound inquiry has driven scientific exploration for decades, but recent technological ...

Sensitivity analysis helps predict outcomes by varying key variables in financial models. It simplifies complex models, aids in understanding variable effects, and reduces uncertainty. This analysis ...

In today’s digital background, sentiment analysis has become an essential factor of Natural Language Processing (NLP), offering valuable insights from vast online data sources. This paper presents a ...

In this paper, we introduce QProteoML, a new quantum machine learning (QML) framework for predicting drug sensitivity in Multiple Myeloma (MM) using high-dimensional proteomic data. MM, an extremely ...

In this study, the authors developed a novel radiotherapy sensitivity score (NPC-RSS) for nasopharyngeal carcinoma patients using machine learning algorithms. They identified 18 key genes associated ...