In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...

Reverse Logistics, Artificial Intelligence, Circular Economy, Supply Chain Management, Sustainability, Machine Learning Share and Cite: Waditwar, P. (2026) De-Risking Returns: How AI Can Reinvent Big ...

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 ...

By applying machine learning techniques, engineers at MIT have created a new method for 3D printing metal alloys that produce ...

Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.

Helium is a valuable gas that plays crucial roles in a large variety of modern industry or scientific research, including low-temperature superconductivity, nuclear magnetic resonance, semiconductor ...

Please provide your email address to receive an email when new articles are posted on . Machine learning can use patient-reported outcomes to identify low disease activity in rheumatoid arthritis.

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...

Background and aims: Pattern identification (PI) provides a basis for understanding disease symptoms and signs. The aims of this study are to extract features for identifying conventional PI types ...

Abstract: Critical patterns are layout patterns that cannot be corrected by standard MB-OPC with enough accuracy. Since they are infrequent, lithography applications targeting critical patterns such ...

Neuroblastoma is the most common solid tumor in infants and accounts for nearly 15% of all pediatric cancer-related deaths. Despite decades of progress in surgery, chemotherapy, and stem cell ...