Forget waiting a week for mold test results. New electronic nose technology detects toxic indoor mold species in just 30 ...

Security teams face an impossible choice: set thresholds too sensitive and drown in false positives, or set them too loose and miss real attacks. Traditional monitoring systems force this trade-off ...

Explainable AI plays a central role in validating model behavior. Using established explainability techniques, the study examines which financial variables drive fraud predictions. The results show a ...

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 ...

Overview: In 2025, Java is expected to be a solid AI and machine-learning language.Best Java libraries for AI in 2025 can ease building neural networks, predict ...

As the diagnostic landscape evolves, several patterns are emerging across both clinical research and market innovation.

The progression of glaucoma was accurately predicted by machine learning models based on structural, functional and vascular ...

When NASA scientists opened the sample return canister from the OSIRIS-REx asteroid sample mission in late 2023, they found something astonishing. Dust and rock collected from the asteroid Bennu ...

The cybersecurity landscape in 2026 presents unprecedented challenges for organizations across all industries. With ...

As digital ecosystems expand across industries, so does the threat surface they expose. For Dr. Karthik Kambhampati, a ...

Fraud detection is defined by a structural imbalance that has long challenged data-driven systems. Fraudulent transactions typically account for a fraction of a percent of total transaction volume, ...

Crucially, detection and response must be unified across identity and data layers. An alert about unusual data access is meaningless if it is not correlated with identity risk signals. Autonomous ...