A machine learning-driven eNose detects ovarian cancer in blood plasma with 97 % sensitivity and specificity, offering a promising biomarker-agnostic approach.

MIT created "periodic table" for ML, organizing 20 algorithms by mathematical similarities which discovered of a new image-classification algorithm by 8%.

A research team funded by the National Institutes of Health (NIH) has developed a versatile machine learning model that could one day greatly expand what medical scans can tell us about disease.

By transforming everyday smartphone signals into high-resolution mobility data, researchers have reconstructed how residents of Cuenca travel across the city and what those patterns mean for energy ...

A study published in The Journal of Engineering Research (TJER) at Sultan Qaboos University presents an advanced intrusion detection system (IDS) designed to improve the accuracy and efficiency of ...

Physical artificial intelligence (PAI) is the application of AI and machine learning (ML) algorithms to enable autonomous ...

In retinal disease screenings, artificial intelligence can help deliver diagnoses earlier, giving physicians more time to preserve vision.

Abstract: Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction ...

Researchers used 16S rRNA sequencing and machine learning to identify gut microbiome patterns associated with insulin resistance severity in people with type 2 diabetes. XGBoost models showed that ...

Abstract: The UC Merced (UCM) land use dataset is a widely adopted benchmark for evaluating aerial image classification algorithms. This paper presents a comparative performance analysis of prominent ...

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...