Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

Cities are increasingly becoming the epicenter of climate-related risks, with research showing that impervious surfaces (e.g.

Schizophrenia is a severe and often highly debilitating psychiatric disorder characterized by distorted emotions, thinking ...

A new low-power sensor node framework combines sensing and machine learning, with the potential to enhance real-time ...

A new algorithm analyzes electrical activity in the brain to forecast whether standard depression drugs will work, ...

A signal-processing–based framework converts DNA sequences into numerical signals to identify protein-coding regions. By integrating spectral analysis and SVM classification, the approach improves ...

In a study titled Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples, published in the ...

Market growth is driven by industrial automation, predictive maintenance demand, AI/ML analytics adoption, IoT integration, and the need to reduce downtime and operational costs.Austin, Jan. 27, 2026 ...

Medical researchers at Mass General Brigham say the self-supervised foundational model can identify inherent features from ...

AI algorithms analyse complex medical images with speed and precision, supporting early disease detection.Radiology and ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...