Recent advances in the field of medical imaging and computational neuroscience have transformed the landscape of brain pathology detection. The application ...

The integration of AI and Machine Learning into injury prediction is transforming how researchers, clinicians, and sports ...

Explore how AI in food safety improves risk detection, predictive analytics, traceability, cold-chain monitoring, and recall ...

AI plays a role in improving defect capture rate and distinguishing between yield-killing and nuisance defects. New developments in wafer edge inspection are proving essential to bonded wafer yields.

Machine learning (ML) is reshaping pipeline integrity management (PIM) from physics-based to data-driven paradigms. This ...

Researchers from South Korean organisations Pohang University of Science and Technology (POSTECH), Korea Institute of Materials Science (KIMS), and the Hyundai Motor Group, and the Japanese University ...

There is no doubt that the semiconductor industry is in an era of rapid and profound transformation, driven by an increasing demand for smaller, faster, and more powerful chips. As the speed of ...

Abstract: To address the challenge of traditional visual image object detection models' difficulty in effectively learning semantic information in substations, this paper proposes a substation ...

Effectively detecting subtle surface defects in strip steel is vital for industrial quality assurance; however, most existing approaches fail to strike an optimal balance between accuracy and ...

We developed and released an image dataset for surface defect detection on waterborne painted wood products. The dataset comprises 13400 high-resolution images capturing four defect types: scratches, ...

US researchers say a self-supervised machine-learning tool can identify long-term physical defects in solar assets weeks or years before conventional inspections, potentially reducing operations and ...

Researchers from Stony Brook University, in collaboration with Ecosuite and Ecogy Energy, have developed a self-supervised machine learning algorithm designed to identify physical anomalies in solar ...