Analytics Insight

Deep Learning for Computer Vision, Stanford University

Stanford University’s Deep Learning for Computer Vision (XCS231N) is a 100% online, instructor-led course offered by the ...
The New York Times

These Tools Say They Can Spot A.I. Fakes. Do They Really Work?

Artificial intelligence detectors are increasingly used to check the veracity of content online. We ran more than 1,000 tests and found several strengths and plenty of weaknesses. By Stuart A.
GitHub

Malware Detection using Deep Learning

This project uses deep learning techniques to detect malware by analyzing file characteristics, byte sequences, and behavioral patterns. It employs Convolutional Neural Networks (CNNs) for image-based ...
Frontiers

A vision-based drowsiness detection system for railway operators using lightweight convolutional neural networks

This research addresses the challenge of monitoring railway driver drowsiness using a real-time, vision-based system powered by convolutional neural networks, specifically the YOLOv8 architecture ...
IEEE

Threshold-Free Event Detection for Nonintrusive Load Monitoring Using Motif Difference Field Images and Convolutional Neural Networks

Abstract: Event detection is a critical process in non-intrusive load monitoring (NILM). Accurate detection enhances subsequent load identification and facilitates a prompt understanding of the system ...
IEEE

Using Convolutional Neural Networks to Detect In-Field PV Module Glass Cracks

Abstract: Double-glass photovoltaic modules are being increasingly deployed, and there is a growing concern about glass cracking in these modules. To confirm this phenomenon, to quantify the rates of ...