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Advancing AI Education With AMD Technology In Colorado

Advancing AI Education With AMD Technology In Colorado
menafn.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from menafn.com Daily Mail and Mail on Sunday newspapers.

United States , Team Corn Corp , Vrain Valley Schools Innovation Center , Vrain Valley Schools , Machine Learning , Object Detection , Winning Project , Team Corn , Perfect Picture , Face Detection , Computer Vision ,

"An improved deep learning-based optimal object detection system from i" by Satya Prakash Yadav, Muskan Jindal et al.

Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional Neural Networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss. ....

Fast Convolutional Neural Networks Cnns , Convolutional Neural Networks , Fast Convolutional Neural Networks , Only Look Once , Single Stage Detector , Faster Region Based Convolutional Neural Networks , Single Shot Detector , Chess Piece Identification , Aster Region Based Convolutional Neural Networksr Cnn , Object Detection , Ingle Stage Detector Ssd , Ou Only Look Once Yolo ,

"Health Monitoring of Old Buildings in Bangladesh: Detection of Cracks " by Rafiul Bari Angan, Md Safaiat Hossain et al.

Numerous buildings in Bangladesh were constructed without following standard building codes. As a result, those are vulnerable to increased earthquake frequency and variable loads. To address this issue, old buildings need to be monitored frequently by non-destructive testing (NDT) to avoid any failures. The identification of cracks and dampness is one of the most important parts of this test. Generally, this detection part is costly and time-consuming to conduct it manually. To resolve this, the current study has been performed for intelligent structural damage identification based on deep learning techniques. A state-of-the-art Convolutional Neural Network (CNN)-based object detection model YOLOv4-tiny has been used to detect cracks and dampness and repair cost analysis of damages. The study outcome suggests that using our proposed deep model, it is possible to detect building cracks with a mean Average Precision (%mAP) of 72.46%. In addition to traditional structural health monitori ....

Convolutional Neural Network , Average Precision , Crack And Dampness , Deep Learning , Deep Neural Network Dnn , Object Detection , Epair Cost Estimation , Olov4 Tiny ,

CSRWire - Advancing AI Education With AMD Technology in Colorado

CSRWire - Advancing AI Education With AMD Technology in Colorado
csrwire.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from csrwire.com Daily Mail and Mail on Sunday newspapers.

United States , Team Corn Corp , Vrain Valley Schools Innovation Center , Vrain Valley Schools , Machine Learning , Object Detection , Winning Project , Team Corn , Perfect Picture , Face Detection , Computer Vision ,

Ask HN: Who wants to be hired? (October 2023)

Ask HN: Who wants to be hired? (October 2023)
ycombinator.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from ycombinator.com Daily Mail and Mail on Sunday newspapers.

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