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"Going Deeper with Recursive Convolutional Layers" by Johan Chagnon, Markus Hagenbuchner et al.

The development of Convolutional Neural Networks (CNNs) trends towards models with an ever growing number of Convolutional Layers (CLs) and increases the number of trainable parameters significantly. Such models are sensitive to these structural parameters, which implies that large models have to be carefully tuned using hyperparameter optimisation, a process that can be very time consuming. In this paper, we study the usage of Recursive Convolutional Layers (RCLs), a module relying on an algebraic feedback loop wrapped around a CL, which can replace any CL in CNNs. Using three publicly available datasets, CIFAR10, CIFAR100 and SVHN, and a simple model comprised of 4 RCLs, we compare its performances with those obtained by its feedforward counterpart, and exhibit some core properties and use-cases of RCLs. In particular, we show that RCLs can lead to models of better performances, and that reducing the number of modules from four to one lead to a decrease in accuracy of 3.5% on average ....

Convolutional Neural Networks Cnns , Convolutional Neural Networks , Convolutional Layers , Recursive Convolutional Layers , Convolutional Neural Network , Dynamic Depth , Image Classification , Recursive Neural Network ,

"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 ,

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Group Qr Code , Convolutional Neural Network , More Option , App Store , Whatsapp Web , Save Image ,

Study employs deep learning to explain extreme events

Study employs deep learning to explain extreme events
phys.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from phys.org Daily Mail and Mail on Sunday newspapers.

Eric Jagodinski , Stella Batalama , Northrop Grumman , Convolutional Neural Network , Florida Atlantic University College Of Engineering , Department Of Ocean , College Of Engineering , Computer Science , Fort Lauderdale , Florida Atlantic University , Physical Review Fluids , Siddhartha Verma ,

Engineering study employs deep learning to explain extreme events

Engineering study employs deep learning to explain extreme events
sciencedaily.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from sciencedaily.com Daily Mail and Mail on Sunday newspapers.

Stella Batalama , Eric Jagodinski , Xingquan Hill Zhu , Department Of Electrical Engineering , Department Of Ocean , National Science Foundation , Convolutional Neural Network , College Of Engineering , Florida Atlantic University College Of Engineering , Northrop Grumman , Fort Lauderdale , Florida Atlantic University , Computer Science , Physical Review Fluids , Siddhartha Verma , Electrical Engineering ,