Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network by Thanh Bui-Tien, Dung Bui-Ngoc, Hieu Nguyen-Tran, Lan Nguyen-Ngoc, Hoa Tran-Ngoc, Hung Tran-Viet

“…In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. …”
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Spatio-temporal Graph Convolutional Neural Network for traffic signal prediction in large-scale urban networks by Shimon Komarovsky, Jack Haddad

“…In this paper, two models that can handle spatio-temporal graphical data are developed based on Graph Convolutional Neural Network. The developed models can be utilized either for traffic prediction tasks or for decision-making, e.g. of green times in intersections, given fixed cycle time steps. …”
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Number Recognition Through Color Distortion Using Convolutional Neural Networks by Christopher Henshaw, Jacob Dennis, Jonathan Nadzam, Alan J. Michaels

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Crushed Stone Grain Shapes Classification Using Convolutional Neural Networks by Alexey N. Beskopylny, Evgenii M. Shcherban’, Sergey A. Stel’makh, Irina Razveeva, Alexander L. Mailyan, Diana Elshaeva, Andrei Chernil’nik, Nadezhda I. Nikora, Gleb Onore

“…This study implements methods using convolutional neural networks, which solve an important problem in the construction industry—to classify crushed stone grains by their shape. …”
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Transformer network enhanced by dual convolutional neural network and cross-attention for wheelset bearing fault diagnosis by Jing Zhao, Jing Zhao, Junfeng Li, Ziteng Li, Zengqiang Ma, Zengqiang Ma

“…To address these challenges, this study proposes a Transformer network model based on dual convolutional neural networks and cross-attention enhancement (Trans-DCC) for wheelset bearing fault diagnosis. …”
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An abnormal traffic detection method for chain information management system network based on convolutional neural network by Chao Liu, Chunxiang Liu, Changrong Liu

“…However, the problem of abnormal network traffic becomes increasingly prominent currently. …”
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THE CURRENT STATE OF ARTIFICIAL INTELLIGENCE IN RADIOLOGY – A REVIEW OF THE BASIC CONCEPTS, APPLICATIONS, AND CHALLENGES by Mariana Yordanova

“…Deep learning, especially deep convolutional neural networks (CNNs), has become a prominent approach, mimicking brain functions to process images through multiple layers. …”
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Quantitative Convolutional Neural Network Based Multi-Phase XRD Pattern Analysis by Höfer Hawo H., Orth André, Schweidler Simon, Breitung Ben, Aghassi-Hagmann Jasmin, Reischl Markus

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Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals by Bing Chen, Tengwei Yu

Subjects: “…complex-valued convolutional neural network…”
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A Method for Fault Localization in Distribution Networks with High Proportions of Distributed Generation Based on Graph Convolutional Networks by Xiping Ma, Wenxi Zhen, Haodong Ren, Guangru Zhang, Kai Zhang, Haiying Dong

“…To address the issues arising from the integration of a high proportion of distributed generation (DG) into the distribution network, which has led to the transition from traditional single-source to multi-source distribution systems, resulting in increased complexity of the distribution network topology and difficulties in fault localization, this paper proposes a fault localization method based on graph convolutional networks (GCNs) for distribution networks with a high proportion of distributed generation. …”
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Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks by Prabu Selvam, P. Karthikeyan, S. Manochitra, A. V. L. N. Sujith, T. Ganesan, Rajaram Ayyasamy, Mohammed Shuaib, Shadab Alam, A. Rajendran

“…For multi-class intrusion detection in IoT networks, this study proposed an novel hybrid convolutional recurrent neural network (CRNN) model based on federated learning. …”
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ECVNet: A Fusion Network of Efficient Convolutional Neural Networks and Visual Transformers for Tomato Leaf Disease Identification by Fendong Zou, Jing Hua, Yuanhao Zhu, Jize Deng, Ruimin He

“…Existing models for tomato leaf disease recognition can primarily be categorized into Convolutional Neural Networks (CNNs) and Visual Transformers (VTs). …”
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Research on the Timing of Replacing Worn Milling Cutters by Using Wear Transition Percentage Constructed Based on Spindle Current Clutter Signals by Zhihao Liu, Min Wang, Zhishan Wang, Tao Zan, Xiangsheng Gao, Peng Gao

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Spatio-temporal transformer and graph convolutional networks based traffic flow prediction by Jin Zhang, Yimin Yang, Xiaoheng Wu, Sen Li

“…Despite substantial progress in this field, several challenges still remain. Firstly, most current methods rely on Graph Convolutional Networks (GCNs) to extract spatial correlations, typically using predefined adjacency matrices. …”
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Dual-Path Beat Tracking: Combining Temporal Convolutional Networks and Transformers in Parallel by Nikhil Thapa, Joonwhoan Lee

“…Transformers excel at capturing global, long-range dependencies in sequences, which is valuable for tracking rhythmic patterns over time. Temporal Convolutional Networks (TCNs), with their dilated convolutions, are effective at processing local, temporal patterns with reduced complexity. …”
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Dynamic graph convolutional networks with Temporal representation learning for traffic flow prediction by Aihua Zhang

“…To tackle this challenge, we introduce a novel framework termed Dynamic Graph Convolutional Networks with Temporal Representation Learning for Traffic Flow Prediction (DGCN-TRL). …”
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Intelligent Analysis of Hydraulic Concrete Vibration Time Based on Convolutional Neural Network by Hao Liu, Chengzhao Liu, Jiake Fu, Chenzhe Ma, Ye Zhang, Yumeng Lei

“…The system took the convolutional neural network as the basic framework, and divided the concrete vibration process into three different states: vibrating, not vibrating, and no vibration tube, realized the concrete vibration time through the analysis of concrete vibration video data. …”
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Automatic plant disease detection using computationally efficient convolutional neural network by Muhammad Rizwan, Samina Bibi, Sana Ul Haq, Muhammad Asif, Tariqullah Jan, Mohammad Haseeb Zafar

“…Convolutional neural network (CNN)‐based prominent models, such as MobileNet, ResNet50, Inception, and Xception, are preferred for automatic plant disease detection due to their high performance, but they demand substantial computational resources, limiting their use to a class of large‐scale farmers. …”
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Deep Learning Models for Image Classification Advances in Convolutional Neural Network Architectures by Kumar Pathak Prakash, M Srivani, M Diwakaran, R Purushothaman, Sheeba Adlin, R Ahila

“…Deep learning has improved image classification tasks dramatically, where Convolutional Neural Networks (CNNs) have prevailed as the most successful architecture. …”
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An Efficient 3D Model Retrieval Method Based on Convolutional Neural Network by Bo Ding, Lei Tang, Yong-jun He

“…In the index building stage, 3D models in library are projected to generate a large number of views, and then representative views are selected and input into a well-learned convolutional neural network (CNN) to extract features. …”
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