Deep convolution neural network model in problem of crack segmentation on asphalt images by B. V. Sobol, A. N. Soloviev, P. V. Vasiliev, L. A. Podkolzina

“…The model is implemented as an optimized version of the most popular, at this time, fully convolution neural networks (FCNN). …”
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Analytical Comparison of Two Emotion Classification Models Based on Convolutional Neural Networks by Huiping Jiang, Demeng Wu, Rui Jiao, Zongnan Wang

“…As EEG signal technology has matured over the years, it has been applied in various methods to EEG emotion recognition, most significantly including the use of convolutional neural network (CNN). …”
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Congestion Management Using an Optimized Deep Convolution Neural Network in Deregulated Environment by Dhanadeepika B., Vanithasri M., Chakravarthi M.

“…This analysis incorporates restrictions such as line loads, bus voltage influence, generator, line limits, etc. The most important results for the test system indicating convergence profile, congestion cost, and change in real-power and voltage magnitude are obtained by the simulation in MATLAB, and on the basis of the obtained simulation outcomes, it is evident that the proposed Improved Lion Algorithm optimized Deep Convolution Neural Network displays phenomenal computation performance in minimizing congestion losses at minimum congestion costs. …”
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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 vibrating of concrete is one of the most important procedures that directly determines the quality of construction projects. …”
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Short term prediction of photovoltaic power with time embedding temporal convolutional networks by Jingxin Wang, Guohan Li, Jin Gu, Zhengyi Xu, Xinrong Chen, Jianming Wei

“…To address these limitations, this study introduces the Time-Embedding Temporal Convolutional Network (ETCN), providing an innovative solution. …”
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Pose Invariant Palm Vein Identification System using Convolutional Neural Network by Baghdad Science Journal

“…Palm vein recognition is a one of the most efficient biometric technologies, each individual can be identified through its veins unique characteristics, palm vein acquisition techniques is either contact based or contactless based, as the individual's hand contact or not the peg of the palm imaging device, the needs a contactless palm vein system in modern applications rise tow problems, the pose variations (rotation, scaling and translation transformations) since the imaging device cannot aligned correctly with the surface of the palm, and a delay of matching process especially for large systems, trying to solve these problems. …”
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Balancing Complexity and Performance in Convolutional Neural Network Models for QUIC Traffic Classification by Giovanni Pettorru, Matteo Flumini, Marco Martalò

“…The upcoming deployment of sixth-generation (6G) wireless networks promises to significantly outperform 5G in terms of data rates, spectral efficiency, device densities, and, most importantly, latency and security. To cope with the increasingly complex network traffic, Network Traffic Classification (NTC) will be essential to ensure the high performance and security of a network, which is necessary for advanced applications. …”
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Edge Convolutional Networks for Style Change Detection in Arabic Multi-Authored Text by Abeer Saad Alsheddi, Mohamed El Bachir Menai

“…However, to the best of our knowledge, this task has not yet been investigated in Arabic text. Moreover, most existing SCD solutions represent boundaries surrounding segments by concatenating them. …”
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PERFORMANCE REFINEMENT OF CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES FOR SOLVING BIG DATA PROBLEMS by Saud Aljaloud

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A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder by Hoyeon Lee, Jaehong Yu

“…Then, a two-dimensional convolutional autoencoder is trained using only normal signals. …”
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Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder by Zahra Rastin, Gholamreza Ghodrati Amiri, Ehsan Darvishan

“…The major focus of SHM studies in recent years has been on developing vibration-based damage detection algorithms and using machine learning, especially deep learning-based approaches. Most of the deep learning-based methods proposed for damage detection in civil structures are based on supervised algorithms that require data from the healthy state and different damaged states of the structure in the training phase. …”
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EEG Functional Connection Analysis Based on the Weight Distribution of Convolutional Neural Network by Jinglong Wu, Peiwen Huang, Tiantian Liu, Go Ritsu, Duanduan Chen, Tianyi Yan

“…Functional connections are commonly used when exploring the human brain, especially in brain data analysis. However, most of the studies concentrate on traditional statistical analysis. …”
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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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Hyperbolic multi-channel hypergraph convolutional neural network based on multilayer hypergraph by Libing Bai, Feng Hu, Chunyang Tang, Zhangyu Mei, Chuang Liu

“…Then, a multi-channel convolution mechanism is introduced, which integrates hypergraph’s derivative graph, hypergraph’s line graph, and hyperbolic hypergraph convolution. …”
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Time–Frequency Transformations for Enhanced Biomedical Signal Classification with Convolutional Neural Networks by Georgios Lekkas, Eleni Vrochidou, George A. Papakostas

“…<b>Background:</b> Transforming one-dimensional (1D) biomedical signals into two-dimensional (2D) images enables the application of convolutional neural networks (CNNs) for classification tasks. …”
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Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems. by Ali Raza, Akhtar Ali, Sami Ullah, Yasir Nadeem Anjum, Basit Rehman

“…Skin cancer is among the most prevalent types of malignancy all over the global and is strongly associated with the patient's prognosis and the accuracy of the initial diagnosis. …”
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Few-shot traffic classification based on autoencoder and deep graph convolutional networks by Shengwei Xu, Jijie Han, Yilong Liu, Haoran Liu, Yijie Bai

“…Many researchers have proposed their own traffic classification methods based on GCN in recent years. However, most of the current approaches use two-layer GCN primarily due to the over-smoothing problem associated with deeper GCN. …”
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MulGCN: MultiGraph Convolutional Network for Aspect-Level Sentiment Analysis by Huyen Trang Phan, Van Du Nguyen, Ngoc Thanh Nguyen

“…Various approaches have been proposed to improve the performance of ALSA, most recently graph convolutional networks (GCNs). …”
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3D long time spatiotemporal convolution for complex transfer sequence prediction by Qiu Yunan, Cui Yingjie, Tang Haibo, Chen Zhongfeng, Lu Zhenyu, Xue Feng

“…However, two challenges still exist in the existing methods: 1) Most of the existing spatio-temporal prediction tasks focus on extracting temporal information using recurrent neural networks and using convolution networks to extract spatial information, but ignore the fact that the forgetting of historical information still exists as the input sequence length increases. 2) Spatio-temporal sequence data have complex non-smoothness in both temporal and spatial, such transient changes are difficult to be captured by existing models, while such changes are often particularly important for the detail reconstruction in the image prediction task. …”
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Learning to Make Document Context-Aware Recommendation with Joint Convolutional Matrix Factorization by Lei Guo, Yu Han, Haoran Jiang, Xinxin Yang, Xinhua Wang, Xiyu Liu

“…Recent works argued that the understanding of document context can be improved by the convolutional neural network (CNN) and proposed the convolutional matrix factorization (ConvMF) to leverage the contextual information of documents to enhance the rating prediction accuracy. …”
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