ADMM-TransNet: ADMM-Based Sparse-View CT Reconstruction Method Combining Convolution and Transformer Network by Sukai Wang, Xueqin Sun, Yu Li, Zhiqing Wei, Lina Guo, Yihong Li, Ping Chen, Xuan Li

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A cooperative intrusion detection system for internet of things using fuzzy logic and ensemble of convolutional neural networks by Xiongwei Qiu, Lianzhi Shi, Pengtong Fan

“…In this regard, our research presents a collaborative solution for intrusion detection in the IoT that relies on a combination of fuzzy logic techniques and Convolutional Neural Network (CNN) ensemble. Our goal is to solve the challenges in intrusion detection by using this combination and provide better performance in threat detection. …”
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A comparative study of convolutional neural networks and traditional feature extraction techniques for adulteration detection in ground beef by Leila Bahmani, Saied Minaei, Alireza Mahdavian, Ahmad Banakar, Mahmoud Soltani Firouz

“…Convolutional Neural Network (CNN) was also used to extract features and classify images. …”
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A Convolutional Neural Network-Weighted Cellular Automaton Model for the Fast Prediction of Urban Pluvial Flooding Processes by Jiarui Yang, Kai Liu, Ming Wang, Gang Zhao, Wei Wu, Qingrui Yue

“…This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton (CNN-WCA) to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results. …”
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Arrhythmia Disease Diagnosis Based on ECG Time–Frequency Domain Fusion and Convolutional Neural Network by Bocheng Wang, Guorong Chen, Lu Rong, Yuchuan Liu, Anning Yu, Xiaohui He, Tingting Wen, Yixuan Zhang, Biaobiao Hu

“…Electrocardiogram (ECG) signals are often used to diagnose cardiac status. However, most of the existing ECG diagnostic methods only use the time-domain information, resulting in some obviously lesion information in frequency-domain of ECG signals are not being fully utilized. …”
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Advanced Detection of Violence From Video: Performance Evaluation of Transformer and State of the Art of Convolution of Neural Network Transformer by Abdulrahman Alshalawi, Wadood Abdul, Ghulam Muhammad

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Detection of degraded forests in Guinea, West Africa, using convolutional neural networks and Sentinel-2 time series by An Vo Quang, An Vo Quang, Nicolas Delbart, Gabriel Jaffrain, Camille Pinet

“…The results show that the CNN U-Net model is the most adequate method, with an 94% agreement with the photo-interpreted map in the Ziama massif for the year 2021 unused for the training. …”
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Sway frequencies may predict postural instability in Parkinson’s disease: a novel convolutional neural network approach by David Engel, R. Stefan Greulich, Alberto Parola, Kaleb Vinehout, Justus Student, Josefine Waldthaler, Lars Timmermann, Frank Bremmer

“…Our aim was to use a convolutional neural network (CNN) to differentiate people with early to mid-stage PD from healthy age-matched individuals based on spectrogram images obtained from their body sway. …”
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A Seq-to-Seq Temporal Convolutional Network for Volleyball Jump Monitoring Using a Waist-Mounted IMU by Meng Shang, Camilla de Bleecker, Jos Vanrenterghem, Roel de Ridder, Sabine Verschueren, Carolina Varon, Walter de Raedt, Bart Vanrumste

“…A Multi-Layer Temporal Convolutional Network (MS-TCN) was applied for sequence-to-sequence (seq-to-seq) classification without using the sliding window technique. …”
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Penerapan Deep Convolutional Generative Adversarial Network Untuk Menciptakan Data Sintesis Perilaku Pengemudi Dalam Berkendara by Michael Stephen Lui, Fitra Abdurrachman Bachtiar, Novanto Yudistira

“…One way to increase deep learning method performance is by using additional synthesis data made by generative model. Deep Convolutional Generative Adversarial Network (DCGAN) is a generative model that uses convolution layer. …”
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An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features by Mustain Billah, Sajjad Waheed, Mohammad Motiur Rahman

“…Video endoscopy is the most used diagnostic modality for gastrointestinal polyps. …”
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Automatic detection of orthodontically induced external root resorption based on deep convolutional neural networks using CBCT images by Shuxi Xu, Houli Peng, Lanxin Yang, Wenjie Zhong, Xiang Gao

“…Abstract Orthodontically-induced external root resorption (OIERR) is among the most common risks in orthodontic treatment. Traditional OIERR diagnosis is limited by subjective judgement as well as cumbersome manual measurement. …”
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VNet: An End-to-End Fully Convolutional Neural Network for Road Extraction From High-Resolution Remote Sensing Data by Arnick Abdollahi, Biswajeet Pradhan, Abdullah Alamri

“…One of the most important tasks in the advanced transportation systems is road extraction. …”
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Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions by Jayaraman Kumarappan, Elakkiya Rajasekar, Subramaniyaswamy Vairavasundaram, Ketan Kotecha, Ambarish Kulkarni

“…To address these challenges, this paper proposes a new method called Siagra-ConSA-HSOA (Siamese Graph Convolutional Split-Attention Network with NLP-based Social Sentiment Data). …”
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A Novel Hybrid Model for Brain Ischemic Stroke Detection Using Feature Fusion and Convolutional Block Attention Module by Ahmad Abumihsan, Amani Yousef Owda, Majdi Owda, Mobarak Abumohsen, Lampros Stergioulas, Mohammad Ahmad Abu Amer

“…In addition to the CBAM, which refines the feature maps by selectively focusing on the most important channels and spatial regions in brain CT images. …”
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Solar Cycle Prediction Using a Temporal Convolutional Network Deep-learning Model with a One-step Pattern by Cui Zhao, Kun Liu, Shangbin Yang, Jinchao Xia, Jingxia Chen, Jie Ren, Shiyuan Liu, Fangyuan He

“…Although many deep-learning models are currently used for solar cycle prediction, most of them are based on a multistep pattern. In this paper a solar cycle prediction method based on a one-step pattern is proposed with the temporal convolutional network neural network model, in which historical data are input and only one value is predicted at a time. …”
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Learning EEG Representations With Weighted Convolutional Siamese Network: A Large Multi-Session Post-Stroke Rehabilitation Study by Shuailei Zhang, Kai Keng Ang, Dezhi Zheng, Qianxin Hui, Xinlei Chen, Yang Li, Ning Tang, Effie Chew, Rosary Yuting Lim, Cuntai Guan

“…To circumvent this shortage, we propose a deep metric learning based method, Weighted Convolutional Siamese Network (WCSN) to learn representations from electroencephalogram (EEG) signal. …”
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Efficient Convolutional Neural Network Model for the Taxonomy and Sex Identification of Three Phlebotomine Sandfly Species (Diptera, Psychodidae, and Phlebotominae) by Mohammad Fraiwan

“…Sandflies, small insects primarily from the Psychodidae family, are commonly found in sandy, tropical, and subtropical regions. Most active during dawn and dusk, female sandflies feed on blood to facilitate egg production. …”
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PCN: a deep learning approach to jet tagging utilizing novel graph construction methods and Chebyshev graph convolutions by Yash Semlani, Mihir Relan, Krithik Ramesh

“…In this study, we propose a graph-based representation of a jet that encodes the most information possible. To learn best from this representation, we design Particle Chebyshev Network (PCN), a graph neural network (GNN) using Chebyshev graph convolutions (ChebConv). …”
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EEG Emotion Recognition Using AttGraph: A Multi-Dimensional Attention-Based Dynamic Graph Convolutional Network by Shuai Zhang, Chengxi Chu, Xin Zhang, Xiu Zhang

“…Results: Through the dynamic weighting of EEG features via a multi-dimensional attention convolution layer, the AttGraph method is able to precisely detect emotional changes and automatically choose the most discriminative features for emotion recognition tasks. …”
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