Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network by Yilin Hu, Junling Ran, Rui Qiao, Jiayang Xu, Congming Tan, Liangliang Hu, Yin Tian

“…We employed a graph convolutional neural network model to identify individuals with ADHD. …”
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An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security by J. Maruthupandi, S. Sivakumar, B. Lakshmi Dhevi, S. Prasanna, R. Karpaga Priya, Shitharth Selvarajan

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Bearing Fault Detection and Classification Based on Temporal Convolutions and LSTM Network in Induction Machine by Mohammad Hoseintabar Marzebali, Saeed Hasani Borzadaran, Hoda Mashayekhi, Valiollah Mashayekhi

“…One of the critical components in most electromechanical systems are the bearing system. …”
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A Hybrid Compact Convolutional Transformer with Bilateral Filtering for Coffee Berry Disease Classification by Biniyam Mulugeta Abuhayi, Andras Hajdu

“…This study proposes a lightweight and accurate solution using a Compact Convolutional Transformer (CCT) for classifying healthy and CBD-affected coffee berries. …”
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Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling by Younghyun Koo, Maryam Rahnemoonfar

“…Although several deep learning emulators using graphic processing units (GPUs) have been proposed to accelerate ice sheet modeling, most of them rely on convolutional neural networks (CNNs) designed for regular grids. …”
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Local kernel renormalization as a mechanism for feature learning in overparametrized convolutional neural networks by R. Aiudi, R. Pacelli, P. Baglioni, A. Vezzani, R. Burioni, P. Rotondo

“…Abstract Empirical evidence shows that fully-connected neural networks in the infinite-width limit (lazy training) eventually outperform their finite-width counterparts in most computer vision tasks; on the other hand, modern architectures with convolutional layers often achieve optimal performances in the finite-width regime. …”
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Analysis of MSTAR Object Classification Features Extracted by a Deep Convolutional Neural Network by I. F. Kupryashkin

“…Introduction. Deep convolutional neural networks are effective tools for classifying objects on radar images; however, their decision-making process is not transparent. …”
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Comparative exploration of deep convolutional neural networks using real-time endoscopy images by Subhashree Mohapatra, Pukhraj Singh Jeji, Girish Kumar Pati, Manohar Mishra, Tripti Swarnkar

“…Until now various deep convolutional neural networks are designed and trained for the purpose of classifying different medical conditions related to the domain of gastroenterology. …”
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Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks by Huong Hoang Luong, Hai Thanh Nguyen, Nguyen Thai-Nghe

“…This study proposed a procedure to fine-tune the Convolutional Neural Networks (CNN) model with data preprocessing and augmentation in classifying mammogram images called the Hybrid Mammogram Classification and Detection Pipeline (HMCaD). …”
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Dash: Accelerating Distributed Private Convolutional Neural Network Inference with Arithmetic Garbled Circuits by Jonas Sander, Sebastian Berndt, Ida Bruhns, Thomas Eisenbarth

“…In this work, we investigate how to protect distributed machine learning systems, focusing on deep convolutional neural networks. The most common and best-performing mixed MPC approaches are based on HE, secret sharing, and garbled circuits. …”
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A lightweight fabric defect detection with parallel dilated convolution and dual attention mechanism by Zheqing Zhang, Kezhong Lu, Gaoming Yang

“…In order to capture multi-scale contextual information, we designed a parallel dilated convolution downsampling (PDCD) block to replace the conventional downsampling block in the backbone. …”
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An explainable AI-based blood cell classification using optimized convolutional neural network by Oahidul Islam, Md Assaduzzaman, Md Zahid Hasan

“…This study presents an enhanced convolutional neural network (CNN) for detecting blood cells with the help of various image pre-processing techniques. …”
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Detecting malicious code variants using convolutional neural network (CNN) with transfer learning by Nazish Younas, Shazia Riaz, Saqib Ali, Rafiullah Khan, Farman Ali, Daehan Kwak

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Robust remaining useful life prediction of lithium-ion battery with convolutional denoising autoencoder by Asri Rizki Yuliani, Hilman Ferdinandus Pardede, Ade Ramdan, Vicky Zilvan, Raden Sandra Yuwana, M Faizal Amri, R. Budiarianto Suryo Kusumo, Subrata Pramanik

“…DAE with convolutional layers has the best average performance with MSE of 0.61 and is the most consistent model.…”
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A hybrid parallel convolutional spiking neural network for enhanced skin cancer detection by K. Anup Kumar, C. Vanmathi

“…Abstract The most widespread kind of cancer, affecting millions of lives is skin cancer. …”
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Deep Unified Model For Face Recognition Based on Convolution Neural Network and Edge Computing by Muhammad Zeeshan Khan, Saad Harous, Saleet Ul Hassan, Muhammad Usman Ghani Khan, Razi Iqbal, Shahid Mumtaz

“…This paper proposes an algorithm for face detection and recognition based on convolution neural networks (CNN), which outperform the traditional techniques. …”
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PERFORMANCE COMPARISON OF GRADIENT-BASED CONVOLUTIONAL NEURAL NETWORK OPTIMIZERS FOR FACIAL EXPRESSION RECOGNITION by Sri Nurdiati, Mohamad Khoirun Najib, Fahren Bukhari, Refi Revina, Fitra Nuvus Salsabila

“…A convolutional neural network (CNN) is one of the machine learning models that achieve excellent success in recognizing human facial expressions. …”
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EDB-Net: Efficient Dual-Branch Convolutional Transformer Network for Hyperspectral Image Classification by Hufeng Guo, Wenyi Liu

“…To tackle this challenge, we propose a lightweight dual-branch convolutional transformer network with efficient attention-aware mechanism (EDB-Net), which aims to balance model complexity, classification accuracy, and inference speed. …”
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Lightweight Multiscale Spatio-Temporal Graph Convolutional Network for Skeleton-Based Action Recognition by Zhiyun Zheng, Qilong Yuan, Huaizhu Zhang, Yizhou Wang, Junfeng Wang

“…Secondly, the dilated convolution is incorporated into the temporal convolution to construct Lightweight Multiscale Temporal Convolutional Network (LMTCN), which allows to obtain a wider receptive field while keeping the size of the convolution kernel unchanged. …”
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Diagnosis of trigeminal neuralgia based on plain skull radiography using convolutional neural network by Jung Ho Han, So Young Ji, Myeongju Kim, Ji Eyon Kwon, Jin Byeong Park, Ho Kang, Kihwan Hwang, Chae-Yong Kim, Tackeun Kim, Han-Gil Jeong, Young Hwan Ahn, Hyun-Tai Chung

“…Abstract This study aimed to determine whether trigeminal neuralgia can be diagnosed using convolutional neural networks (CNNs) based on plain X-ray skull images. …”
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