Detection of fasting blood sugar using a microwave sensor and convolutional neural network by Mohammad Amir Sattari, Mohsen Hayati

“…To interpret the complex, non-linear features of the sensor response, a convolutional neural network (CNN) was developed and trained using the entire dataset. …”
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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

“…While deep learning has advanced plant disease detection, most existing research targets leaf diseases, with limited focus on berry-specific infections like CBD. …”
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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

“…Most of the study carried out have considered publicly available datasets to train the classification networks. …”
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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

“…Breast cancer is cancer that forms in the cells of the breasts and is a severe health issue that affects many people around the world, especially since it is the most deadly cancer in women. By finding it early and using new treatments, patients can overcome this challenge and get back to a healthier life. …”
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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

“…To increase detection efficiency, a variety of automatic fabric defect detections have been developed. However, most of these methods rely on complex model with heavy parameters, leading to high computational costs that hinder their adaptation to real-time detection environments. …”
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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

“…Face recognition is considered as one of the most reliable solutions. Usually, for face recognition, scale-invariant feature transforms (SIFT) and speeded up robust features (SURF) have been used by the research community. …”
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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

“…Nevertheless, the deployment of most existing DL models on resource-constrained devices remains challenging because of their intricate architectures and high computational demands. …”
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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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