Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network by Aythem Khairi Kareem, Mohammed M. AL-Ani, Ahmed Adil Nafea

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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

“…In this work, we present a theoretical framework that provides a rationale for these differences in one-hidden-layer networks; we derive an effective action in the so-called proportional limit for an architecture with one convolutional hidden layer and compare it with the result available for fully-connected networks. …”
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Training Sample Formation for Convolution Neural Networks to Person Re-Identification from Video by S. A. Ihnatsyeva, R. P. Bohush

“…Another PolReID1077 advantage is the video data use obtained from external and internal surveillance in a large number of different filming locations. Therefore, the people images in the created set are characterized by the variability of the background, brightness and color characteristics. …”
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Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network by Fanar E. K. Al-Khuzaie, Oguz Bayat, Adil D. Duru

“…There are many kinds of brain abnormalities that cause changes in different parts of the brain. Alzheimer’s disease is a chronic condition that degenerates the cells of the brain leading to memory asthenia. …”
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CLASS IMBALANCE PROBLEM IN ANTI-FRAUD PROBLEM: METRICS, SAMPLING AND CONVOLUTIONAL NEURAL NETWORKS by Ruslan Ch. Bobonazarov

“…Researchers, using publicly available datasets, apply different approaches to model evaluation, some of which are not effective in conditions of severe class imbalance. …”
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ECT Image Reconstruction Algorithm Based on Multiscale Dual-Channel Convolutional Neural Network by Lili Wang, Xiao Liu, Deyun Chen, Hailu Yang, Chengdong Wang

“…The middle layer of the network consists of two fully convolutional structures. Convolutional layers and jump connections are designed separately for different channels, which greatly improves the network’s ability to extract feature information and reduces the number of feature maps required for each layer. …”
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ExShall-CNN: An Explainable Shallow Convolutional Neural Network for Medical Image Segmentation by Vahid Khalkhali, Sayed Mehedi Azim, Iman Dehzangi

“…On the other hand, hand-crafted features extracted to represent different aspects of the input data and traditional machine learning models are generally more understandable. …”
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Evaluation of convolutional neural networks for the classification of falls from heterogeneous thermal vision sensors by Miguel Ángel López-Medina, Macarena Espinilla, Chris Nugent, Javier Medina Quero

“…In this work, we analyze the capabilities of non-invasive thermal vision sensors to detect falls using several architectures of convolutional neural networks. First, we integrate two thermal vision sensors with different capabilities: (1) low resolution with a wide viewing angle and (2) high resolution with a central viewing angle. …”
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Cross-Scale Spatial Refinement Graph Convolutional Network for Skeleton-Based Action Recognition by Chengyuan Ke, Sheng Liu, Zhenghao Ke, Yuan Feng, Shengyong Chen

“…Furthermore, the CFA module selectively integrates features from different spatial scales, enhancing feature distinctiveness while balancing global motion and local details. …”
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Recognition of common shortwave protocols and their subcarrier modulations based on multi-scale convolutional GRU. by Jiuxiao Cao, Rui Zhu, Zhen Wang, Jun Wang, Guohao Shi, Peng Chu

“…The model transforms temporal signals into two-dimensional representations, applies parallel convolutional branches with different receptive fields, and captures temporal dependencies through a bidirectional GRU. …”
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DDoS-MSCT: A DDoS Attack Detection Method Based on Multiscale Convolution and Transformer by Bangli Wang, Yuxuan Jiang, You Liao, Zhen Li

“…The LFEM employs convolutional kernels of different sizes, accompanied by dilated convolutions, with the aim of enhancing the receptive field and capturing multiscale features simultaneously. …”
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Edge Convolution Graph Neural Network Assisted Power Allocation for Wireless IoT Networks by Jihyung Kim, Yeji Cho, Junghyun Kim

“…We propose a novel power control technique called PC-ECGNN, which uses edge convolution to optimize power allocation in wireless IoT networks. …”
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Supervised Convolutional Encoder-Decoder With Gated Linear Units for Detecting Fetal R-Peaks by Yao Chen, Jian Wang, Jing Zhang, Junkun Zhang, Zhentao Qin, Xinran Liu

“…The versatility of our approach is validated through tests of different label encoding strategies, demonstrating its potential for other complex fetal ECG labeling tasks.…”
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Lane Boundary Detection for Intelligent Vehicles Using Deep Convolutional Neural Network Architecture by Xuewen Chen, Chenxi Xia, Xiaohai Chen

“…The BIFPN network is utilized for bidirectional feature fusion across different scales, significantly enhancing the accuracy of lane boundary detection. …”
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Research on rolling bearing compound fault diagnosis based on AMOMCKD and convolutional neural network by Runfang Hao, Yunpeng Bai, Kun Yang, Yongqiang Cheng, Shengjun Chang

“…Experimental results from two different datasets highlight that AMOMCKD-CNN outperforms other classical diagnostic methods under the same conditions, and it is more conducive to the detection of compound faults.…”
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Establishment and Test Effect of Artificial Intelligence Optimization Model Based on Convolutional Neural Network by Chunrong Zhou, Zhenghong Jiang

“…In addition, the authors optimized the convolutional layer, pooling layer, and loss function of AL-CNN in different parameters, which improved the stability of noise processing, respectively. …”
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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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Enhancing market trend prediction using convolutional neural networks on Japanese candlestick patterns by Edrees Ramadan Mersal, Kürşat Mustafa Karaoğlan, Hakan Kutucu

“…Subsequently, the model was trained and evaluated multiple times using different combinations of these subsets. This method allows for a more accurate assessment of the model’s predictive capabilities by examining its performance on unseen data.…”
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Hydrogen reaction rate modeling based on convolutional neural network for large eddy simulation by Quentin Malé, Corentin J. Lapeyre, Nicolas Noiray

“…First, five different lean premixed turbulent $ {\mathrm{H}}_2 $ -air flame Direct Numerical Simulations (DNSs) are computed each with a unique global equivalence ratio. …”
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Real-Time Transformer Detection of Underwater Objects Based on Lightweight Gated Convolutional Network by Yuhui LI, Huixia CUI, Yaomin LI, Senping JIA

“…To address the challenges in underwater object detection algorithms, including difficult image feature processing, redundant model architectures, and excessive parameter numbers, this paper proposed a real-time Transformer detection method for underwater objects based on a lightweight gated convolutional network. This method first constructed a convolutional gated linear unit based on the gating mechanism to dynamically modulate feature transmission. …”
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