Study on Lightweight Bridge Crack Detection Algorithm Based on YOLO11 by Xuwei Dong, Jiashuo Yuan, Jinpeng Dai

“…Furthermore, a lightweight detection head (LDH) is introduced to process feature information from different channels using efficient grouped convolutions. …”
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Research on Fault Diagnosis of High-Voltage Circuit Breakers Using Gramian-Angular-Field-Based Dual-Channel Convolutional Neural Network by Mingkun Yang, Liangliang Wei, Pengfeng Qiu, Guangfu Hu, Xingfu Liu, Xiaohui He, Zhaoyu Peng, Fangrong Zhou, Yun Zhang, Xiangyu Tan, Xuetong Zhao

“…This study proposes a Dual-Channel Convolutional Neural Network (DC-CNN) framework based on the Gramian Angular Field (GAF) transformation, which effectively captures both global and local information about faults. …”
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Classification Analysis of Blended Copper Concentrate Tablet Combustion Behavior by High-speed Imaging of Suspended Combustion Test and Convolutional Neural Network by Shungo NATSUI, Yuko GOTO, Jun-ichi TAKAHASHI, Hiroshi NOGAMI

“…A classification system based on a convolutional neural network was performed to recognize the different combustion patterns of Cu concentrate-SiO2 mixtures tablets under oxidation gas to estimate their combustion behavior and phase changes in flash smelting. …”
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Small Scale Multi-Object Segmentation in Mid-Infrared Image Using the Image Timing Features–Gaussian Mixture Model and Convolutional-UNet by Meng Lv, Haoting Liu, Mengmeng Wang, Dongyang Wang, Haiguang Li, Xiaofei Lu, Zhenhui Guo, Qing Li

“…The approach integrates the Image Timing Features–Gaussian Mixture Model (ITF-GMM) and Convolutional-UNet (Con-UNet) to improve the accuracy of target detection. …”
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An Approximated Solutions for nth Order Linear Delay Integro-Differential Equations of Convolution Type Using B-Spline Functions and Weddle Method by Baghdad Science Journal

“…The paper is devoted to solve nth order linear delay integro-differential equations of convolution type (DIDE's-CT) using collocation method with the aid of B-spline functions. …”
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Performance Analysis of Convolutional Neural Network in Pempek Food Image Classification with MobileNetV2 and GoogLeNet Architecture by Yudha Pratomo, Muhammad Taufik Roseno, Hadi Syaputra, Darius Antoni

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Classification of Known and Unknown Study Items in a Memory Task Using Single-Trial Event-Related Potentials and Convolutional Neural Networks by Jorge Delgado-Munoz, Reiko Matsunaka, Kazuo Hiraki

“…Recent advancements in convolutional neural networks (CNNs) have enabled the classification of ERP trials under different conditions and the identification of features related to neural processes at the single-trial level. …”
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Lifelong Learning-Enabled Fractional Order-Convolutional Encoder Model for Open-Circuit Fault Diagnosis of Power Converters Under Multi-Conditions by Tao Li, Enyu Wang, Jun Yang

“…Firstly, the model automatically extracts and identifies fault signal features using the convolutional module and the encoder module, respectively. …”
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Tilting Pad Thrust Bearing Fault Diagnosis Based on Acoustic Emission Signal and Modified Multi-Feature Fusion Convolutional Neural Network by Meijiao Mao, Zhiwen Jiang, Zhifei Tan, Wenqiang Xiao, Guangchao Du

“…Learning was then conducted on the signal fused with multiple features using the inverse-add module and spanning convolution. Next, a comparative analysis was carried out among the CNN1D, ResNet, and DFCNN models, and the MMFCNN model proposed in this study. …”
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MATLAB Application for User-Friendly Design of Fully Convolutional Data Description Models for Defect Detection of Industrial Products and Its Concurrent Visualization by Fusaomi Nagata, Shingo Sakata, Keigo Watanabe, Maki K. Habib, Ahmad Shahrizan Abdul Ghani

“…In this paper, a fully convolutional data description (FCDD) model is applied to defect detection and its concurrent visualization for industrial products and materials. …”
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Design of a Classification Recognition Model for Bone and Muscle Anatomical Imaging Based on Convolutional Neural Network and 3D Magnetic Resonance by Ting Pan, Yang Yang

“…In this paper, we use convolutional neural networks to conduct in-depth research and analysis on the classification and recognition of bone and muscle anatomical imaging graphics of 3D magnetic resonance and design corresponding models for practical applications. …”
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Harnessing Real-Time UV Imaging and Convolutional Neural Networks (CNNs): Unlocking New Opportunities for Empirical In Vitro–In Vivo Relationship Modelling by Maciej Stróżyk, Adam Pacławski, Aleksander Mendyk

“…<b>Background:</b> This study delves into the potential use of real-time UV imaging of the dissolution process combined with convolutional neural networks (CNNs) to develop multidimensional models representing the relation between in vitro and in vivo performance of drugs. …”
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DRR-YOLO: A Study of Small Target Multi-Modal Defect Detection for Multiple Types of Insulators Based on Large Convolution Kernel by Mingming Hu, Jun Liu, Junfu Liu

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3D medical image segmentation using the serial–parallel convolutional neural network and transformer based on cross‐window self‐attention by Bin Yu, Quan Zhou, Li Yuan, Huageng Liang, Pavel Shcherbakov, Xuming Zhang

“…The MLE module selectively fuses features by computing the voxel attention between different branch features, and uses convolution to strengthen the dense local information. …”
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Advancing Alzheimer’s disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis by Md Nurul Ahad Tawhid, Siuly Siuly, Enamul Kabir, Yan Li

“…We have tested different segment lengths to test the impact on AD detection. …”
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A transfer learning-based graph convolutional network for dynamic security assessment considering loss of synchronism of wind turbines and unknown faults by Sasan Azad, Mohammad Taghi Ameli, Amjad Anvari-Moghaddam, Miadreza Shafie-khah

“…To tackle these challenges, this paper introduces a new dynamic security index that considers the effects of loss of synchronism in power electronics-based units on DSA. Also, a graph convolutional network (GCN)-based model is developed to improve DSA accuracy by incorporating the topological information of the power system in the form of an adjacency matrix. …”
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A New Classification Method in Ultrasound Images of Benign and Malignant Thyroid Nodules Based on Transfer Learning and Deep Convolutional Neural Network by Weibin Chen, Zhiyang Gu, Zhimin Liu, Yaoyao Fu, Zhipeng Ye, Xin Zhang, Lei Xiao

“…The joint training of different data sets and the secondary transfer learning further improved its accuracy. …”
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The deep separable convolution with DSC NCF model and optimization mechanism of digital economy for intelligent manufacturing under sales order recommendation algorithm by Jin Qiu

“…The study employs the Deep Separable Convolutional Neural Collaborative Filtering (DSC-NCF) algorithm, combined with the publicly available smart manufacturing dataset Alibaba Click and Conversion Prediction (Ali-CCP), to build a deep learning-based intelligent recommendation platform. …”
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A multi-graph convolutional network method for Alzheimer’s disease diagnosis based on multi-frequency EEG data with dual-mode connectivity by Qingjie Xu, Qingjie Xu, Libing An, Haiqiang Yang, Haiqiang Yang, Keum-Shik Hong, Keum-Shik Hong

“…Moreover, many existing approaches fail to fully integrate multi-frequency EEG features, limiting the comprehensive understanding of dynamic brain activity across different frequency bands. This study aims to address these limitations by developing a novel graph-based deep learning model that fully utilizes both functional and structural information from multi-frequency EEG data.MethodsThis paper introduces a Multi-Frequency EEG data-based Multi-Graph Convolutional Network (MF-MGCN) model for AD diagnosis. …”
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Automated and Enhanced Classification of Persea Americana Using Optimized Deep Convolutional Neural Networks With Improved Training Strategies for Agro-Industrial Settings by Oscar Vera, Jose Cruz, Severo Huaquipaco, Wilson Mamani, Victor Yana-Mamani, Saul Huaquipaco

“…This paper proposes a machine learning model that correctly identifies the different attributes of Persea americana. For this, an automatic agro-industrial plant was implemented following industrial standards where advanced image processing techniques were used on a dataset of 346 images for training and 146 images for testing, with three deep convolutional neural networks with improved training strategies and advanced validation techniques including True Skill Statistic (TSS), Cohen&#x2019;s Kappa (K), Threat Score (TS), Heidke Skill Score (HSS) and Probability of Error (Pe). …”
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