PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature Fusion by Heqi Yang, Junming Dong, Cancan Wang, Zhida Lian, Hui Chang

“…First, a developed Pre-convolution Receptive Field Enhancement (PRFE) module replaces C3k in the C3k2 module. …”
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Deep learning-assisted screening and diagnosis of scoliosis: segmentation of bare-back images via an attention-enhanced convolutional neural network by Xingyu Duan, Xiaojun Ma, Mengqi Zhu, Linan Wang, Dingqi You, Lili Deng, Ningkui Niu

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Boreal tree species classification using airborne laser scanning data annotated with harvester production reports, and convolutional neural networks by Raul de Paula Pires, Christoffer Axelsson, Eva Lindberg, Henrik Jan Persson, Kenneth Olofsson, Johan Holmgren

“…Then, the individual tree-level ALS point clouds were converted into 2D images from multiple viewing angles, with varying image dimensions and pixel sizes to accommodate trees of different sizes. These images served as input for CNN-based classification, enabling species identification across ALS datasets with varying spectral and spatial resolutions. …”
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HD-MVCNN: High-density ECG signal based diabetic prediction and classification using multi-view convolutional neural network by D. Santhakumar, K. Dhana Shree, M. Buvanesvari, A. Saran Kumar, Ayodeji Olalekan Salau

“…Traditional electrocardiogram recordings utilize twelve channels, each capturing a complex combination of activities originating from different regions of the heart. Examining ECG signals recorded on the body’s surface may not be an effective method for studying and diagnosing diabetic issues. …”
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A novel device-free Wi-Fi indoor localization using a convolutional neural network based on residual attention by Mashael Maashi, Alanoud Al Mazroa, Shoayee Dlaim Alotaibi, Asma Alshuhail, Muhammad Kashif Saeed, Ahmed S. Salama

“…In addition, we study how the precision change of different inertial dimension units may negatively influence the tracking performance, and we implement a solution to the problem of exactness variance. …”
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Seismic Foresight: A Novel Multi-Input 1D Convolutional Mixer Model for Earthquake Prediction Using Ionospheric Signals by Hakan Uyanik, Mehmet Kokum, Erman Senturk, Mohamed Freeshah, Salih T. A. Ozcelik, Muhammed Halil Akpinar, Serenay Celik, Abdulkadir Sengur

“…Future work should focus on validating the model’s performance in different geographical regions and investigating its limitations.…”
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Application of deep learning based on convolutional neural network model in multimodal ultrasound diagnosis of unexplained cervical lymph node enlargement by Shanshan Jiang, Naiqian Zhang, Chen Li, Lingxia Tong, Xiuhua Yang

“…Statistically significant differences were found in the clinical and ultrasound features of all patients, including location, shape, margin, and color Doppler flow imaging (CDFI) (p<0.05). …”
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FLEDNet: Enhancing the Drone Classification in the Radio Frequency Domain by Boban Sazdic-Jotic, Milenko Andric, Boban Bondzulic, Slobodan Simic, Ivan Pokrajac

“…Researchers are actively pursuing advancements in convolutional neural networks and their application in anti-drone systems for drone classification tasks. …”
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Rolling Bearing Degradation Identification Method Based on Improved Monopulse Feature Extraction and 1D Dilated Residual Convolutional Neural Network by Chang Liu, Haiyang Wu, Gang Cheng, Hui Zhou, Yusong Pang

“…The established 1D-DRCNN model integrates the advantages of dilated convolution and residual connections and can deeply mine sensitive features and accurately identify different bearing degradation states. …”
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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

“…Specifically, vibration signals from circuit breaker sensors are firstly transformed into Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF) images. These images are then combined into multi-channel inputs for parallel CNN modules to extract and fuse complementary features. …”
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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

“…Unlike the basic Gaussian Mixture Model (GMM), the proposed model dynamically adjusts the learning rate according to the content difference between adjacent frames and optimizes the number of Gaussian distributions through time series histogram analysis of pixels. …”
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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

“…The results show that under consistent operating conditions, the MMFCNN model achieves an average fault diagnosis accuracy of 99.58% when utilizing AE signal data from tilting pad thrust bearings in four states as inputs. Furthermore, when different operational conditions are introduced, the MMFCNN model also outperforms other models in terms of accuracy.…”
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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

“…Models supported by the application include the following original designs: convolutional neural network (CNN), transfer learning-based CNN, NN-based support vector machine (SVM), convolutional autoencoder (CAE), variational autoencoder (VAE), fully convolution network (FCN) (such as U-Net), and YOLO. …”
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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

“…A series of medical image segmentation models based on convolutional neural networks is proposed. In this paper, firstly, a separated attention mechanism is introduced in the model, which divides the input data into multiple paths, applies self-attention weights to adjacent data paths, and finally fuses the weighted values to form the basic convolutional block. …”
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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>Result:</b> Moreover, results were captured at different wavelengths (255 nm and 520 nm) to provide a comprehensive view of the process. …”
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