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 address the issue of unknown faults, this paper uses transfer learning based on full fine-tuning to adapt a pre-trained GCN model to a different but related unknown fault. This approach eliminates the need for a large number of labeled examples for new faults and ensures efficient transfer of the model to new faults with a small database. …”
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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

“…By comparing it with traditional Neural Collaborative Filtering (NCF), Factorization Machine (FM), and other benchmark algorithms, the study evaluates key performance indicators such as accuracy, recall, F1 score, and Area Under the ROC Curve (AUC) of the DSC-NCF algorithm across different training epochs. The experimental results demonstrate the significant superiority of the DSC-NCF algorithm across all training epochs. …”
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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’s Kappa (K), Threat Score (TS), Heidke Skill Score (HSS) and Probability of Error (Pe). …”
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Multi scale convolutional neural network combining BiLSTM and attention mechanism for bearing fault diagnosis under multiple working conditions by Zhao Dengfeng, Tian Chaoyang, Fu Zhijun, Zhong Yudong, Hou Junjian, He Wenbin

“…To further improve the adaptability of the network to different load conditions, the parameters of pretrained MSCNN-BiLSTM-AM network are applied to initialize the new task model parameters. …”
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A Novel Diagnostic Framework with an Optimized Ensemble of Vision Transformers and Convolutional Neural Networks for Enhanced Alzheimer’s Disease Detection in Medical Imaging by Joy Chakra Bortty, Gouri Shankar Chakraborty, Inshad Rahman Noman, Salil Batra, Joy Das, Kanchon Kumar Bishnu, Md Tanvir Rahman Tarafder, Araf Islam

“…One of the major and primary challenges for preventing any disease is to identify the disease at the initial stage through a quick and reliable detection process. Different researchers across the world are still working relentlessly, coming up with significant solutions. …”
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Space-Frequency Fusion Dual-Branch Convolutional Neural Networks for Significant Wave Height Retrieval From GF-3 SAR Data by Xuan Jin, Yawei Zhao, Xin Zhang, Yanlei Du, Jinsong Chong

“…Consequently, our model exhibits applicability across diverse imaging modes and superior performance under different sea states. In addition, ablation experiments are conducted to evaluate the importance of the SFFCL and GFFL modules.…”
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DSMF-Net: A One-Stage SAR Ship Detection Network Based on Deformable Strip Convolution and Multiscale Feature Refinement and Fusion by Xingyu Liu, Jun Pan, Rong Hu, Wenli Huang, Jiawei Lin, Jiarui Hu

“…Through the mixing spatial and channel attention (MSCA) mechanism, differences and correlations between complex backgrounds and ship entities are further captured, enhancing feature expression. …”
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High precision light field image depth estimation via multi‐region attention enhanced network by Jie Li, Wenxuan Yang, Chuanlun Zhang, Heng Li, Xinjia Li, Lin Wang, Yanling Wang, Xiaoyan Wang

“…Firstly, we construct a multi‐region disparity selection module based on angular patch, which selects specific regions for generating angular patch, achieving representative sub‐angular patch by balancing different regions. Secondly, different from traditional guided deformable convolution, the guided optimisation leverages colour prior information to learn the aggregation of sampling points, which enhances the deformable convolution ability by learning deformation parameters and fitting irregular windows. …”
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Point‐convolution‐based human skeletal pose estimation on millimetre wave frequency modulated continuous wave multiple‐input multiple‐output radar by Jinxiao Zhong, Liangnian Jin, Ran Wang

“…The extraction of point cloud features is based on point‐by‐point convolution, that is, different weights are applied to different features of each point, which also increases the nonlinear expression ability of the model. …”
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Predictive machine health monitoring using deep convolution neural network for noisy vibration signal of rotating machine using empirical mode decomposition by R. Pavithra, Prakash Ramachandran

“…The study compares all IMFs of clean and noisy signals to quantify the impact of noise on EMD for 8 different specific faults of the CWRU bearing dataset. …”
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Investigating the effects of destructive factors on pulse repetition interval modulation type recognition using deep convolutional neural networks based on transfer learning by Mahshid Khodabandeh, Azar Mahmoodzadeh, Hamed Agahi

“…The current article examines the effects of destructive factors on recognising PRI modulation in radar signals using deep convolutional neural networks (DCNNs). The article uses simulations based on the actual environment to generate data and consider destructive factors with different percentages. …”
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Enhancing Fault Detection and Classification in Wind Farm Power Generation Using Convolutional Neural Networks (CNN) by Leveraging LVRT Embedded in Numerical Relays by Tarek Kandil, Adam Harris, Remon Das

“…To validate the model, a detailed analysis was performed, comparing different combinations of classifiers and optimizers. …”
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Impact of training data composition on the generalizability of convolutional neural network aortic cross-section segmentation in four-dimensional magnetic resonance flow imaging by Chiara Manini, Markus Hüllebrand, Lars Walczak, Sarah Nordmeyer, Lina Jarmatz, Titus Kuehne, Heiko Stern, Christian Meierhofer, Andreas Harloff, Jennifer Erley, Sebastian Kelle, Peter Bannas, Ralf Felix Trauzeddel, Jeanette Schulz-Menger, Anja Hennemuth

“…Methods: The study population consists of 260 4D flow CMR datasets, including subjects without known aortic pathology, healthy volunteers, and patients with bicuspid aortic valve (BAV) examined at different hospitals. The dataset was split to train segmentation models on subsets with different representations of characteristics, such as pathology, gender, age, scanner model, vendor, and field strength. …”
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CCDR: Combining Channel-Wise Convolutional Local Perception, Detachable Self-Attention, and a Residual Feedforward Network for PolSAR Image Classification by Jianlong Wang, Bingjie Zhang, Zhaozhao Xu, Haifeng Sima, Junding Sun

“…In the channel-wise convolutional local perception module, channel-wise convolution operations enable accurate extraction of local features from different channels of PolSAR images. …”
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Integrating convolutional neural networks for microscopic image analysis in acute lymphoblastic leukemia classification: A deep learning approach for enhanced diagnostic precision by Md. Samiul Alim, Suborno Deb Bappon, Shahriar Mahmud Sabuj, Md Jayedul Islam, M. Masud Tarek, Md. Shafiul Azam, Md. Monirul Islam

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