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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Dual conditional GAN based on external attention for semantic image synthesis by Gang Liu, Qijun Zhou, Xiaoxiao Xie, Qingchen Yu

“…The graph attention (GAT) is added to the generator to strengthen the relationship between different categories in the generated image. A graph convolutional segmentation network (GSeg) is designed to learn information for each category. …”
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A steel surface defect detection method based on improved RetinaNet by Zhanglin Yang, Yu Liu

“…Abstract To address the issue of low detection accuracy caused by the variety of steel surface defect types, large shape differences, and the similarity between defects and the background, this paper proposes an improved method for detecting steel surface defects based on RetinaNet. …”
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A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion by Qi Wang, Guanghu Xu, Donglin Jing

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METRIC: Multiple preferences learning with refined item attributes for multimodal recommendation by Yunfei Zhao, Jie Guo, Longyu Wen, Letian Wang

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YOLO-LSM: A Lightweight UAV Target Detection Algorithm Based on Shallow and Multiscale Information Learning by Chenxing Wu, Changlong Cai, Feng Xiao, Jiahao Wang, Yulin Guo, Longhui Ma

“…Second, a Multiscale Lightweight Convolution (MLConv) is designed, and a lightweight feature extraction module, MLCSP, is constructed to enhance the extraction of detailed information. …”
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Jordanian banknote data recognition: A CNN-based approach with attention mechanism by Ahmad Nasayreh, Ameera S. Jaradat, Hasan Gharaibeh, Waed Dawaghreh, Rabia Mehamad Al Mamlook, Yaqeen Alqudah, Qais Al-Na'amneh, Mohammad Sh. Daoud, Hazem Migdady, Laith Abualigah

“…The study made use of a data set from Kaggle that includes a collection of Jordanian banknotes in five different denominations. Image processing techniques were employed to produce artificial images by boosting the brightness of real ones. …”
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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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Partial-Net: A Method for Data Gaps Reconstruction on Mars Images by Depei Gu, Dingruibo Miao, Jianguo Yan, Zhigang Tu, Jean-Pierre Barriot

“…This often results in artifacts, such as color differences and blurriness. In addition, existing mask sets commonly used in computer vision cannot simulate and learn the particular irregular shapes of data gaps in Mars images well. …”
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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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