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

“…Deep learning in synthetic aperture radar (SAR) sea state retrieval is becoming increasingly prevalent. In current studies, convolutional neural networks (CNNs) are widely employed to extract either deep space features from normalized radar cross section (NRCS) of SAR images or deep frequency features from SAR spectra, with some studies combining artificially designed scalar features to retrieve significant wave height (SWH). …”
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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

“…This framework contains EEG data collection, pre-processing for noise removal, temporal segmentation, convolutional neural network (CNN) model training and classification, and finally, evaluation. …”
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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

“…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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Node Classification Based on Kolmogorov-Arnold Networks by YUAN Lining, FENG Wengang, LIU Zhao

“…In the node classification task, G-KAN outperforms the currently advanced baselines and raises Micro-F1 and Macro-F1 by 50.42 and 52.84 percentage points respectively compared with graph convolutional networks (GCN) on BlogCatalog. …”
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Mal-Detect: An intelligent visualization approach for malware detection by Olorunjube James Falana, Adesina Simon Sodiya, Saidat Adebukola Onashoga, Biodun Surajudeen Badmus

“…This work used an ensemble technique consisting of Deep Convolutional Neural Network and Deep Generative Adversarial Neural Network (Mal-Detect) to analyse, detect, and categorise malware. …”
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DPHNet: Dual-Path Hybrid Network for Blurry Face Image Super-Resolution by Tailai Qiu, Yubao Yan

“…Face images captured in the real world are usually corrupted by motion blur, which inevitably degrades the performance of current face super-resolution methods. To remedy this, in this study, a dual-path hybrid network with a CNN branch and a Transformer branch is developed. …”
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Multi-Scale Channel Distillation Network for Image Compressive Sensing by Tianyu Zhang, Kuntao Ye, Yue Zhang, Rui Lu

“…Recently, convolutional neural networks (CNNs) have demonstrated striking success in computer vision tasks. …”
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From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications by Evgenia Gkintoni, Anthimos Aroutzidis, Hera Antonopoulou, Constantinos Halkiopoulos

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Learning and Generation of Drawing Sequences Using a Deep Network for a Drawing Support System by Homari Matsumoto, Atomu Nakamura, Shun Nishide

“…We developed an encoder–decoder model based on convolutional neural networks to predict the next frame from a current input image. …”
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A novel framework to identify delamination location/size in BFRP pipe based on convolutional neural network (CNN) algorithm hybrid with capacitive sensors by Wael A. Altabey

“…Therefore, a new type of convolutional neural network (CNN) algorithm is adopted to train and test the EPC maps to evaluate delamination location/size. …”
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Grounding Fault Diagnosis of Running Rails Based on a Multi-scale One-Dimensional Convolutional Neural Network in a DC Subway System by Guifu Du, Na Liu, Dongliang Zhang, Qiaoyue Li, Jianxiang Sun, Xingxing Jiang, Zhongkui Zhu

“…In this paper, a method of grounding fault diagnosis of running rails based on a multi-scale one-dimensional convolutional neural network (MS-1DCNN) is proposed. …”
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Fault Diagnosis of Rotating Machines Based on Combination of One-Dimensional Convolutional Neural Network and Long Short-Term Memory in Variable Working Conditions by Fasikaw Kibrete, Dereje Engida Woldemichael, Hailu Shimels Gebremedhen

“…Deep learning models, particularly one-dimensional convolutional neural networks (1D CNNs), have shown great potential in the fault diagnosis of rotating machines. …”
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PM2.5 prediction and its influencing factors in the Beijing-Tianjin-Hebei urban agglomeration using spatial temporal graph convolutional networks by Yawen Zhao

“…To address this, this study uses spatiotemporal analysis and Spatial Temporal Graph Convolutional Networks (ST-GCN) to evaluate the variation and driving factors of PM _2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) urban agglomeration from 2014 to 2024, and to make predictions. …”
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Improving Tuberculosis Detection in Chest X-Ray Images Through Transfer Learning and Deep Learning: Comparative Study of Convolutional Neural Network Architectures by Alex Mirugwe, Lillian Tamale, Juwa Nyirenda

“…ObjectiveThis study aimed to evaluate the performance of 6 convolutional neural network architectures—Visual Geometry Group-16 (VGG16), VGG19, Residual Network-50 (ResNet50), ResNet101, ResNet152, and Inception-ResNet-V2—in classifying chest x-ray (CXR) images as either normal or TB-positive. …”
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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

“…However, recognition pulse repetition interval (PRI) modulation is challenging in natural environments due to destructive factors, including missing pulses (MP), spurious pulses (SP), and large outliers (LO) (caused by antenna scanning), which lead to noisy sequences of PRI variation patterns. The current article examines the effects of destructive factors on recognising PRI modulation in radar signals using deep convolutional neural networks (DCNNs). …”
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Detection and Severity Assessment of Parkinson’s Disease Through Analyzing Wearable Sensor Data Using Gramian Angular Fields and Deep Convolutional Neural Networks by Sayyed Mostafa Mostafavi, Shovito Barua Soumma, Daniel Peterson, Shyamal H. Mehta, Hassan Ghasemzadeh

“…In the present study, we developed a method for the diagnosis and severity assessment of PD using Gramian Angular Fields (GAFs) in combination with deep Convolutional Neural Networks (CNNs). Our model was applied to PD gait signals captured using pressure sensors embedded into insoles. …”
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Probabilistic Forecasting of Ground Magnetic Perturbation Spikes at Mid‐Latitude Stations by Michael Coughlan, Amy Keesee, Victor Pinto, Raman Mukundan, José Paulo Marchezi, Jeremiah Johnson, Hyunju Connor, Don Hampton

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A Hybrid Convolutional–Transformer Approach for Accurate Electroencephalography (EEG)-Based Parkinson’s Disease Detection by Chayut Bunterngchit, Laith H. Baniata, Hayder Albayati, Mohammad H. Baniata, Khalid Alharbi, Fanar Hamad Alshammari, Sangwoo Kang

“…To overcome these challenges, this study proposes a convolutional transformer enhanced sequential model (CTESM), which integrates convolutional neural networks, transformer attention blocks, and long short-term memory layers to capture spatial, temporal, and sequential EEG features. …”
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FDCN-C: A deep learning model based on frequency enhancement, deformable convolution network, and crop module for electroencephalography motor imagery classification. by Hong-Jie Liang, Ling-Long Li, Guang-Zhong Cao

“…Secondly, for temporal feature extraction, a deformable convolution network is employed to enhance feature extraction capabilities, utilizing offset parameters to modulate the convolution kernel size. …”
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Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN by Ming Zeng, Min Wang, Fuqiang Xie, Zhiwei Ji

“…Additionally, the current training of models lacks an effective guiding mechanism, leading to the insufficient improvement of network’s representation capabilities. …”
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