Exploring the Latent Information in Spatial Transcriptomics Data via Multi‐View Graph Convolutional Network Based on Implicit Contrastive Learning by Sheng Ren, Xingyu Liao, Farong Liu, Jie Li, Xin Gao, Bin Yu

“…This study introduces STMIGCL, a novel framework that leverages a multi‐view graph convolutional network and implicit contrastive learning. …”
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3-D Model Extraction Network Based on RFM-Constrained Deformation Inference and Self-Similar Convolution for Satellite Stereo Images by Wen Chen, Hao Chen, Shuting Yang

“…Meanwhile, deep-learning methods require a large number of training samples and restoring the complete 3-D structure of the target is challenging when it is quite different from the training sample. To address these problems, we propose a 3-D extraction method for SSIs based on self-similar convolution and a deformation inference network constrained by a rational function model (RFM). …”
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Prediction of Potato Rot Level by Using Electronic Nose Based on Data Augmentation and Channel Attention Conditional Convolutional Neural Networks by Jiayu Mai, Haonan Lin, Xuezhen Hong, Zhenbo Wei

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A topology-guided high-quality solution learning framework for security-constraint unit commitment based on graph convolutional network by Liqian Gao, Lishen Wei, Shichang Cui, Jiakun Fang, Xiaomeng Ai, Wei Yao, Jinyu Wen

“…In this sense, this paper proposes a topology-guided high-quality solution learning framework based on graph convolutional network (GCN) and neighborhood search (NS). …”
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A Dense Bootstrap Contrastive Learning Method With 3-D Dynamic Convolution for Few-Shot PolSAR Image Classification by Nana Jiang, Wenbo Zhao, Jiao Guo, Xiuya Dong, Jubo Zhu

“…In response to this challenge, we propose a few-shot PolSAR image classification method based on dense bootstrap contrastive learning with 3-D dynamic convolution (DBCL-3DDC). The design of 3DDC enhances the feature extraction ability of the network for complex data. …”
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Application of 3D ELD_MobileNetV2 Incorporating Attention Mechanism and Dilated Convolution in Hepatic Nodules Classification by SUN Haoyun, WANG Lijia

“…Then, 3D dilated structure was introduced into depthwise convolution to improve the receptive field of the convolution kernel. …”
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BCINetV1: Integrating Temporal and Spectral Focus Through a Novel Convolutional Attention Architecture for MI EEG Decoding by Muhammad Zulkifal Aziz, Xiaojun Yu, Xinran Guo, Xinming He, Binwen Huang, Zeming Fan

“…The BCINetV1 utilizes three innovative components: a temporal convolution-based attention block (T-CAB) and a spectral convolution-based attention block (S-CAB), both driven by a new convolutional self-attention (ConvSAT) mechanism to identify key non-stationary temporal and spectral patterns in the EEG signals. …”
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MEMPSEP‐I. Forecasting the Probability of Solar Energetic Particle Event Occurrence Using a Multivariate Ensemble of Convolutional Neural Networks by Subhamoy Chatterjee, Maher A. Dayeh, Andrés Muñoz‐Jaramillo, Hazel M. Bain, Kimberly Moreland, Samuel Hart

“…MEMPSEP workhorse is an ensemble of Convolutional Neural Networks that ingests a comprehensive data set (MEMPSEP‐III by Moreland et al. (2024, https://doi.org/10.1029/2023SW003765)) of full‐disc magnetogram‐sequences and in situ data from different sources to forecast the occurrence (MEMPSEP‐I—this work) and properties (MEMPSEP‐II by Dayeh et al. (2024, https://doi.org/10.1029/2023SW003697)) of a SEP event. …”
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Implementation Of Deep Learning Using Convolutional Neural Network Method In A Rupiah Banknote Detection System For Those With Low Vision by Dinul Akhiyar, Tukino Tukino, Sarjon Defit

“…In this project, a system was developed using the Convolutional Neural Network (CNN) architecture combined with the YOLO (You Only Look Once) algorithm. …”
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Fine-scale forest classification with multi-temporal sentinel-1/2 imagery using a temporal convolutional neural network by Rongfei Duan, Chunlin Huang, Peng Dou, Jinliang Hou, Ying Zhang, Juan Gu

“…However, spectral similarity between different vegetation types and the issue of mixed pixels in medium-resolution satellite imagery remain significant challenges for fine-scale forest classification. …”
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A Novel Approach for Visual Speech Recognition Using the Partition-Time Masking and Swin Transformer 3D Convolutional Model by Xiangliang Zhang, Yu Hu, Xiangzhi Liu, Yu Gu, Tong Li, Jibin Yin, Tao Liu

“…By adopting a strategy that combines Swin Transformer and 3D convolution, the proposed model enhances performance. …”
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Transforming Early Breast Cancer Detection: A Deep Learning Approach Using Convolutional Neural Networks and Advanced Classification Techniques by Arun Kumar Singh, Bireshwar Dass Mazumdar, Rohit Raja, Kamred Udham Singh, Ankit Kumar, Mohd Asif Shah

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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

“…This study explores the potential of spatially explicit Harvester Production Reports (HPRs) for automatic annotation of Aerial Laser Scanning (ALS) data at tree-level, enabling accurate tree species classification using Convolutional Neural Networks (CNNs). By integrating HPRs into the modelling process, this approach provides a practical solution for addressing challenges in remote sensing data annotation for forestry applications. …”
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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

“…This framework may help identify the hypoglycaemia effects on brain regions, leading to decreased complexity and increased theta and delta power during scalp electrocardiogram procedures. The convolutional architectural model primarily contributes to enhancement and optimization through its Stochastic Gradient Descent (SGD) along with convolutional layers and according to results, the HD-MVCNN demonstrated better stability and accuracy in comparison to traditional classification models. …”
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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

“…However, existing convolutional neural network (CNN) fingerprinting placement algorithms have a limited receptive area, limiting their effectiveness since important data in CSI has not been thoroughly explored. …”
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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

“…First, we pre-trained four convolutional neural networks using a public medical image dataset. …”
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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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