Research on Land Use and Land Cover Information Extraction Methods for Remote Sensing Images Based on Improved Convolutional Neural Networks by Xue Ding, Zhaoqian Wang, Shuangyun Peng, Xin Shao, Ruifang Deng

“…Next, a novel PMFF module is designed to effectively promote the fusion of features at different scales, deepening the model’s understanding of global and local spatial contextual information. …”
Get full text

Investigating a three-dimensional convolution recognition model for acoustic emission signal analysis during uniaxial compression failure of coal by Tao Wang, Zishuo Liu, Liyuan Liu

“…DenseNet + GC + SE showed a probability distribution focusing on different risk levels. By integrating group convolution and SE modules, this model significantly reduced both model and time complexity while preserving precision, enhancing efficiency. …”
Get full text

Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning by Rubaiya Hafiz, Mohammad Reduanul Haque, Aniruddha Rakshit, Mohammad Shorif Uddin

“…The experiment confirms that our system can detect and recognize different types of drinks with an accuracy of 98.51%.…”
Get full text

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

“…Finally, an attention mechanism is used to adaptively integrate different views, capturing the importance of spots in various views to obtain the final spot representation. …”
Get full text

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). …”
Get full text

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

Get full text

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

“…Thirdly, a customized prediction-based NS is developed to restore the feasibility of the predicted commitment. Case studies with different scales verify the effectiveness and efficiency of the proposed framework for SCUC. …”
Get full text

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

“…The effectiveness of the proposed method is validated through experiments on three different datasets. Notably, on the Flevoland 1989 dataset, DBCL-3DDC achieves an overall accuracy of 97.29% using only 0.2% of labeled samples.…”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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

“…To further evaluate the system's reliability, tests were conducted under varying conditions, such as banknotes with creases, folds, or different lighting scenarios. These tests resulted in an mAP score of 88%, showcasing the system's adaptability to real-world conditions. …”
Get full text

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. …”
Get full text

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

“…However, this technology still faces challenges, such as limited generalization ability due to different speech habits, high recognition error rates caused by confusable phonemes, and difficulties adapting to complex lighting conditions and facial occlusions. …”
Get full text

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

Get full text

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. …”
Get full text

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

Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text