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CONVOLUTION: 作品数：628被引量：731H指数：11; 导出分析报告; 相关领域：理学机械工程更多>>; 相关作者：朱延娟周来水李陶深何乐生贾积身更多>>; 相关机构：华中师范大学南京航空航天大学东南大学云南大学更多>>; 相关期刊：更多>>; 相关基金：国家自然科学基金中国博士后科学基金国家重点基础研究发展计划国家高技术研究发展计划更多>>

Multi-Layer Feature Extraction with Deformable Convolution for Fabric Defect Detection: 《Intelligent Automation & Soft Computing》2024年第4期725-744,共20页Jielin Jiang Chao Cui Xiaolong Xu Yan Cui; supported in part by the National Science Foundation of China under Grant 62001236;in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 20KJA520003.; In the textile industry,the presence of defects on the surface of fabric is an essential factor in determining fabric quality.Therefore,identifying fabric defects forms a crucial part of the fabric production process....; 关键词：Fabric defect detection multi-layer features deformable convolution

Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System: 《Intelligent Automation & Soft Computing》2024年第1期33-55,共23页M.Adimoolam K.Maithili N.M.Balamurugan R.Rajkumar S.Leelavathy Raju Kannadasan Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Arfat Ahmad Khan; supported by Project No.R-2023-23 of the Deanship of Scientific Research at Majmaah University.; At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)algorithms.Although various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns st...; 关键词：Brain tumor extended deep learning algorithm convolution neural network tumor detection deep learning

A Nonlinear Spatiotemporal Optimization Method of Hypergraph Convolution Networks for Traffic Prediction: 《Intelligent Automation & Soft Computing》2023年第9期3083-3100,共18页Difeng Zhu Zhimou Zhu Xuan Gong Demao Ye Chao Li Jingjing Chen; Traffic prediction is a necessary function in intelligent transporta-tion systems to alleviate traffic congestion.Graph learning methods mainly focus on the spatiotemporal dimension,but ignore the nonlinear movement o...; 关键词：Intelligent transportation systems traffic prediction hypergraph convolution networks spatiotemporal optimization

Atrous Convolution-Based Residual Deep CNN for Image Dehazing with Spider Monkey-Particle Swarm Optimization: 《Intelligent Automation & Soft Computing》2023年第8期1711-1728,共18页CH.Mohan Sai Kumar R.S.Valarmathi; Image dehazing is a rapidly progressing research concept to enhance image contrast and resolution in computer vision applications.Owing to severe air dispersion,fog,and haze over the environment,hazy images pose speci...; 关键词：Image dehazing computer vision convolutional neural network color distortion over-saturation pseudo-shadow phenomenon convergence rate

Railway Passenger Flow Forecasting by Integrating Passenger Flow Relationship and Spatiotemporal Similarity: 《Intelligent Automation & Soft Computing》2023年第8期1877-1893,共17页Song Yu Aiping Luo Xiang Wang; Railway passenger flow forecasting can help to develop sensible railway schedules,make full use of railway resources,and meet the travel demand of passengers.The structure of passenger flow in railway networks and the...; 关键词：Railway passenger flow forecast graph convolution neural network passenger flow relationship passenger flow similarity

CNN-LSTM: A Novel Hybrid Deep Neural Network Model for Brain Tumor Classification: 《Intelligent Automation & Soft Computing》2023年第7期1129-1143,共15页R.D.Dhaniya K.M.Umamaheswari; Current revelations in medical imaging have seen a slew of computer-aided diagnostic(CAD)tools for radiologists developed.Brain tumor classification is essential for radiologists to fully support and better interpret ...; 关键词：Brain tumor segmentation particle swarm optimization CNN-LSTM convolution neural network

PF-YOLOv4-Tiny: Towards Infrared Target Detection on Embedded Platform: 《Intelligent Automation & Soft Computing》2023年第7期921-938,共18页Wenbo Li Qi Wang Shang Gao; supported by The Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grants No.19JKB520031).; Infrared target detection models are more required than ever before to be deployed on embedded platforms,which requires models with less memory consumption and better real-time performance while considering accuracy.T...; 关键词：Infrared target detection visual attention module spatial pyramid pooling dual-path feature fusion depthwise separable convolution soft-NMS

Lightweight Method for Plant Disease Identification Using Deep Learning: 《Intelligent Automation & Soft Computing》2023年第7期525-544,共20页Jianbo Lu Ruxin Shi Jin Tong Wenqi Cheng Xiaoya Ma Xiaobin Liu; supported by the Guangxi Key R&D Project(Gui Ke AB21076021);the Project of Humanities and social sciences of“cultivation plan for thousands of young and middle-aged backbone teachers in Guangxi Colleges and universities”in 2021:Research on Collaborative integration of logistics service supply chain under high-quality development goals(2021QGRW044).; In the deep learning approach for identifying plant diseases,the high complexity of the network model,the large number of parameters,and great computational effort make it challenging to deploy the model on terminal d...; 关键词：Plant disease identification mixed depthwise convolution LIGHTWEIGHT ShuffleNetV2 attention mechanism

Graph Convolutional Neural Network Based Malware Detection in IoT-Cloud Environment被引量：1: 《Intelligent Automation & Soft Computing》2023年第6期2897-2914,共18页Faisal SAlsubaei Haya Mesfer Alshahrani Khaled Tarmissi Abdelwahed Motwakel; Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R237);Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia;The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR13).; Cybersecurity has become the most significant research area in the domain of the Internet of Things(IoT)owing to the ever-increasing number of cyberattacks.The rapid penetration of Android platforms in mobile devices ...; 关键词：CYBERSECURITY IoT CLOUD malware detection graph convolution network

Early Detection Glaucoma and Stargardt’s Disease Using Deep Learning Techniques: 《Intelligent Automation & Soft Computing》2023年第5期1283-1299,共17页Somasundaram Devaraj Senthil Kumar Arunachalam; Retinal fundus images are used to discover many diseases.Several Machine learning algorithms are designed to identify the Glaucoma disease.But the accuracy and time consumption performance were not improved.To address...; 关键词：Glaucoma detection max pool convolution neural network kuanﬁlter radial basis function

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