计算机科学最新文献

{"title":"Optimized encoder-decoder cascaded deep convolutional network for leaf disease image segmentation.","authors":"David Femi, Manapakkam Anandan Mukunthan","doi":"10.1080/0954898X.2024.2326493","DOIUrl":"10.1080/0954898X.2024.2326493","url":null,"abstract":"<p><p>Nowadays, Deep Learning (DL) techniques are being used to automate the identification and diagnosis of plant diseases, thereby enhancing global food security and enabling non-experts to detect these diseases. Among many DL techniques, a Deep Encoder-Decoder Cascaded Network (DEDCNet) model can precisely segment diseased areas from the leaf images to differentiate and classify multiple diseases. On the other hand, the model training depends on the appropriate selection of hyperparameters. Also, this network structure has weak robustness with different parameters. Hence, in this manuscript, an Optimized DEDCNet (ODEDCNet) model is proposed for improved leaf disease image segmentation. To choose the best DEDCNet hyperparameters, a brand-new Dingo Optimization Algorithm (DOA) is included in this model. The DOA depends on the foraging nature of dingoes, which comprises exploration and exploitation phases. In exploration, it attains many predictable decisions in the search area, whereas exploitation enables exploring the best decisions in a provided area. The segmentation accuracy is used as the fitness value of each dingo for hyperparameter selection. By configuring the chosen hyperparameters, the DEDCNet is trained to segment the leaf disease regions. The segmented images are further given to the pre-trained Convolutional Neural Networks (CNNs) followed by the Support Vector Machine (SVM) for classifying leaf diseases. ODEDCNet performs exceptionally well on the PlantVillage and Betel Leaf Image datasets, attaining an astounding 97.33% accuracy on the former and 97.42% accuracy on the latter. Both datasets achieve noteworthy recall, F-score, Dice coefficient, and precision values: the Betel Leaf Image dataset shows values of 97.4%, 97.29%, 97.35%, and 0.9897; the PlantVillage dataset shows values of 97.5%, 97.42%, 97.46%, and 0.9901, all completed in remarkably short processing times of 0.07 and 0.06 seconds, respectively. The achieved outcomes are evaluated with the contemporary optimization algorithms using the considered datasets to comprehend the efficiency of DOA.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"480-506"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141077390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing inset-fed rectangular micro strip patch antenna by improved particle swarm optimization and simulated annealing.","authors":"Jakkuluri Vijaya Kumar, S Maflin Shaby","doi":"10.1080/0954898X.2024.2358961","DOIUrl":"10.1080/0954898X.2024.2358961","url":null,"abstract":"<p><p>The recent wireless communication systems require high gain, lightweight, low profile, and simple antenna structures to ensure high efficiency and reliability. The existing microstrip patch antenna (MPA) design approaches attain low gain and high return loss. To solve this issue, the geometric dimensions of the antenna should be optimized. The improved Particle Swarm Optimization (PSO) algorithm which is the combination of PSO and simulated annealing (SA) approach (PSO-SA) is employed in this paper to optimize the width and length of the inset-fed rectangular microstrip patch antennas for Ku-band and C-band applications. The inputs to the proposed algorithm such as substrate height, dielectric constant, and resonant frequency and outputs are optimized for width and height. The return loss and gain of the antenna are considered for the fitness function. To calculate the fitness value, the Feedforward Neural Network (FNN) is employed in the PSO-SA approach. The design and optimization of the proposed MPA are implemented in MATLAB software. The performance of the optimally designed antenna with the proposed approach is evaluated in terms of the radiation pattern, return loss, Voltage Standing Wave Ratio (VSWR), gain, computation time, directivity, and convergence speed.</p>","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1282-1312"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141159287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statement of Retraction.","authors":"","doi":"10.1080/0954898X.2024.2385532","DOIUrl":"10.1080/0954898X.2024.2385532","url":null,"abstract":"","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"ii"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-Path Graph Neural Network with Adaptive Auxiliary Module for Link Prediction.","authors":"Zhenzhen Yang, Zelong Lin, Yongpeng Yang, Jiaqi Li","doi":"10.1089/big.2023.0130","DOIUrl":"10.1089/big.2023.0130","url":null,"abstract":"<p><p>Link prediction, which has important applications in many fields, predicts the possibility of the link between two nodes in a graph. Link prediction based on Graph Neural Network (GNN) obtains node representation and graph structure through GNN, which has attracted a growing amount of attention recently. However, the existing GNN-based link prediction approaches possess some shortcomings. On the one hand, because a graph contains different types of nodes, it leads to a great challenge for aggregating information and learning node representation from its neighbor nodes. On the other hand, the attention mechanism has been an effect instrument for enhancing the link prediction performance. However, the traditional attention mechanism is always monotonic for query nodes, which limits its influence on link prediction. To address these two problems, a Dual-Path Graph Neural Network (DPGNN) for link prediction is proposed in this study. First, we propose a novel Local Random Features Augmentation for Graph Convolution Network as a baseline of one path. Meanwhile, Graph Attention Network version 2 based on dynamic attention mechanism is adopted as a baseline of the other path. And then, we capture more meaningful node representation and more accurate link features by concatenating the information of these two paths. In addition, we propose an adaptive auxiliary module for better balancing the weight of auxiliary tasks, which brings more benefit to link prediction. Finally, extensive experiments verify the effectiveness and superiority of our proposed DPGNN for link prediction.</p>","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":"333-343"},"PeriodicalIF":2.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140289590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Polarization-Transforming Reconfigurable Intelligent Surface-Aided LoS Communications","authors":"Zhong Tian, Zhengchuan Chen, Min Wang, Jintao Wang, Xiaoheng Tan, Bo Ai, Tony Q. S. Quek","doi":"10.1109/twc.2025.3592868","DOIUrl":"https://doi.org/10.1109/twc.2025.3592868","url":null,"abstract":"","PeriodicalId":13431,"journal":{"name":"IEEE Transactions on Wireless Communications","volume":"149 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Amplitude Correlation and Structured Sparsity Inspired Compressed Sensing for Channel Estimation in RIS-aided MU-MISO Systems","authors":"Weijie Jin, Jing Zhang, Chao-Kai Wen, Shi Jin","doi":"10.1109/twc.2025.3592787","DOIUrl":"https://doi.org/10.1109/twc.2025.3592787","url":null,"abstract":"","PeriodicalId":13431,"journal":{"name":"IEEE Transactions on Wireless Communications","volume":"26 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic Geometry Approach assisted Reliability Analysis for OTFS based LEO-satellite-air-terrestrial communication","authors":"Junfan Hu, Zan Li, Jia Shi, Peichang Zhang, Pei Xiao, Rahim Tafazolli","doi":"10.1109/tcomm.2025.3594799","DOIUrl":"https://doi.org/10.1109/tcomm.2025.3594799","url":null,"abstract":"","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"9 Suppl 1 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum Passive Optical Networks: General Principles","authors":"Amir Mohammad Yaghoobianzadeh, Jawad A. Salehi","doi":"10.1109/tcomm.2025.3594787","DOIUrl":"https://doi.org/10.1109/tcomm.2025.3594787","url":null,"abstract":"","PeriodicalId":13041,"journal":{"name":"IEEE Transactions on Communications","volume":"31 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Translating to a Low-resource Language with Compiler Feedback: A Case Study on Cangjie","authors":"Jun Wang, Chenghao Su, Yijie Ou, Yanhui Li, Jialiang Tan, Lin Chen, Yuming Zhou","doi":"10.1109/tse.2025.3594908","DOIUrl":"https://doi.org/10.1109/tse.2025.3594908","url":null,"abstract":"","PeriodicalId":13324,"journal":{"name":"IEEE Transactions on Software Engineering","volume":"146 1","pages":""},"PeriodicalIF":7.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Multi-UAV-Enabled MEC Networks for Deep Learning Tasks: A Joint Offloading and Deployment Approach","authors":"Wei Liu, Cheng Zhan, Huan Yan, Die Yang","doi":"10.1109/tvt.2025.3594911","DOIUrl":"https://doi.org/10.1109/tvt.2025.3594911","url":null,"abstract":"","PeriodicalId":13421,"journal":{"name":"IEEE Transactions on Vehicular Technology","volume":"31 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}