Multiagent and Grid Systems最新文献

Sine tangent search algorithm enabled LeNet for cotton crop classification using satellite image 利用卫星图像对棉花作物分类的正切搜索算法启用 LeNet

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Multiagent and Grid Systems Pub Date : 2024-03-04 DOI: 10.3233/mgs-230055

Devyani Jadhav Bhamare, Ramesh Pudi, Garigipati Rama Krishna

{"title":"Sine tangent search algorithm enabled LeNet for cotton crop classification using satellite image","authors":"Devyani Jadhav Bhamare, Ramesh Pudi, Garigipati Rama Krishna","doi":"10.3233/mgs-230055","DOIUrl":"https://doi.org/10.3233/mgs-230055","url":null,"abstract":"Economic growth of country largely depends on crop production quantity and quality. Among various crops, cotton is one of the major crops in India, where 23 percent of cotton gets exported to various other countries. To classify these cotton crops, farmers consume much time, and this remains inaccurate most probably. Hence, to eradicate this issue, cotton crops are classified using deep learning model, named LeNet in this research paper. Novelty of this paper lies in utilization of hybrid optimization algorithm, named proposed sine tangent search algorithm for training LeNet. Initially, hyperspectral image is pre-processed by anisotropic diffusion, and then allowed for further processing. Also, SegNet is deep learning model that is used for segmenting pre-processed image. For perfect and clear details of pre-processed image, feature extraction is carried out, wherein vegetation index and spectral spatial features of image are found accurately. Finally, cotton crop is classified from segmented image and features extracted, using LeNet that is trained by sine tangent search algorithm. Here, sine tangent search algorithm is formed by hybridization of sine cosine algorithm and tangent search algorithm. Then, performance of sine tangent search algorithm enabled LeNet is assessed with evaluation metrics along with Receiver Operating Characteristic (ROC) curve. These metrics showed that sine tangent search algorithm enabled LeNet is highly effective for cotton crop classification with superior values of accuracy of 91.7%, true negative rate of 92%, and true positive rate of 92%.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140079913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blockchain applications for Internet of Things (IoT): A review 物联网（IoT）的区块链应用：综述

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Multiagent and Grid Systems Pub Date : 2024-03-04 DOI: 10.3233/mgs-230074

A. Laghari, Hang Li, Shoulin Yin, Shahid Karim, A. Khan, Muhammad Ibrar

引用次数: 0

Optimization enabled elastic scaling in cloud based on predicted load for resource management 根据预测负载优化云中的弹性扩展，实现资源管理

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Multiagent and Grid Systems Pub Date : 2024-03-04 DOI: 10.3233/mgs-230003

Naimisha Shashikant Trivedi, Shailesh D. Panchal

{"title":"Optimization enabled elastic scaling in cloud based on predicted load for resource management","authors":"Naimisha Shashikant Trivedi, Shailesh D. Panchal","doi":"10.3233/mgs-230003","DOIUrl":"https://doi.org/10.3233/mgs-230003","url":null,"abstract":"Cloud computing epitomizes an important invention in the field of Information Technology, which presents users with a way of providing on-demand access to a pool of shared computing resources. A major challenge faced by the cloud system is to assign the exact quantity of resources to the users based on the demand, while meeting the Service Level Agreement (SLA). Elasticity is a major aspect that provides the cloud with the capability of adding and removing resources “on the fly” for handling load variations. However, elastic scaling requires suspension of the application tasks forcibly, while performing resource distribution; thereby Quality of Service (QoS) gets affected. In this research, an elastic scaling approach based on optimization is developed which aims at attaining an improved user experience. Here, load prediction is performed based on various factors, like bandwidth, CPU, and memory. Later, horizontal as well as vertical scaling is performed based on the predicted load using the devised leader Harris honey badger algorithm. The devised optimization enabled elastic scaling is evaluated for its effectiveness based on metrics, such as predicted load error, cost, and resource utilization, and is found to have attained values of 0.0193, 153.581, and 0.3217.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140080210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Geese jellyfish search optimization trained deep learning for multiclass plant disease detection using leaf images 利用叶片图像进行多类植物病害检测的鹅水母搜索优化训练深度学习

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Multiagent and Grid Systems Pub Date : 2024-03-04 DOI: 10.3233/mgs-230061

Bandi Ranjitha, Sampath A K

{"title":"Geese jellyfish search optimization trained deep learning for multiclass plant disease detection using leaf images","authors":"Bandi Ranjitha, Sampath A K","doi":"10.3233/mgs-230061","DOIUrl":"https://doi.org/10.3233/mgs-230061","url":null,"abstract":"Accurate and early detection of plant disease is significant for stable and proper agriculture and also for preventing the unwanted waste of financial and other possessions. Hence, a new technique is devised in this work, where geese jellyfish search optimization trained deep learning is used for multiclass detection of plant disease utilizing plant leaf images. At first, the input leaves of the plant image acquired from the database are pre-processed utilizing the Kalman filter. Then, the plant leaf segmentation is done by LinK-Net, where the training function of LinK-Net is processed by the proposed geese jellyfish search optimization, which is formed using wild geese migration optimization and jellyfish search optimizer. Then, image augmentation is carried out and then the feature extraction is done. Consequently, the classification of plant leaf type is processed, which is employed by Deep Q-Network (DQN), which is structurally adapted by the proposed geese jellyfish search optimization. At last, multi-label plant leaf disease is detected based on DQN. Moreover, the proposed geese jellyfish search optimization based DQN obtains an accuracy of 89.44%, true positive rate of 90.18%, and false positive rate of 10.56% respectively.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140080346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Load balancing model for cloud environment using swarm intelligence technique 使用蜂群智能技术的云环境负载平衡模型

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Multiagent and Grid Systems Pub Date : 2023-12-15 DOI: 10.3233/mgs-230021

G. Verma, Soumen Kanrar

引用次数: 0

Hybrid trust-based optimized virtual machine migration for dynamic load balancing and replica management in heterogeneous cloud 基于混合信任的优化虚拟机迁移，用于异构云中的动态负载平衡和副本管理

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Multiagent and Grid Systems Pub Date : 2023-12-15 DOI: 10.3233/mgs-230025

M. H. Nebagiri, Latha Pillappa Hanumanthappa

{"title":"Hybrid trust-based optimized virtual machine migration for dynamic load balancing and replica management in heterogeneous cloud","authors":"M. H. Nebagiri, Latha Pillappa Hanumanthappa","doi":"10.3233/mgs-230025","DOIUrl":"https://doi.org/10.3233/mgs-230025","url":null,"abstract":"Cloud computing is an upcoming technology that has garnered interest from academic as well as commercial domains. Cloud offers the advantage of providing huge computing capability as well as resources that are positioned at multiple locations irrespective of time or location of the user. Cloud utilizes the concept of virtualization to dispatch the multiple tasks encountered simultaneously to the server. However, allocation of tasks to the heterogeneous servers requires that the load is balanced among the servers. To address this issue, a trust based dynamic load balancing algorithm in distributed file system is proposed. Load balancing is performed by predicting the loads in the physical machine with the help of the Rider optimization algorithm-based Neural Network (RideNN). Further, load balancing is carried out using the proposed Fractional Social Deer Optimization (FSDO) algorithm, where the virtual machine migration is performed based on the load condition in the physical machine. Later, replica management is accomplished for managing the replica in distributed file system with the help of the devised FSDO algorithm. Moreover, the proposed FSDO based dynamic load balancing algorithm is evaluated for its performance based on parameters, like predicted load, prediction error, trust, cost and energy consumption with values 0.051, 0.723, 0.390 and 0.431J correspondingly.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138998387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adam Adadelta Optimization based bidirectional encoder representations from transformers model for fake news detection on social media 亚当-阿达德尔塔基于变压器模型的双向编码器表示法优化社交媒体上的假新闻检测

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Multiagent and Grid Systems Pub Date : 2023-12-15 DOI: 10.3233/mgs-230033

S. T. S., P.S. Sreeja

{"title":"Adam Adadelta Optimization based bidirectional encoder representations from transformers model for fake news detection on social media","authors":"S. T. S., P.S. Sreeja","doi":"10.3233/mgs-230033","DOIUrl":"https://doi.org/10.3233/mgs-230033","url":null,"abstract":"Social platform have disseminated the news in rapid speed and has been considered an important news resource for many people over worldwide because of easy access and less cost benefits when compared with the traditional news organizations. Fake news is the news deliberately written by bad writers that manipulates the original contents and this rapid dissemination of fake news may mislead the people in the society. As a result, it is critical to investigate the veracity of the data leaked via social media platforms. Even so, the reliability of information reported via this platform is still doubtful and remains a significant obstacle. As a result, this study proposes a promising technique for identifying fake information in social media called Adam Adadelta Optimization based Deep Long Short-Term Memory (Deep LSTM). The tokenization operation in this case is carried out with the Bidirectional Encoder Representations from Transformers (BERT) approach. The measurement of the features is reduced with the assistance of Kernel Linear Discriminant Analysis (LDA), and Singular Value Decomposition (SVD) and the top-N attributes are chosen by employing Renyi joint entropy. Furthermore, the LSTM is applied to identify false information in social media, with Adam Adadelta Optimization, which comprises a combo of Adam Optimization and Adadelta Optimization . The Deep LSTM based on Adam Adadelta Optimization achieved maximum accuracy, sensitivity, specificity of 0.936, 0.942, and 0.925.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138997736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An evolutionary mechanism of social preference for knowledge sharing in crowdsourcing communities 众包社区知识共享社会偏好的进化机制

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Multiagent and Grid Systems Pub Date : 2023-12-15 DOI: 10.3233/mgs-221532

Jianfeng Meng, Gongpeng Zhang, Zihan Li, Hongji Yang

{"title":"An evolutionary mechanism of social preference for knowledge sharing in crowdsourcing communities","authors":"Jianfeng Meng, Gongpeng Zhang, Zihan Li, Hongji Yang","doi":"10.3233/mgs-221532","DOIUrl":"https://doi.org/10.3233/mgs-221532","url":null,"abstract":"Crowdsourcing community, as an important way for enterprises to obtain external public innovative knowledge in the era of the Internet and the rise of users, has a very broad application prospect and research value. However, the influence of social preference is seldom considered in the promotion of knowledge sharing in crowdsourcing communities. Therefore, on the basis of complex network evolutionary game theory and social preference theory, an evolutionary game model of knowledge sharing among crowdsourcing community users based on the characteristics of small world network structure is constructed. Through Matlab programming, the evolution and dynamic equilibrium of knowledge sharing among crowdsourcing community users on this network structure are simulated, and the experimental results without considering social preference and social preference are compared and analysed, and it is found that social preference can significantly promote the evolution of knowledge sharing in crowdsourcing communities. This research expands the research scope of the combination and application of complex network games and other disciplines, enriches the theoretical perspective of knowledge sharing research in crowdsourcing communities, and has a strong guiding significance for promoting knowledge sharing in crowdsourcing communities.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138998918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Feature level fusion for land cover classification with landsat images: A hybrid classification model 基于陆地卫星影像的土地覆盖分类特征级融合:一种混合分类模型

Multiagent and Grid Systems Pub Date : 2023-10-06 DOI: 10.3233/mgs-230034

Malige Gangappa

引用次数: 0

Imbalanced data classification using improved synthetic minority over-sampling technique 利用改进的合成少数派过采样技术对不平衡数据进行分类

Multiagent and Grid Systems Pub Date : 2023-10-06 DOI: 10.3233/mgs-230007

Yamijala Anusha, R. Visalakshi, Konda Srinivas

引用次数: 0