Multiagent and Grid Systems最新文献

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":"58 3","pages":""},"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":"23 2","pages":""},"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":"90 10","pages":""},"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

Effective movie recommendation based on improved densenet model 基于改进的密度网络模型的有效电影推荐

Multiagent and Grid Systems Pub Date : 2023-10-06 DOI: 10.3233/mgs-230012

V. Lakshmi Chetana, Raj Kumar Batchu, Prasad Devarasetty, Srilakshmi Voddelli, Varun Prasad Dalli

{"title":"Effective movie recommendation based on improved densenet model","authors":"V. Lakshmi Chetana, Raj Kumar Batchu, Prasad Devarasetty, Srilakshmi Voddelli, Varun Prasad Dalli","doi":"10.3233/mgs-230012","DOIUrl":"https://doi.org/10.3233/mgs-230012","url":null,"abstract":"In recent times, recommendation systems provide suggestions for users by means of songs, products, movies, books, etc. based on a database. Usually, the movie recommendation system predicts the movies liked by the user based on attributes present in the database. The movie recommendation system is one of the widespread, useful and efficient applications for individuals in watching movies with minimal decision time. Several attempts are made by the researchers in resolving these problems like purchasing books, watching movies, etc. through developing a recommendation system. The majority of recommendation systems fail in addressing data sparsity, cold start issues, and malicious attacks. To overcome the above-stated problems, a new movie recommendation system is developed in this manuscript. Initially, the input data is acquired from Movielens 1M, Movielens 100K, Yahoo Y-10-10, and Yahoo Y-20-20 databases. Next, the data are rescaled using a min-max normalization technique that helps in handling the outlier efficiently. At last, the denoised data are fed to the improved DenseNet model for a relevant movie recommendation, where the developed model includes a weighting factor and class-balanced loss function for better handling of overfitting risk. Then, the experimental result indicates that the improved DenseNet model almost reduced by 5 to 10% of error values, and improved by around 2% of f-measure, precision, and recall values related to the conventional models on the Movielens 1M, Movielens 100K, Yahoo Y-10-10, and Yahoo Y-20-20 databases.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135302331","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

Skin cancer detection: Improved deep belief network with optimal feature selection 皮肤癌检测:基于最优特征选择的改进深度信念网络

Multiagent and Grid Systems Pub Date : 2023-10-06 DOI: 10.3233/mgs-230040

Jinu P. Sainudeen, Ceronmani Sharmila V, Parvathi R

{"title":"Skin cancer detection: Improved deep belief network with optimal feature selection","authors":"Jinu P. Sainudeen, Ceronmani Sharmila V, Parvathi R","doi":"10.3233/mgs-230040","DOIUrl":"https://doi.org/10.3233/mgs-230040","url":null,"abstract":"During the past few decades, melanoma has grown increasingly prevalent, and timely identification is crucial for lowering the mortality rates linked to this kind of skin cancer. Because of this, having access to an automated, trustworthy system that can identify the existence of melanoma may be very helpful in the field of medical diagnostics. Because of this, we have introduced a revolutionary, five-stage method for detecting skin cancer. The input images are processed utilizing histogram equalization as well as Gaussian filtering techniques during the initial pre-processing stage. An Improved Balanced Iterative Reducing as well as Clustering utilizing Hierarchies (I-BIRCH) is proposed to provide better image segmentation by efficiently allotting the labels to the pixels. From those segmented images, features such as Improved Local Vector Pattern, local ternary pattern, and Grey level co-occurrence matrix as well as the local gradient patterns will be retrieved in the third stage. We proposed an Arithmetic Operated Honey Badger Algorithm (AOHBA) to choose the best features from the retrieved characteristics, which lowered the computational expense as well as training time. In order to demonstrate the effectiveness of our proposed skin cancer detection strategy, the categorization is done using an improved Deep Belief Network (DBN) with respect to those chosen features. The performance assessment findings are then matched with existing methodologies.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135302172","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

Deep embedded clustering with matrix factorization based user rating prediction for collaborative recommendation 基于矩阵分解的深度嵌入聚类协同推荐用户评分预测

Multiagent and Grid Systems Pub Date : 2023-10-06 DOI: 10.3233/mgs-230039

Jagannath E. Nalavade, Chandra Sekhar Kolli, Sanjay Nakharu Prasad Kumar

{"title":"Deep embedded clustering with matrix factorization based user rating prediction for collaborative recommendation","authors":"Jagannath E. Nalavade, Chandra Sekhar Kolli, Sanjay Nakharu Prasad Kumar","doi":"10.3233/mgs-230039","DOIUrl":"https://doi.org/10.3233/mgs-230039","url":null,"abstract":"Conventional recommendation techniques utilize various methods to compute the similarity among products and customers in order to identify the customer preferences. However, such conventional similarity computation techniques may produce incomplete information influenced by similarity measures in customers’ preferences, which leads to poor accuracy on recommendation. Hence, this paper introduced the novel and effective recommendation technique, namely Deep Embedded Clustering with matrix factorization (DEC with matrix factorization) for the collaborative recommendation. This approach creates the agglomerative matrix for the recommendation using the review data. The customer series matrix, customer series binary matrix, product series matrix, and product series binary matrix make up the agglomerative matrix. The product grouping is carried out to group the similar products using DEC for retrieving the optimal product. Moreover, the bi-level matching generates the best group customer sequence in which the relevant customers are retrieved using tversky index and angular distance. Also, the final product suggestion is made using matrix factorization, with the goal of recommending to clients the product with the highest rating. Also, according to the experimental results, the developed DEC with the matrix factorization approach produced better results with respect to f-measure values of 0.902, precision values of 0.896, and recall values of 0.908, respectively.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135302175","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

Deep learning based graphical password authentication approach against shoulder-surfing attacks 基于深度学习的图形密码认证方法对抗肩部冲浪攻击

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Multiagent and Grid Systems Pub Date : 2023-06-08 DOI: 10.3233/mgs-230024

Norman Dias, Mouleeswaran Singanallur Kumaresan, Reeja Sundaran Rajakumari

{"title":"Deep learning based graphical password authentication approach against shoulder-surfing attacks","authors":"Norman Dias, Mouleeswaran Singanallur Kumaresan, Reeja Sundaran Rajakumari","doi":"10.3233/mgs-230024","DOIUrl":"https://doi.org/10.3233/mgs-230024","url":null,"abstract":"The password used to authenticate users is vulnerable to shoulder-surfing assaults, in which attackers directly observe users and steal their passwords without using any other technical upkeep. The graphical password system is regarded as a likely backup plan to the alphanumeric password system. Additionally, for system privacy and security, a number of programs make considerable use of the graphical password-based authentication method. The user chooses the image for the authentication procedure when using a graphical password. Furthermore, graphical password approaches are more secure than text-based password methods. In this paper, the effective graphical password authentication model, named as Deep Residual Network based Graphical Password is introduced. Generally, the graphical password authentication process includes three phases, namely registration, login, and authentication. The secret pass image selection and challenge set generation process is employed in the two-step registration process. The challenge set generation is mainly carried out based on the generation of decoy and pass images by performing an edge detection process. In addition, edge detection is performed using the Deep Residual Network classifier. The developed Deep Residual Network based Graphical Password algorithm outperformance than other existing graphical password authentication methods in terms of Information Retention Rate and Password Diversity Score of 0.1716 and 0.1643, respectively.","PeriodicalId":43659,"journal":{"name":"Multiagent and Grid Systems","volume":"1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42348269","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: 1