Pub Date : 2020-07-01DOI: 10.33545/27075907.2020.v1.i2a.14
K. Mounya
{"title":"A cross-Tenant access control with efficient tenant revocation using CRMS scheme in cloud computing","authors":"K. Mounya","doi":"10.33545/27075907.2020.v1.i2a.14","DOIUrl":"https://doi.org/10.33545/27075907.2020.v1.i2a.14","url":null,"abstract":"","PeriodicalId":170946,"journal":{"name":"International Journal of Cloud Computing and Database Management","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133004062","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}
Pub Date : 2020-07-01DOI: 10.33545/27075907.2020.v1.i2a.15
N. Subalakshmi, M. Jeyakarthic
{"title":"Cloud resources analysis and prediction system using Deep Neural network","authors":"N. Subalakshmi, M. Jeyakarthic","doi":"10.33545/27075907.2020.v1.i2a.15","DOIUrl":"https://doi.org/10.33545/27075907.2020.v1.i2a.15","url":null,"abstract":"","PeriodicalId":170946,"journal":{"name":"International Journal of Cloud Computing and Database Management","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133337954","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}
Pub Date : 2020-07-01DOI: 10.33545/27075907.2020.v1.i2a.12
Ponaganti Suneel Kumar
{"title":"Multi authority access control mechanism for secure cloud storage","authors":"Ponaganti Suneel Kumar","doi":"10.33545/27075907.2020.v1.i2a.12","DOIUrl":"https://doi.org/10.33545/27075907.2020.v1.i2a.12","url":null,"abstract":"","PeriodicalId":170946,"journal":{"name":"International Journal of Cloud Computing and Database Management","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114739001","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}
Pub Date : 2020-07-01DOI: 10.33545/27075907.2020.v1.i2a.13
G. V. Reddy
{"title":"Protecting cloud based multimedia content using signatures","authors":"G. V. Reddy","doi":"10.33545/27075907.2020.v1.i2a.13","DOIUrl":"https://doi.org/10.33545/27075907.2020.v1.i2a.13","url":null,"abstract":"","PeriodicalId":170946,"journal":{"name":"International Journal of Cloud Computing and Database Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130456126","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}
Pub Date : 2020-01-01DOI: 10.33545/27075907.2020.v1.i1a.7
Abbas Abazari, A. Hatamlou
{"title":"A new framework for security and trust in cloud computing","authors":"Abbas Abazari, A. Hatamlou","doi":"10.33545/27075907.2020.v1.i1a.7","DOIUrl":"https://doi.org/10.33545/27075907.2020.v1.i1a.7","url":null,"abstract":"","PeriodicalId":170946,"journal":{"name":"International Journal of Cloud Computing and Database Management","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132234828","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}
Pub Date : 2020-01-01DOI: 10.33545/27075907.2020.v1.i1a.6
Amin Rezaeipanah, M. Mokhtari, Milad Boshkani Zadeh
Social network analysis is one of the most important research fields in data mining today. The purpose of the analysis of these networks is to extract the embedded knowledge in the data set and to learn the behavior of users in the social networking environment. One of the most attractive and central applications of social network analysis is link prediction. The purpose of link prediction in social networks is to identify missing and unknown information from users or to predict the future link between two users. In recent years, various artificial intelligence algorithms have been introduced as one of the most important tools for resolving link prediction and big data. In this research, a strategy based on a meta-heuristic is used to improve link prediction in social networks. The proposed method is based on the characteristics of the signed social networks provided and turns the link prediction problem into a two-class classification problem. Then uses the capability of the Particle Swarm Optimization (PSO) and the topological properties of the social network graph to create a database with two classes, the first class pointing to the existence of a connection between the users and the second class pointing to the absence of this relationship. Creates a database using the support vector machine model for categorization work and uses the classic Katz similarity criterion for end-user suggestions. Twitter social network information has been used to compare and evaluate the proposed method. The results of the experiments show the superiority of the proposed method with 0.23, 0.99, and 6.32, respectively, compared to Meta-Path, Katz and CN algorithms in F-measure criterion.
{"title":"Providing a new method for link prediction in social networks based on the meta-heuristic algorithm","authors":"Amin Rezaeipanah, M. Mokhtari, Milad Boshkani Zadeh","doi":"10.33545/27075907.2020.v1.i1a.6","DOIUrl":"https://doi.org/10.33545/27075907.2020.v1.i1a.6","url":null,"abstract":"Social network analysis is one of the most important research fields in data mining today. The purpose of the analysis of these networks is to extract the embedded knowledge in the data set and to learn the behavior of users in the social networking environment. One of the most attractive and central applications of social network analysis is link prediction. The purpose of link prediction in social networks is to identify missing and unknown information from users or to predict the future link between two users. In recent years, various artificial intelligence algorithms have been introduced as one of the most important tools for resolving link prediction and big data. In this research, a strategy based on a meta-heuristic is used to improve link prediction in social networks. The proposed method is based on the characteristics of the signed social networks provided and turns the link prediction problem into a two-class classification problem. Then uses the capability of the Particle Swarm Optimization (PSO) and the topological properties of the social network graph to create a database with two classes, the first class pointing to the existence of a connection between the users and the second class pointing to the absence of this relationship. Creates a database using the support vector machine model for categorization work and uses the classic Katz similarity criterion for end-user suggestions. Twitter social network information has been used to compare and evaluate the proposed method. The results of the experiments show the superiority of the proposed method with 0.23, 0.99, and 6.32, respectively, compared to Meta-Path, Katz and CN algorithms in F-measure criterion.","PeriodicalId":170946,"journal":{"name":"International Journal of Cloud Computing and Database Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130499964","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：