Pub Date : 2022-08-11DOI: 10.1142/s021884302150009x
Yue Wang
{"title":"CNS: Research on data security technology and network data security regulations driven by digital economy","authors":"Yue Wang","doi":"10.1142/s021884302150009x","DOIUrl":"https://doi.org/10.1142/s021884302150009x","url":null,"abstract":"","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45105852","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}
Pub Date : 2022-08-11DOI: 10.1142/s0218843021500106
R. Surender, D. Sathian, P. Dananjayan
{"title":"AHI: Energy Efficiency Enhancement in Massive Mimo Using P-ZF Precoding Scheme With Power Control","authors":"R. Surender, D. Sathian, P. Dananjayan","doi":"10.1142/s0218843021500106","DOIUrl":"https://doi.org/10.1142/s0218843021500106","url":null,"abstract":"","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42090343","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}
Pub Date : 2022-07-13DOI: 10.1142/s0218843021500088
Yanlin Guo
{"title":"CNS: Interactive Intelligent Analysis of Financial Management Software Based on Apriori Data Mining Algorithm","authors":"Yanlin Guo","doi":"10.1142/s0218843021500088","DOIUrl":"https://doi.org/10.1142/s0218843021500088","url":null,"abstract":"","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45518404","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}
Pub Date : 2022-01-01DOI: 10.1007/978-3-031-17834-4
{"title":"Cooperative Information Systems: 28th International Conference, CoopIS 2022, Bozen-Bolzano, Italy, October 4–7, 2022, Proceedings","authors":"","doi":"10.1007/978-3-031-17834-4","DOIUrl":"https://doi.org/10.1007/978-3-031-17834-4","url":null,"abstract":"","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76908827","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}
Pub Date : 2022-01-01DOI: 10.1142/S0218843021500027
R. Anusha, L. Ramaparvathy
{"title":"DRIIS: MapReduce Parameter Optimization of Hadoop Using Genetic Algorithm","authors":"R. Anusha, L. Ramaparvathy","doi":"10.1142/S0218843021500027","DOIUrl":"https://doi.org/10.1142/S0218843021500027","url":null,"abstract":"","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64252967","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}
Pub Date : 2022-01-01DOI: 10.1142/S0218843023500016
Prateek Sikka
{"title":"Hashed Access Policy-Based Secure Data Transmission Using SDHAK-ECC","authors":"Prateek Sikka","doi":"10.1142/S0218843023500016","DOIUrl":"https://doi.org/10.1142/S0218843023500016","url":null,"abstract":"","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64253473","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}
Pub Date : 2022-01-01DOI: 10.1142/S0218843022500058
C. Priya, P. Vincent
{"title":"CNS: Hybrid Explainable Artificial Intelligence-Based Sentiment Analysis on COVID-19 Lockdown Using Twitter Data","authors":"C. Priya, P. Vincent","doi":"10.1142/S0218843022500058","DOIUrl":"https://doi.org/10.1142/S0218843022500058","url":null,"abstract":"","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64253091","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}
Abstract —During the Covid-19 pandemic, all learning activities were carried out online. Many learning media and software are used in online learning. One of them is the Open Source Operating System which is currently widely used in online learning systems. This makes the operating system much attention by the world of education, researchers, government organizations, individuals, even large software companies such as Google and others who are very interested in developing Open Source software, especially the Linux Operating System. This makes the authors interested in knowing how to develop an operating system based on open source software in online learning systems and disseminate the use of open source software to the public.
{"title":"Operating System Development Based on Open Source Software in Online Learning Systems","authors":"A. Prasetya, Muqorobin Muqorobin, Farid Fitriyadi","doi":"10.29040/IJCIS.V2I2.31","DOIUrl":"https://doi.org/10.29040/IJCIS.V2I2.31","url":null,"abstract":"Abstract —During the Covid-19 pandemic, all learning activities were carried out online. Many learning media and software are used in online learning. One of them is the Open Source Operating System which is currently widely used in online learning systems. This makes the operating system much attention by the world of education, researchers, government organizations, individuals, even large software companies such as Google and others who are very interested in developing Open Source software, especially the Linux Operating System. This makes the authors interested in knowing how to develop an operating system based on open source software in online learning systems and disseminate the use of open source software to the public.","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45322481","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}
Abstract — Buying and selling shares is a transaction that is widely carried out at this time, especially buying and selling stocks online which are widely available in the market, to make buying and selling shares require ability or knowledge so that the buying and selling of shares are profitable, to be able to help economic players predict prices. Profit shares or not purchased in the future, this research will conduct stock price predictions using classification methods, namely K-Nearest Neighbor and Naive Bayes, to predict the stock price data used for one month in minute levels totalling 39065 data, based on prediction results. The highest results obtained were using Naive Bayes with an accuracy value of 69.38 then the K-Nearest Neighbor method with a K = 5 value of 67.25%, based on these results it can be concluded that the use of the K-Nearest Neighbor and Naive Bayes methods for prediction share price not yet owned I high accuracy, so it can be combined with other methods or by using other variable predictors.
{"title":"Comparative Analysis of K-NN and Naïve Bayes Methods to Predict Stock Prices","authors":"Budi Soepriyanto","doi":"10.29040/IJCIS.V2I2.32","DOIUrl":"https://doi.org/10.29040/IJCIS.V2I2.32","url":null,"abstract":"Abstract — Buying and selling shares is a transaction that is widely carried out at this time, especially buying and selling stocks online which are widely available in the market, to make buying and selling shares require ability or knowledge so that the buying and selling of shares are profitable, to be able to help economic players predict prices. Profit shares or not purchased in the future, this research will conduct stock price predictions using classification methods, namely K-Nearest Neighbor and Naive Bayes, to predict the stock price data used for one month in minute levels totalling 39065 data, based on prediction results. The highest results obtained were using Naive Bayes with an accuracy value of 69.38 then the K-Nearest Neighbor method with a K = 5 value of 67.25%, based on these results it can be concluded that the use of the K-Nearest Neighbor and Naive Bayes methods for prediction share price not yet owned I high accuracy, so it can be combined with other methods or by using other variable predictors.","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42150488","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}
Abstract — Classification is a technique that many of us encounter in everyday life, classification science is also growing and being applied to various types of data and cases in everyday life, in computer science classification has been developed to facilitate human work, one example of its application is to classify fish species in the world, the number of fish species in the world is very much so that there are still many people who are sometimes confused to distinguish them, therefore in this study a study will be conducted to classify fish species using the K-Nearest Neighbor Method. 4 types of fish, all data totaling 160 data. The purpose of this study was to test the K-Nearest Neighbor method for classifying fish species based on color, texture, and shape features. Based on the test results, the accuracy value of the truth is obtained using the value of K = 7 with a percentage of the truth of 77.50%, the second-highest accuracy value is the value of K = 10, namely 76.88%. Based on the results of this study, it can be concluded that the K-Nearest Neighbor method has a good enough ability to classify, but it can be done by adding variables or adding more amount of data, and using other types of fish.
{"title":"Classification of Fish Species with Image Data Using K-Nearest Neighbor","authors":"Kaharuddin Kaharuddin, E. W. Sholeha","doi":"10.29040/IJCIS.V2I2.33","DOIUrl":"https://doi.org/10.29040/IJCIS.V2I2.33","url":null,"abstract":"Abstract — Classification is a technique that many of us encounter in everyday life, classification science is also growing and being applied to various types of data and cases in everyday life, in computer science classification has been developed to facilitate human work, one example of its application is to classify fish species in the world, the number of fish species in the world is very much so that there are still many people who are sometimes confused to distinguish them, therefore in this study a study will be conducted to classify fish species using the K-Nearest Neighbor Method. 4 types of fish, all data totaling 160 data. The purpose of this study was to test the K-Nearest Neighbor method for classifying fish species based on color, texture, and shape features. Based on the test results, the accuracy value of the truth is obtained using the value of K = 7 with a percentage of the truth of 77.50%, the second-highest accuracy value is the value of K = 10, namely 76.88%. Based on the results of this study, it can be concluded that the K-Nearest Neighbor method has a good enough ability to classify, but it can be done by adding variables or adding more amount of data, and using other types of fish.","PeriodicalId":54966,"journal":{"name":"International Journal of Cooperative Information Systems","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42283014","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}
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