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JournalISSN: 1975-9320

Journal of Convergence Information Technology 

Advanced Institute of Convergence Information Technology Research Center
About: Journal of Convergence Information Technology is an academic journal. The journal publishes majorly in the area(s): Convergence (relationship) & Wireless sensor network. It has an ISSN identifier of 1975-9320. Over the lifetime, 3353 publications have been published receiving 11237 citations.


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Journal ArticleDOI
TL;DR: A perfect combination of cloud computing and internet of things can promote fast development of agricultural modernization, realize smart agriculture and effectively solve the issues concerning agriculture, countryside and farmers.
Abstract: Issues concerning agriculture, countryside and farmers have been always hindering China’s development. The only solution to these three problems is agricultural modernization. However, China's agriculture is far from modernized. The introduction of cloud computing and internet of things into agricultural modernization will probably solve the problem. Based on major features of cloud computing and key techniques of internet of things, cloud computing, visualization and SOA technologies can build massive data involved in agricultural production. Internet of things and RFID technologies can help build plant factory and realize automatic control production of agriculture. Cloud computing is closely related to internet of things. A perfect combination of them can promote fast development of agricultural modernization, realize smart agriculture and effectively solve the issues concerning agriculture, countryside and farmers.

248 citations

Journal ArticleDOI
TL;DR: After reviewing Bayesian theory, the naive Bayes classifier and k-nearest neighbor classifier is implemented and applied to a dataset "credit card approval" application and the performance of these two classifiers is observed in terms of the correct classification and misclassification.
Abstract: Probability theory is the framework for making decision under uncertainty. In classification, Bayes' rule is used to calculate the probabilities of the classes and it is a big issue how to classify raw data rationally to minimize expected risk. Bayesian theory can roughly be boiled down to one principle: to see the future, one must look at the past. Naive Bayes classifier is one of the mostly used practical Bayesian learning methods. K-nearest neighbor is a supervised learning algorithm where the result of new instance query is classified based on majority of k-nearest neighbor category. The classifiers do not use any model to fit and only based on memory/training data. In this paper, after reviewing Bayesian theory, the naive Bayes classifier and k-nearest neighbor classifier is implemented and applied to a dataset \"credit card approval\" application. Eventually the performance of these two classifiers is observed on this application in terms of the correct classification and misclassification and how the performance of k-nearest neighbor classifier can be improved by varying the value of k.

123 citations

Journal ArticleDOI
TL;DR: It can be concluded that in the datasets with few numbers of records, the AUC become deviated and the comparison between classifiers may not do correctly and when the number of the records and theNumber of the attributes in each record are increased, the results become more stable.
Abstract: In this paper, the efficacy of seven data classification methods; Decision Tree (DT), k-Nearest Neighbor (k-NN), Logistic Regression (LogR), Naive Bayes (NB), C4.5, Support Vector Machine (SVM) and Linear Classifier (LC) with regard to the Area Under Curve (AUC) metric have been compared. The effects of parameters including size of the dataset, kind of the independent attributes, and the number of the discrete and continuous attributes have been investigated. Based on the results, it can be concluded that in the datasets with few numbers of records, the AUC become deviated and the comparison between classifiers may not do correctly. When the number of the records and the number of the attributes in each record are increased, the results become more stable. Four classifiers DT, k-NN, SVM and C4.5 obtain higher AUC than three classifiers LogR, NB and LC. Among these four classifiers, C4.5 provides higher AUC in the most cases. As a comparison among three classifiers LogR, NB and LC, it can be said that NB provides the best AUC among them and classifiers LogR and NB have the same results, approximately.

113 citations

Journal ArticleDOI
TL;DR: Structural equation analysis results indicate that hedonic motivation, performance expectancy, social influence, and price value positively affect students’ mobile learning adoption.
Abstract: Although mobile learning in higher education has recently received considerable attention, the fact remains that much of the scholarly effort has been limited to the notions of instrumentality. Drawing on UTAUT2 and extant literature on learning behavior, an adoption model that reflects the determinants of undergraduate students’ mobile learning acceptance in a consumer context was developed and empirically tested against data collected from 182 undergraduate students in China. Structural equation analysis results indicate that hedonic motivation, performance expectancy, social influence, and price value positively affect students’ mobile learning adoption. Surprisingly, self-management of learning was found to have both direct and indirect negative influences on undergraduate students’ adoption of mobile learning. Theoretical and practical implications of the findings are discussed.

96 citations

Journal ArticleDOI
TL;DR: An optimization model based on the basic ideal of traditional grey relational analysis (GRA) method is established, by which the attribute weights can be determined, and a relative relational degree is defined to determine the ranking order of all alternatives by calculating the degree of grey relation to both the positive-ideal solution (PIS) and every alternative.
Abstract: The aim of this paper is to investigate the multiple attribute decision making problems with intuitionistic fuzzy information, in which the information about attribute weights is incompletely known, and the attribute values take the form of intuitionistic fuzzy numbers. In order to get the weight vector of the attribute, we establish an optimization model based on the basic ideal of traditional grey relational analysis (GRA) method, by which the attribute weights can be determined. Then, based on the traditional GRA method, calculation steps for solving intuitionistic fuzzy multiple attribute decision-making problems with completely known weight information are given. The degree of grey relation between every alternative and positive ideal solution is calculated. Then, a relative relational degree is defined to determine the ranking order of all alternatives by calculating the degree of grey relation to both the positive-ideal solution (PIS). Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.

84 citations

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Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202199
2020194
2019242
2018156
2017130
201649