• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3219-3237
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• 2019

The Cloud Computing is growing rapidly in the current IT industry. Cloud computing has become a buzzword in relation to Grid & Utility computing. It provides on demand services to customers and customers will pay for what they get. Various "Cloud Service Provider" such as Microsoft Azure, Google Web Services etc. enables the users to access the cloud in cost effective manner. However, security, privacy and integrity of data is a major concern. In this paper various security challenges have been identified and the survey briefs the comprehensive overview of various security issues in cloud computing. The classification of security issues in cloud computing have been studied. In this paper we have discussed security challenges in cloud computing and also list recommended methods available for addressing them in the literature.

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.31-40
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• 2010

In this paper, we propose a service component based on active model for supporting a variety of u-healthcare application services. It implemented that component as a classification of function for developing healthcare application services. Especially we focus on the adaptive information service in integrated environment using a distributed object technologies of the various healthcare home service based on distributed object group framework. And we shows the service component applying to Healthcare application services such as healthcare home monitoring, mobile monitoring and web based monitoring. Also, we show the performance evaluation results such as response time, system load and network load.

• Journal of Internet Computing and Services
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• v.19 no.3
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• pp.35-42
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• 2018

Deep learning is known as a method of high accuracy among several methods for image classification. In this paper, we propose a method of enhancing the accuracy of image classification using CNN with a batch normalization method for classification of images using deep CNN (Convolutional Neural Network). In this paper, we propose a method to add a batch normalization layer to existing neural networks to enhance the accuracy of image classification. Batch normalization is a method to calculate and move the average and variance of each batch for reducing the deflection in each layer. In order to prove the superiority of the proposed method, Accuracy and mAP are measured by image classification experiments using five image data sets SHREC13, MNIST, SVHN, CIFAR-10, and CIFAR-100. Experimental results showed that the CNN with batch normalization is better classification accuracy and mAP rather than using the conventional CNN.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• Journal of Information Technology Applications and Management
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• v.13 no.2
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• pp.55-65
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• 2006

Since the World-Wide Web (WWW) has become a major channel for information delivery, information overload also has become a serious problem to the Internet users. Therefore, effective information searching is critical to the success of Internet services. We present an integrated search engine for searching relevant web pages on the WWW in a certain Internet domain. It supports a local search on the web sites. The spider obtains all of the web pages from the web sites through web links. It operates autonomously without any human supervision. We developed state transition diagram to control navigation and analyze link structure of each web site. We have implemented an integrated local search engine and it shows that a higher satisfaction is obtained. From the user evaluation, we also find that higher precision is obtained.

• Journal of Korea Multimedia Society
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• v.12 no.6
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• pp.867-875
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• 2009

More and richer information sources and services are available on the web everyday. However, harmful information, such as adult content, is not appropriate for all users, notably children. Since internet is a worldwide open network, it has a limit to regulate users providing harmful contents through each countrie's national laws or systems. Additionally it is not a desirable way of developing a certain system-specific classification technology for harmful contents, because internet users can contact with them in diverse ways, for example, porn sites, harmful spams, or peer-to-peer networks, etc. Therefore, it is being emphasized to research and develop context-based core technologies for classifying harmful contents. In this paper, we propose an efficient text filter for blocking harmful texts of web documents using context-based technologies and examine which algorithms for feature selection, the process that select content terms, as features, can be useful for text categorization in all content term occurs in documents, are suitable for classifying harmful contents through implementation and experiment.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.49-56
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• 2011

It is urgently needed to develop programs supporting lifelong education for nurses and students of nursing, which are not restricted by time or space and use personal computers or smartphone. The purpose of this study is to develop CCC(Clinical Care Classification) System into a education program and provides guideline to support clinical tasks for students of nursing. Comparing the search times of the book guideline and the web guideline developed, this study found that it was over 3.5 times faster. And its error rate was over four times lower. This result shows that it can provide accurate intervention for patients since it approaches to intervention and evaluation guideline fast and precisely in the actual tasks of nursing.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.109-118
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• 2007

This paper proposed a method that can be simply analyzed instead of the basic general Fuzzy rule that its insufficient characters are cut out. Based on the proposed method. Rough sets are used to eliminate the incomplete attributes included in the rule and also for a classification more precise; the agreement of the membership function's output extracted the maximum attributes. Besides, the proposed method in the simulation shows that in order to verify the validity, compare the max-product result of fuzzy before and after reducing rule hosed on the rice taste data; then, we can see that both the max-product result of fuzzy before and after reducing rule are exactly the same; for a verification more objective, we compared the defuzzificated real number section.

• Journal of Internet Computing and Services
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• v.7 no.6
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• pp.1-10
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• 2006

A software development using components reuse is an useful method to reduce the software development cost. But a retrieval method by the keyword and category classifications is difficult to search an exact matching component due to components complexity in component reuse. Therefore, after different existing methods are examined and analyzed, an effective classification and retrieval method using XML specifications and the system architecture of components integrated management based on it are presented. Many discording elements of DTD which is component meta-expression exist in components retrieval. To compensate it, this retrieval method using estimations of precision and concision is effective one to catch considerable matching preference components. This method makes possible to retrieve suitable components having better priority due to searching similar matching components that are difficult in an existing keyword matching method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.27-35
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• 2013

Recently, as Social Network Services(SNS) are becoming more popular, much research has been doing on analyzing public opinions from SNS. One of the most important tasks for solving such a problem is to separate opinion(subjective) documents from others(e.g. objective documents) in SNS. In this paper, we propose a new method of retrieving the opinion documents from Twitter. The reason why it is not easy to search or classify the opinion documents in Twitter is due to a lack of publicly available Twitter documents for training. To tackle the problem, at first, we build a machine-learned model for sentiment classification using the external documents similar to Twitter, and then modify the model to separate the opinion documents from Twitter. Experimental results show that proposed method can be applied successfully in opinion classification.