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A great deal of attention has been given to deep learning over the past several years, and new deep learning techniques are emerging with improved functionality. Many computer and network applications actively utilize such deep learning algorithms and report enhanced performance through them. In this study, we present an overview of deep learning methodologies, including restricted Bolzmann machine-based...
Identifying anomalous events in the network is one of the vital functions in enterprises, ISPs, and datacenters to protect the internal resources. With its importance, there has been a substantial body of work for network anomaly detection using supervised and unsupervised machine learning techniques with their own strengths and weaknesses. In this work, we take advantage of the both worlds of unsupervised...
This paper proposes to establish a framework for use in the earliest stages of a software project for comparing the various types of software development, thus allowing this information to be part of the project selection process. The possible types of software development projects are classified into three categories: new development, reuse-based without modification, and reuse-based with modification...
Previous research proposed a framework for the objective and accurate estimation of software project schedules in the proposal preparation stage of large-sized software development projects. A probabilistic approach was used to take into account the uncertainty inherent in the early stage of such projects. This work illustrates how to use the framework in the later planning stage when additional,...
This paper mainly focuses on the technical factors of the website targeted at shopping mall websites in Korea and China. There are a total of six technical factors, and we conducted an empirical study on how those six factors are different based on the shopping mall websites of two different countries. Statistical data for an empirical study targeted at undergraduate students in Korea and China, and...
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