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Article Citation - WoS: 3Citation - Scopus: 9Text Classification Using Word Embedding in Rule-Based Methodologies: A Systematic Mapping(Uikten - Assoc information Communication Technology Education & Science, 2018) Aubaid, Asmaa M.; Mishra, AlokWith the advancing growth of the World Wide Web (WWW) and the expanding availability of electronic text documents, the automatic assignment of text classification (ATC) has become more important in sorting out information and knowledge. One of the most crucial tasks that should be carried out is document representation using word embedding and Rule-Based methodologies. As a result, this, along with their modeling methods, has become an essential step to improve neural language processing for text classification. In this paper, a systematic mapping study is a way to survey all the primary studies on word embedding to rule-based and machine learning of automatic text classification. The search procedure identifies 20 articles as relevant to answer our research questions. This study maps what is currently known about word embedding in rule-based text classification (TC). The result shows that the research is concentrated on some main areas, mainly in social sciences, shopping products classification, digital libraries, and spam filtering. The present paper contributes to the available literature by summarizing all research in the field of TC and it can be beneficial to other researchers and specialists in order to sort information.Conference Object Citation - Scopus: 11The Use of Artificial Neural Networks in Network Intrusion Detection: a Systematic Review(Institute of Electrical and Electronics Engineers Inc., 2019) Öney,M.U.; Peker,S.Network intrusion detection is an important research field and artificial neural networks have become increasingly popular in this subject. Despite this, there is a lack of systematic literature review on that issue. In this manner, the aim of this study to examine the studies concerning the application artificial neural network approaches in network intrusion detection to determine the general trends. For this purpose, the articles published within the last decade from 2008 to 2018 were systematically reviewed and 43 articles were retrieved from commonly used databases by using a search strategy. Then, these selected papers were classified by the publication type, the year of publication, the type of the neural network architectures they employed, and the dataset they used. The results indicate that there is a rising trend in the usage of ANN approaches in the network intrusion detection with the gaining popularity of deep neural networks in recent years. Moreover, the KDD'99 dataset is the most commonly used dataset in the studies of network intrusion detection using ANNs. We hope that this paper provides a roadmap to guide future research on network intrusion detection using ANNs. © 2018 IEEE.Conference Object The Use of Artificial Neural Networks in Network Intrusion Detection: a Systematic Review(Institute of Electrical and Electronics Engineers Inc., 2019) Öney,M.U.; Peker,S.Network intrusion detection is an important research field and artificial neural networks have become increasingly popular in this subject. Despite this, there is a lack of systematic literature review on that issue. In this manner, the aim of this study to examine the studies concerning the application artificial neural network approaches in network intrusion detection to determine the general trends. For this purpose, the articles published within the last decade from 2008 to 2018 were systematically reviewed and 43 articles were retrieved from commonly used databases by using a search strategy. Then, these selected papers were classified by the publication type, the year of publication, the type of the neural network architectures they employed, and the dataset they used. The results indicate that there is a rising trend in the usage of ANN approaches in the network intrusion detection with the gaining popularity of deep neural networks in recent years. Moreover, the KDD'99 dataset is the most commonly used dataset in the studies of network intrusion detection using ANNs. We hope that this paper provides a roadmap to guide future research on network intrusion detection using ANNs. © 2018 IEEE.Article Citation - WoS: 10Citation - Scopus: 15Software Quality Issues in SCRUM: A Systematic Mapping(Graz Univ Technolgoy, inst information Systems Computer Media-iicm, 2018) Mishra, Deepti; Abdalhamid, Samia; Computer EngineeringScrum is a process framework used to develop complex software. As Scrum is one of the prominent approaches in agile development projects, it is significant to define the issues of quality in the Scrum method. In this paper, a systematic mapping approach is adopted to answer specific research questions through an objective procedure to identify the nature of quality issues in Scrum studies. For this purpose, a number of research studies are reviewed in electronic databases to find out about various quality issues related with Scrum. Here, the focus is on how these studies are affective in terms of defining such issues. A total of 53 research papers are examined in detail to answer nine research questions related to quality issues in Scrum. Finally, the responses to all research questions are provided along with suggestions to ensure quality in the Scrum. The results reveal that there is very limited research on people-related quality issues such as employee skills, satisfaction etc. However, process quality such as process effectiveness, conformity, visibility, acceptance etc. have received a lot of attention among researchers, whereas the product quality and project-related quality issues such as team performance, collaboration, etc. are also of interest among researchers.
