• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.317-323
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• 2021

Social media is increasingly becoming a part of our daily life for communicating each other. There are various tools and applications for communication and therefore, identity theft is a common issue among users of such application. A new style of identity theft occurs when cybercriminals break into WhatsApp account, pretend as real friends and demand money or blackmail emotionally. In order to prevent from such issues, data mining can be used for text classification (TC) in analysis authorship attribution (AA) to recognize original sender of the message. Arabic is one of the most spoken languages around the world with different variants. In this research, we built a machine learning model for mining and analyzing the Arabic messages to identify the author of the messages in Saudi dialect. Many points would be addressed regarding authorship attribution mining and analysis: collect Arabic messages in the Saudi dialect, filtration of the messages' tokens. The classification would use a cross-validation technique and different machine-learning algorithms (Naïve Baye, Support Vector Machine). Results of average accuracy for Naïve Baye and Support Vector Machine have been presented and suggestions for future work have been presented.

• 한국컴퓨터정보학회논문지
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• 제27권7호
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• pp.165-175
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• 2022

본 논문의 목표는 버스 정보 모바일의 앱속성(유용성, 디자인, 정보품질, 이동성), 사용자만족, 신뢰, 지속적 사용의도 간의 관계를 확인하는 것이다. 본 연구의 가설을 검정하기 위해 AMOS 21.0의 구조방정식이 사용되었다. 분석의 결과는 다음과 같다. 첫째, 유용성, 디자인, 정보품질 및 이동성이 사용자만족에 긍정적인 영향을 미치는 것으로 나타났다. 둘째, 이동성만이 신뢰에 긍정적인 영향을 미치고 나머지 유용성, 디자인, 정보품질이 유의수준 5%에서 영향을 미치지 않는 것으로 나타났다. 셋째, 사용자만족이 신뢰와 지속적인 사용의도에 긍정적인 영향을 미치는 것으로 나타났다. 넷째, 신뢰가 지속적인 사용의도에 긍정적인 영향을 미치는 것으로 나타났다. 따라서 버스 정보 모바일앱의 특성이 사용자만족, 신뢰, 지속적 사용의도 등의 개선을 위한 중요한 영향요인임이 확되었으며, 특히 이동성이 다른 앱 특성요인보다 중요한 역할을 하고 있다. 지방정부와 버스회사는 버스 정보 모바일앱의 활성화 위한 전략적 방향을 수립할 수 있을 것이다. 본 연구의 한계점은 연구결과를 일반화하는데 다소 부족함이 있어 향후 연구에서는 이 부분을 개선하는데 초점을 둘 필요가 있다.

• 한국IT서비스학회지
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• 제17권2호
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• pp.111-128
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• 2018

About 3.9 million apps and 24 primary categories can be approved on Apple iTunes Store. Making accurate categorization can potentially receive many benefits for developers, app stores, and users, such as improving discoverability and receiving long-term revenue. However, current categorization problems may cause usage inefficiency and confusion, especially for cross-attribution, etc. This study focused on evaluating the reliability of app categorization on Apple iTunes Store by using several rounds of inter-rater reliability statistics, locating categorization problems based on Machine Learning, and making more accurate suggestions about representative functionality stems for each primary category. A mixed methods research was performed and total 4905 popular apps were observed. The original categorization was proved to be substantial reliable but need further improvement. The representative functionality stems for each category were identified. This paper may provide some fusion research experience and methodological suggestions in categorization research field and improve app store's categorization in discoverability.

• 한국정보시스템학회지:정보시스템연구
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• 제27권2호
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• pp.1-28
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• 2018

Purpose More than 7.6 million mobile apps could be approved on both Apple iTunes Store and Google Play. For managing those existed Apps, Apple Inc. established twenty-four primary categories, as well as Google Play had thirty-three primary categories. However, all of their categorizations have appeared more and more problems in managing and classifying numerous apps, such as app miscategorized, cross-attribution problems, lack of categorization keywords index, etc. The purpose of this study focused on introducing individual information cognitive processing as the classification criteria to update the current categorization on Apple iTunes Store. Meanwhile, we tried to observe the effectiveness of the new criteria from a classification process on Apple iTunes Store. Design/Methodology/Approach A research approach with four research stages were performed and a series of mixed methods was developed to identify the feasibility of adopting individual information cognitive processing as categorization criteria. By using machine-learning techniques with Term Frequency-Inverse Document Frequency and Singular Value Decomposition, keyword lists were extracted. By using the prior research results related to car app's categorization, we developed individual information cognitive processing. Further keywords extracting process from the extracted keyword lists was performed. Findings By TF-IDF and SVD, keyword lists from more than five thousand apps were extracted. Furthermore, we developed individual information cognitive processing that included a categorization teaching process and learning process. Three top three keywords for each category were extracted. By comparing the extracted results with prior studies, the inter-rater reliability for two different methods shows significant reliable, which proved the individual information cognitive processing to be reliable as criteria of categorization on Apple iTunes Store. The updating suggestions for Apple iTunes Store were discussed in this paper and the results of this paper may be useful for app store hosts to improve the current categorizations on app stores as well as increasing the efficiency of app discovering and locating process for both app developers and users.