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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Yassen AbdelKhaleq Najm
Al-Iraqia University

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Document classification using term frequency-inverse document frequency and K-means clustering Wasseem N. Ibrahem Al-Obaydy; Hala A. Hashim; Yassen AbdelKhaleq Najm; Ahmed Adeeb Jalal
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1517-1524

Abstract

Increased advancement in a variety of study subjects and information technologies, has increased the number of published research articles. However, researchers are facing difficulties and devote a significant time amount in locating scientific research publications relevant to their domain of expertise. In this article, an approach of document classification is presented to cluster the text documents of research articles into expressive groups that encompass a similar scientific field. The main focus and scopes of target groups were adopted in designing the proposed method, each group include several topics. The word tokens were separately extracted from topics related to a single group. The repeated appearance of word tokens in a document has an impact on the document's weight, which is computed using the term frequency-inverse document frequency (TF-IDF) numerical statistic. To perform the categorization process, the proposed approach employs the paper's title, abstract, and keywords, as well as the categories' topics. We exploited the K-means clustering algorithm for classifying and clustering the documents into primary categories. The K-means algorithm uses category weights to initialize the cluster centers (or centroids). Experimental results have shown that the suggested technique outperforms the k-nearest neighbors algorithm in terms of accuracy in retrieving information.
Cloud computing security for e-learning during COVID-19 pandemic Yassen AbdelKhaleq Najm; Suray Alsamaraee; Ahmed Adeeb Jalal
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1610-1618

Abstract

The demand for e-learning services increased during the developments of the COVID-19 virus and its rapid spread, and the recommendations of the World Health Organization (WHO) that social distancing should be required. The rapid transition to the e-learning environment quickly led to the neglect of some security aspects, which led to an increase in cyber attacks targeting computer accounts, which is one of the most important pillars of e-learning. In these papers, the attacks that target the cloud computer used in the most important e-learning have been studied and classified according to the victim using an inductive methodology based on global statistics related to cyber attacks and recent research. And suggest appropriate solutions to avoid its occurrence in the near future and raise the level of protection for those computer clouds.
Co-Authors Ahmed Adeeb Jalal Hala A. Hashim Suray Alsamaraee Wasseem N. Ibrahem Al-Obaydy