J
Jalal S. Alowibdi
Researcher at Information Technology University
Publications - 41
Citations - 933
Jalal S. Alowibdi is an academic researcher from Information Technology University. The author has contributed to research in topics: Analytics & Social network analysis. The author has an hindex of 13, co-authored 41 publications receiving 668 citations. Previous affiliations of Jalal S. Alowibdi include King Abdulaziz University & IT University.
Papers
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Proceedings ArticleDOI
Predicting Student Performance using Advanced Learning Analytics
Ali Daud,Naif Radi Aljohani,Rabeeh Ayaz Abbasi,Miltiadis D. Lytras,Farhat Abbas,Jalal S. Alowibdi +5 more
TL;DR: Experimental results show that proposed EDM/LA method significantly outperforms existing methods due to exploitation of family expenditures and students' personal information feature sets.
Proceedings ArticleDOI
Language independent gender classification on Twitter
TL;DR: The approach is independent of the user's language, efficient, and scalable, while attaining a good level of accuracy, and proves the validity of the approach by examining different classifiers over a large dataset of Twitter profiles.
Proceedings ArticleDOI
Empirical Evaluation of Profile Characteristics for Gender Classification on Twitter
TL;DR: This work explores profile characteristics for gender classification on Twitter and provides a novel technique to reduce the number of features of text-based profile characteristics from the order of millions to a few thousands and, in some cases, to only 40 features.
Journal ArticleDOI
Saving lives using social media: Analysis of the role of twitter for personal blood donation requests and dissemination
Rabeeh Ayaz Abbasi,Rabeeh Ayaz Abbasi,Onaiza Maqbool,Mubashar Mushtaq,Naif Radi Aljohani,Ali Daud,Ali Daud,Jalal S. Alowibdi,Basit Shahzad +8 more
TL;DR: This study studies the request and dissemination behavior of people using social media to fulfill blood donation requests in India, and identifies areas where future social media enabled automated healthcare systems can focus on the needs of individual patients.
Journal ArticleDOI
Modelling to identify influential bloggers in the blogosphere
Hikmat Ullah Khan,Ali Daud,Umer Ishfaq,Tehmina Amjad,Naif Radi Aljohani,Rabeeh Ayyaz Abbasi,Jalal S. Alowibdi +6 more
TL;DR: This paper reviews the models proposed to find the most influential users in the blogging community, and classification of finding influential bloggers models into feature-based and network-based categories.