Abdulhussain E. Mahdi
University of Limerick
85 Papers
404 Citations
Abdulhussain E. Mahdi is an academic researcher from University of Limerick. The author has contributed to research in topics: Wavelet packet decomposition & Study skills. The author has an hindex of 15, co-authored 85 publications. Previous affiliations of Abdulhussain E. Mahdi include University of Southampton & University of Plymouth.
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Papers
An unsupervised approach to automatic classification of scientific literature utilizing bibliographic metadata
TL;DR: An unsupervised approach for automatic classification of scientific literature archived in digital libraries and repositories according to a standard library classification scheme based on identifying all the references cited in the document to be classified.
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Introducing Peer-Supported Learning Approach to Tutoring in Engineering and Technology Courses:
TL;DR: An innovative, non-traditional tutoring programme based on collaborative and peer-support learning is described, and a reflection on two years of its implementation to specific subjects in electronic engineering and ICT-based courses at the University of Limerick is presented.
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A new text representation scheme combining Bag-of-Words and Bag-of-Concepts approaches for automatic text classification
Alaa Alahmadi,Arash Joorabchi,Abdulhussain E. Mahdi +2 more
- 01 Nov 2013
TL;DR: This paper introduces a new approach to creating text representations and apply it to a standard text classification collections based on supplementing the well-known Bag-of-Words representational scheme with a concept-based representation that utilises Wikipedia as a knowledge base.
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Text mining stackoverflow: An insight into challenges and subject-related difficulties faced by computer science learners
TL;DR: The authors have been able to rank identified topics and categories according to their frequencies, and therefore, mark the most asked about subjects and, hence, identify the most difficult and challenging topics commonly faced by learners of computer programming and software development.
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Automatic mapping of user tags to Wikipedia concepts
TL;DR: A machine learning-based method capable of automatic mapping of user tags to their equivalent Wikipedia concepts is proposed and its performance is evaluated using the currently most popular computer programming Q&A website, StackOverflow.com, as a test platform.
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