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Dr. Moawia Eldow (USA)

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Description

Dr. Moawia Elfaki Yahia Eldow received his BSc in Computer science & Statistics and MSc in Computer Science from University of Khartoum, in 1989 and 1996, and his PhD degree in Artificial Intelligence from Universiti Putra Malaysia in 2000. He has experience of over 25 years for working in academia, research and industry.

In academia, he worked at various universities in different countries with different capacities in academic bodies and professional organizations, and taught over 20 different courses in computing disciplines. In research, Dr. Eldow worked in over 10 research projects and published over 40 articles in international journals and proceedings in the fields of Artificial Intelligence, Machine Learning, Natural Language Processing and Data Mining. In industry, he worked as Software Engineer and IT consultant for many organizations and supervised establishing of many management information systems.

Currently, Dr. Eldow is working as a Clinical Associate Professor at Computer Science and Engineering Department, University of North Texas, Denton, Texas, USA. His research interests are Artificial Intelligence, Hybrid Intelligent Systems, Data Mining, Machine Learning, Deep Learning, Big Data Analytics, Data Science, Large-Scale Data Mining, Text Mining, Natural Language Processing, and Information Retrieval.

Location

Denton County Courthouse, 110 W Hickory St, Denton, Texas 76201, United States

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Languges
  • Arabic
  • English
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  • SDG11
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Subjects
  • Digital Transformation
  • Artificial Intelligence
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  • BSc
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Research Interest/Publications

Sample publications:

Journal & Chapter Publications:

  1. E. Yahia, R. Mahmod, N. Suliman, F. Ahmed, Rough Neural Expert Systems, Int. Journal of Expert Systems with Applications, vol. 18, no. 2, 87-99 (2000).
  2. Moawia Elfaki Yahia, Nasrin Dalil Arabi, Rough Set Analysis for Sudan School Certificate, in Rough Sets and Knowledge Technology, Lecture notes in Computer Science, edited by Paul Wen and Yuefeng Li, Springer-Verlag, Volume 5589, 626-633 (2009).
  3. Moawia Elfaki Yahia, Murtada Elmukashfi Eltaher, A New Approach for Evaluation of Data Mining Techniques, International Journal of Computer Science Issues, Vol. 7, Issue 5, 181-186 (Sep/ 2010).
  4. Azhri Gismalla & Moawia Yahia, Toward An Intelligent Requirements Tool for Modeling Use Cases and Domains in Object-Oriented Software Processes, International Journal of Artificial Intelligence and Machine Learning, Vol. 12, No. 1, 21-27 (July/2012).
  5. Amira Elsir Tayfour, Altahir Mohammed, Moawia E. Eldow, Performance comparisons of artificial neural network algorithms in facial expression recognition, International Journal of Engineering & Technology, Vol. 4, No. 4, 465-471 (2015).
  6. Moawia Eldow, The Worldwide Tools and Methods of Artificial Intelligence for Detection and Diagnosis of COVID-19, in Le Gruenwald (et al), Leveraging Artificial Intelligence for Global Epidemics, Elsevier Book, Vol. 1, pp: 181-201 (Aug/2021).
  7. Tayfour, A. E., Mohammed, A., & Eldow, M. E. A Comparison of the Performance of Artificial Neural Network Algorithms in Facial Expression Recognition. In Current Approaches in Science and Technology Research Vol. 12, 1–11 (2021).

Proceedings publications:

  1. Moawia Elfaki Yahia, Ramlan Mahmod, Neural Expert System with Two Engine of Rough Sets, in ACS / IEEE International Conference in Computer Systems and Applications (AICCSA'01), Beirut, Lebanon, 52-58 (2001).
  2. Moawia Elfaki Yahia, Badria Abakar, K-Nearest Neighbor and C4.5 Algorithms as Data Mining Methods: Advantages and Difficulties, ACS/IEEE International Conference in Computer Systems and Applications(AICCSA'03), Tunisia, (July/2003).
  3. E. Yahia, M. E. Saeed, A. M. Salih, An Intelligent Algorithm For Arabic Soundex Function Using Intuitionist Fuzzy Logic, 3rd International IEEE Conference on Intelligent Systems, 711-715, (Sept/2006).
  4. Salma Mahgoub Gaffer, Moawia Elfaki Yahia, Khaled Ragab, Genetic Fuzzy System for Intrusion Detection : Analysis of Improving of multiclass classification accuracy using KDDCup-99 imbalance dataset, Conf. of Hybrid Intelligent Systems, pp. 318 – 323, (Dec/2012).
  5. Mahgoub Hammad, Alsadig Mohammed, Moawia E. Eldow, Design an Electronic System Use the Audio Fingerprint to Access Virtual Classroom Using Artificial Neural Networks, IEEE-International Conference on Computer, Communication, and Control Technology (I4CT), pp. 192-195, (April/2015).
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