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Moawia Eldow

Title: Clinical Associate Professor

Department: Computer Science and Engineering

College: College of Engineering

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, Universiti Putra Malaysia, 2000
    Major: Artificial Intelligence
    Dissertation: An Approach to the Development of Hybrid Architecture of Expert Systems
  • MSc, University of Khartoum, 1995
    Major: Computer Science
  • BS, University of Khartoum, 1989
    Major: Computer Science & Statistics

Current Scheduled Teaching

CSCE 3201.401Applied Artificial IntelligenceFall 2024 Syllabus
CSCE 5210.004Fundamentals of Artificial IntelligenceFall 2024
CSCE 4010.001Social Issues in ComputingFall 2024 Syllabus
CSCE 4010.003Social Issues in ComputingFall 2024 Syllabus

Previous Scheduled Teaching

CSCE 5210.002Fundamentals of Artificial IntelligenceSummer 10W 2024 SPOT
CSCE 5210.003Fundamentals of Artificial IntelligenceSummer 10W 2024 SPOT
CSCE 5150.003Analysis of Computer AlgorithmsSpring 2024 SPOT
CSCE 4380.001Data MiningSpring 2024 Syllabus SPOT
CSCE 5380.001Data MiningSpring 2024 SPOT
CSCE 5380.002Data MiningSpring 2024 SPOT
CSCE 5380.600Data MiningSpring 2024 SPOT
CSCE 3201.001Applied Artificial IntelligenceFall 2023 Syllabus SPOT
CSCE 5210.004Fundamentals of Artificial IntelligenceFall 2023 SPOT
CSCE 4010.002Social Issues in ComputingFall 2023 Syllabus SPOT
CSCE 4010.003Social Issues in ComputingFall 2023 Syllabus SPOT
CSCE 5210.002Fundamentals of Artificial IntelligenceSummer 10W 2023 SPOT
CSCE 5210.003Fundamentals of Artificial IntelligenceSummer 10W 2023 SPOT
CSCE 5150.003Analysis of Computer AlgorithmsSpring 2023 SPOT
CSCE 3201.001Applied Artificial IntelligenceSpring 2023 Syllabus SPOT
CSCE 4380.001Data MiningSpring 2023 Syllabus SPOT
CSCE 5380.001Data MiningSpring 2023 SPOT
CSCE 5380.002Data MiningSpring 2023 SPOT
CSCE 4110.001AlgorithmsFall 2022 Syllabus SPOT
CSCE 4110.002AlgorithmsFall 2022 Syllabus SPOT
CSCE 5150.005Analysis of Computer AlgorithmsFall 2022 SPOT
CSCE 5150.006Analysis of Computer AlgorithmsFall 2022 SPOT
CSCE 5934.833Directed StudyFall 2022 Syllabus
CSCE 5210.001Fundamentals of Artificial IntelligenceSummer 8W1 2022 Syllabus SPOT
CSCE 5210.002Fundamentals of Artificial IntelligenceSummer 8W2 2022 Syllabus SPOT
CSCE 5150.001Analysis of Computer AlgorithmsSpring 2022 Syllabus SPOT
CSCE 5150.003Analysis of Computer AlgorithmsSpring 2022 Syllabus SPOT
CSCE 4380.001Data MiningSpring 2022 Syllabus SPOT
CSCE 5380.001Data MiningSpring 2022 Syllabus SPOT
CSCE 5380.002Data MiningSpring 2022 Syllabus SPOT

Published Intellectual Contributions

    Abstracts and Proceedings

  • Eldow, M., Dawod, A. (2022). Gold Detection Using Remote Sensing and Artificial Neural Network Techniques. Proceedings of the 10th International Conference on Appropriate Technology. 10 527. https://appropriatetech.net/index.php/10th-icat-2022
  • Book Chapter

  • Eldow, M. (2021). The Worldwide Tools and Methods of Artificial Intelligence for Detection and Diagnosis of COVID-19. Leveraging Artificial Intelligence for Global Epidemics. 1 (1) 181-201. Elsevier Book. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8342405/
  • Journal Article

  • Eldow, M., Tayfour, A.E., Mohammed, A. (2021). A Comparison of the Performance of Artificial Neural Network Algorithms in Facial Expression Recognition. Current Approaches in Science and Technology Research. 12 1-11. B P International. https://stm.bookpi.org/CASTR-V12/article/view/2593
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Overall
Summative Rating
Challenge and
Engagement Index
Response Rate

out of 5

out of 7
%
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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