Faculty Profile

Moawia Eldow

Title
Clinical Associate Professor
Department
Computer Science and Engineering
College
College of Engineering

    

    

      


                          

            
Curriculum Vitae

            

              Click Here to View Faculty CV
            

          

        
                          

            
Education

            



  

  


  
PhD, Universiti Putra Malaysia, 2000. 
Major: Artificial Intelligence
Dissertation Title: 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 3201.001, Applied Artificial Intelligence, Summer 2024 
CSCE 5210.002, Fundamentals of Artificial Intelligence, Summer 2024 
CSCE 5210.003, Fundamentals of Artificial Intelligence, Summer 2024 
CSCE 5150.003, Analysis of Computer Algorithms, Spring 2024 Syllabus
CSCE 4380.001, Data Mining, Spring 2024 Syllabus
CSCE 5380.001, Data Mining, Spring 2024 Syllabus
CSCE 5380.002, Data Mining, Spring 2024 Syllabus
CSCE 5380.600, Data Mining, Spring 2024 Syllabus


          
*
  Texas Education Code 51.974 (HB 2504) requires each institution of
  higher education to make available to the public, a syllabus for
  undergraduate lecture courses offered for credit by the institution.
  

        

      

    

  


    

    

      


        


          


            
Previous Scheduled Teaching*


            
                    

                    CSCE 3201.001, Applied Artificial Intelligence, Fall 2023 Syllabus SPOT
                    

                    CSCE 5210.004, Fundamentals of Artificial Intelligence, Fall 2023 Syllabus SPOT
                    

                    CSCE 4010.002, Social Issues in Computing, Fall 2023 Syllabus SPOT
                    

                    CSCE 4010.003, Social Issues in Computing, Fall 2023 Syllabus SPOT
                    

                    CSCE 5210.002, Fundamentals of Artificial Intelligence, Summer 10W 2023 Syllabus SPOT
                    

                    CSCE 5210.003, Fundamentals of Artificial Intelligence, Summer 10W 2023 Syllabus SPOT
                    

                    CSCE 5150.003, Analysis of Computer Algorithms, Spring 2023 Syllabus SPOT
                    

                    CSCE 3201.001, Applied Artificial Intelligence, Spring 2023 Syllabus SPOT
                    

                    CSCE 4380.001, Data Mining, Spring 2023 Syllabus SPOT
                    

                    CSCE 5380.001, Data Mining, Spring 2023 Syllabus SPOT
                    

                    CSCE 5380.002, Data Mining, Spring 2023 Syllabus SPOT
                    

                    CSCE 4110.001, Algorithms, Fall 2022 Syllabus SPOT
                    

                    CSCE 4110.002, Algorithms, Fall 2022 Syllabus SPOT
                    

                    CSCE 5150.005, Analysis of Computer Algorithms, Fall 2022 Syllabus SPOT
                    

                    CSCE 5150.006, Analysis of Computer Algorithms, Fall 2022 Syllabus SPOT
                    

                    CSCE 5934.833, Directed Study, Fall 2022 Syllabus
                    

                    CSCE 5210.001, Fundamentals of Artificial Intelligence, Summer 8W1 2022 Syllabus SPOT
                    

                    CSCE 5210.002, Fundamentals of Artificial Intelligence, Summer 8W2 2022 Syllabus SPOT
                    

                    CSCE 5150.001, Analysis of Computer Algorithms, Spring 2022 Syllabus SPOT
                    

                    CSCE 5150.003, Analysis of Computer Algorithms, Spring 2022 Syllabus SPOT
                    

                    CSCE 4380.001, Data Mining, Spring 2022 Syllabus SPOT
                    

                    CSCE 5380.001, Data Mining, Spring 2022 Syllabus SPOT
                    

                    CSCE 5380.002, Data Mining, Spring 2022 Syllabus SPOT

          


          
*
  Texas Education Code 51.974 (HB 2504) requires each institution of
  higher education to make available to the public, a syllabus for
  undergraduate lecture courses offered for credit by the institution.
  

        

      

    

  

  
  	

	

	  

		

		   
Published Publications



  

  
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



	
		
		
	  

	

	

  
    
	

	

	  

		

		   	
		
		
	  

	

	

	
	

  
  
  

  	

		

		,  
			
				

					
					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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