Faculty Profile

Mohammed Aledhari

Title
Assistant Professor
Department
Information Science
College
College of Information

    

    

      


                          

            
Curriculum Vitae

            

              Click Here to View Faculty CV
            

          

        
                          

            
Education

            



  

  


  
PhD, Western Michigan University, 2017. 
Major: Computer Science
Degree Specialization: AI/ML
Dissertation Title: A Deep Learning-Based Data Minimization Algorithm for Big Genomics Data in Support of loT and Secure Smart Health Services





          

        
                

          
Current Scheduled Teaching*


          
DTSC 5505.002, Applied Machine Learning for Data Scientists, Summer 2024 
INFO 5505.001, Applied Machine Learning for Data Scientists, Spring 2024 
INFO 6900.724, Special Problems, Spring 2024 


          
*
  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*


            
                    

                    INFO 5505.001, Applied Machine Learning for Data Scientists, Fall 2023  SPOT
                    

                    INFO 5505.002, Applied Machine Learning for Data Scientists, Summer 10W 2023  SPOT
                    

                    INFO 5505.001, Applied Machine Learning for Data Scientists, Spring 2023 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


  
Conference Proceeding
Aledhari, M., Razzak, R., Parizi, R. M., Srivastava, G.  (2021). Multimodal Machine Learning for Pedestrian Detection. IEEE Vehicular Technology Conference. 2021-April, . https://api.elsevier.com/content/abstract/scopus_id/85112406615
Aledhari, M., Razzak, R., Parizi, R. M., Srivastava, G.  (2021). Sensor Fusion for Drone Detection. IEEE Vehicular Technology Conference. 2021-April, . https://api.elsevier.com/content/abstract/scopus_id/85112401473
Aledhari, M., Razzak, R., Parizi, R. M., Srivastava, G.  (2021). Deep Neural Networks for Detecting Real Emotions Using Biofeedback and Voice. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 12664 LNCS, 302-309. https://api.elsevier.com/content/abstract/scopus_id/85103460753
Aledhari, M., Razzak, R.  (2020). An Adaptive Segmentation Technique to Detect Brain Tumors Using 2D Unet. Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. 2328-2334. https://api.elsevier.com/content/abstract/scopus_id/85100332648
Aledhari, M., Razzak, R., Parizi, R. M., Dehghantanha, A.  (2020). A Deep Recurrent Neural Network to Support Guidelines and Decision Making of Social Distancing. Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. 4233-4240. https://api.elsevier.com/content/abstract/scopus_id/85103829199
Aledhari, M., Parizi, R. M., Dehghantanha, A., Choo, K. K. (2019). A Hybrid RSA algorithm in support of IoT greenhouse applications. Proceedings - IEEE International Conference on Industrial Internet Cloud, ICII 2019. 233-240. https://api.elsevier.com/content/abstract/scopus_id/85085936644
Aledhari, M., Joji, S., Hefeida, M., Saeed, F.  (2019). Optimized CNN-based Diagnosis System to Detect the Pneumonia from Chest Radiographs. Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. 2405-2412. https://api.elsevier.com/content/abstract/scopus_id/85084331555
Aledhari, M., Di Pierro, M., Saeed, F.  (2018). A Fourier-Based Data Minimization Algorithm for Fast and Secure Transfer of Big Genomic Datasets. Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress 2018 - Part of the 2018 IEEE World Congress on Services. 128-134. https://api.elsevier.com/content/abstract/scopus_id/85057766459
Aledhari, M., Marhoon, A., Hamad, A., Saeed, F.  (2017). A New Cryptography Algorithm to Protect Cloud-Based Healthcare Services. Proceedings - 2017 IEEE 2nd International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2017. 37-43. https://api.elsevier.com/content/abstract/scopus_id/85029361992
Aledhari, M., Hefeida, M. S., Saeed, F.  (2016). A variable-length network encoding protocol for big genomic data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9674, 212-224. https://api.elsevier.com/content/abstract/scopus_id/84978968786
Aledhari, M., Saeed, F.  (2015). Design and Implementation of Network Transfer Protocol for Big Genomic Data. Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015. 281-288. https://api.elsevier.com/content/abstract/scopus_id/84959452732
Journal Article
Aledhari, M.  (2024). Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques. Internet of Things. No. IEEE. 
Aledhari, M., Razzak, R., Qolomany, B., Al-Fuqaha, A., Saeed, F.  (2022). Biomedical IoT: Enabling Technologies, Architectural Elements, Challenges, and Future Directions. IEEE Access. 10, 31306-31339. https://api.elsevier.com/content/abstract/scopus_id/85126555606
HaddadPajouh, H., Dehghantanha, A., M. Parizi, R., Aledhari, M., Karimipour, H.  (2021). A survey on internet of things security: Requirements, challenges, and solutions. Other. 14, . https://api.elsevier.com/content/abstract/scopus_id/85115019256
Aledhari, M., Razzak, R., Parizi, R. M. (2021). Machine learning for network application security: Empirical evaluation and optimization. Other. 91, . https://api.elsevier.com/content/abstract/scopus_id/85101838345
Shahid, O., Nasajpour, M., Pouriyeh, S., Parizi, R. M., Han, M., Valero, M., Li, F., Aledhari, M., Sheng, Q. Z. (2021). Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance. Other. 117, . https://api.elsevier.com/content/abstract/scopus_id/85104356194
Yazdinejad, A., Parizi, R. M., Dehghantanha, A., Karimipour, H., Srivastava, G., Aledhari, M.  (2021). Enabling Drones in the Internet of Things with Decentralized Blockchain-Based Security. Other. 8(8), 6406-6415. https://api.elsevier.com/content/abstract/scopus_id/85097148493
Tariq, M. U., Haseeb, M., Aledhari, M., Razzak, R., Parizi, R. M., Saeed, F.  (2021). Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey. IEEE Access. 9, 5497-5516. https://api.elsevier.com/content/abstract/scopus_id/85098771417
Yazdinejad, A., Srivastava, G., Parizi, R. M., Dehghantanha, A., Choo, K. K., Aledhari, M.  (2020). Decentralized authentication of distributed patients in hospital networks using blockchain. Other. 24(8), 2146-2156. https://api.elsevier.com/content/abstract/scopus_id/85085736060
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.  (2015). Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. Other. 17(4), 2347-2376. https://api.elsevier.com/content/abstract/scopus_id/84941085241
Review, Journal
Aledhari, M., Razzak, R., Parizi, R. M., Saeed, F.  (2020). Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Access. 8, 140699-140725. https://api.elsevier.com/content/abstract/scopus_id/85089512304



	
		
		
	  

	

	

  
    
	

	

	  

		

		   	
		
		
	  

	

	

	
	

  
  
  

  	

		

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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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