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Elfadil Abdalla Mohamed Abdalla
- Associate Professor
- Ext: 6041
Dr. Elfadil Abdalla Mohamed holds an MSc and Ph.D. in Computer Science from the University of Technology Malaysia (UTM), Malaysia, awarded in 2002. He is currently an Associate Professor at the College of Information Technology, Ajman University, United Arab Emirates. Dr. Mohamed's research interests are primarily focused on data mining and database systems, with a strong emphasis on exploring innovative approaches in these areas.
Education
- Ph.D. in Computer Science, Faculty of Computer Science and Information Systems, University of Technology Malaysia, 2002, Malaysia.
- M.Sc. in Computer Science, Faculty of Computer Science and Information Systems, University of Technology Malaysia, 1997.
- B.Sc. (Honours) in Computer Science and Statistics, School of Mathematical Sciences, University of Khartoum, 1992, Khartoum, Sudan. Graduation Degree: Second Class Division One.
Ph.D. in Computer Science, Faculty of Computer Science and Information Systems, University of Technology Malaysia, 2002, Malaysia.
M.Sc. in Computer Science, Faculty of Computer Science and Information Systems, University of Technology Malaysia, 1997.
B.Sc. (Honours) in Computer Science and Statistics, School of Mathematical Sciences, University of Khartoum, 1992, Khartoum, Sudan. Graduation Degree: Second Class Division One.
Experience
- Associate Professor at the College of Engineering and Information Technology, Ajman University, UAE, from 6th September 2017 - Present.
- Assistant Professor at the Department of Management Information Systems, College of Business Administration, Al Ain University of Science and Technology, UAE, from February 2010 - 4th September 2017.
- Assistant Professor at College of Engineering and Information Technology, Ajman University, UAE, September 2002 - 31 January 2010.
Associate Professor at the College of Engineering and Information Technology, Ajman University, UAE, from 6th September 2017 - Present.
Assistant Professor at the Department of Management Information Systems, College of Business Administration, Al Ain University of Science and Technology, UAE, from February 2010 - 4th September 2017.
Assistant Professor at College of Engineering and Information Technology, Ajman University, UAE, September 2002 - 31 January 2010.
Teaching Area
- Database, Information Systems, Programming, Data Mining and Data Warehousing, Knowledge Management, Change Management.
Database, Information Systems, Programming, Data Mining and Data Warehousing, Knowledge Management, Change Management.
Research
- Data mining
- Data analysis
- Database
Data mining
Data analysis
Database
Publications
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1. Shorouq Eletter, Nasser Taleb, Elfadil A. Mohamed, Loay Alnaji (2017). “Exploring interns’ satisfaction: A case study of United Arab Emirates.” International Journal of Applied Business and Economic Research, Volume 15, Issue 22, 2017, Pages 547-560
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2. Elfadil A. Mohamed and Nasser Taleb (2017). “Framework for the Applications of Data Mining Technique in Viral Marketing Using Social Network.” 30th B&ESI CONFERENCE Abu Dhabi, UAE, January 8-11, 2017
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4. Nayla Alkhatri , Nazar Zaki, Elfadil Mohammed, Musa Shallal (2016). “Data Mining Techniques to Predict the Ranking of E- Government Services.” 2016 12th International Conference on Innovations in Information Technology (IIT), IEEE Xplore Digital Library, Pages: 1 - 6, DOI: 10.1109/INNOVATIONS.2016.7880047
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5. Alramzana Nujum Navaz, Elfadil Mohammed, Mohamed Adel Serhani, Nazar Zaki. (2016). “The Use of Data Mining Techniques to Predict Mortality and Length of Stay in an ICU.” 2016 12th International Conference on Innovations in Information Technology (IIT), IEEE Xplore Digital Library, Pages: 1 - 5, DOI: 10.1109/INNOVATIONS.2016.7880045 year: 2016
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6. Noura AlDarmaki, Elfadil A. Mohamed , Noura AlMansouri, Ibrahim Elsiddig Ahmed, Nazar Zaki (2016). “Prediction of the Closing Price in the Dubai Financial Market: A Data Mining Approach.” 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC), IEEE Xplore Digital Library, Pages: 1 – 7, year: 2016
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7. Elfadil A. Mohamed, Nasser Taleb, and Raed Ahmad Tawfeq Said (2016). “Evaluating the effectiveness of e-course delivery and e-assessment using Course Management System.” International Conference for Academic Disciplines, 31-January-2016, Al Ain, UAE
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8. Nazar Zaki and Elfadil A. Mohamed (2016). “A Combination of Protein-Protein Interaction Network Topological and Biological Process Features for Multiprotein Complex Detection.” Book chapter in a book titled “Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology Systems and Applications”. Publisher: Elsevier Morgan Kaufman. page 305-315 , March, 22, 2016
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9. Nasser Taleb and Elfadil Abdalla (2015). “Framework for Evaluating the Effectiveness of Data Mining in the Analysis of Dynamic Data for Supporting Financial Decision-Making,” 2015 Global Business & Economics Anthology, Vol. 1, USA. The paper also appeared in the Proceeding of the 26th B&ESI CONFERENCE Al Ain, UAE, March 1-4, 2015
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10. Nazar Zaki, Elfadil A. Mohamed, and Antonio Mora (2015). “Characterization of Protein Complexes and Sub-complexes in Protein-protein Interaction Databases.” Journal of Biochemistry Research International. Volume 2015, Article ID 245075, USA
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11. Elfadil A. Mohamed (2014). “Predicting Causes of Traffic Road Accidents Using Multi-class Support Vector Machines.” Journal of Communication and Computer, Vol. 11, Number 5, 2014, USA. The paper also appeared in the Proceeding of the 10th International Conference on Data Mining, July 23-24, 2014, Las Vegas, Nevada, USA
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12. Elfadil A. Mohamed, F. H. Saad, and Omer I. E. Mohamed (2014). “Effects of Classification Techniques on Medical Reports Classification.” International Journal of Computers & Technology. Volume 13, No 2.
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13. Omer I. E. Mohamed, F. H. Saad, and Elfadil A. Mohamed (2012). “Evaluation of Partitional Algorithms for Clustering Medical Documents.” International Journal of Emerging Technology and Advanced Engineering. Volume 2, Issue 4
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14. Nasser Taleb and Elfadil A/Alla Mohamed (2012) “Using Ticket History Method to Serialize Transactions In Distributed Databases.” Proceeding of the International conference on Excellence in Business, University of Sharjah, Sharjah, United Arab Emirates, May 2012.
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1. Elfadil A. Mohamed, Elgilani El. Elshareif, Omer Ishag Eldai Mohamed (2019). “Designing a Generic Information Systems Audit Framework to Improve the Quality of Audit in Higher Education.” Journal of Computer Science, Vol(15), Issue(5), PP. 664-672 May 10, 2019. DOI : 10.3844/jcssp.2019.664.672
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2. Mariam AlMarzooqi, Srikanth Bezawada, Asayel AlNaqbi, Elfadil A. Mohamed, AlMheiri, A., Nazar Zaki (2018). “Increase the exploitation of mars satellite images via deep learning techniques.” 2018 International Conference on Robotics, Control and Automation Engineering, RCAE 2018; Beijing; China
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3. Eiman Alothali, Nazar Zaki, Elfadil A. Mohamed, and Hany Alashwal (2018). “Detecting Social Bots on Twitter: A Literature Review. “ 2018 International Conference on Innovations in Information Technology (IIT). 18-19 Nov. 2018. IEEE Xplore Digital Library, DOI: 10.1109/INNOVATIONS.2018.8605995. Page : 175 – 180
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4. Mohammad Marjan, Nazar Zaki, and Elfadil A. Mohamed (2018). “Link Prediction in Dynamic Social Networks: A Literature Review.” IEEE CiSt'18 5th Edition International IEEE Congress on Information Science and Technology, Marrakech, Morocco, October 21 - 27, 2018. IEEE Xplore Digital Library, DOI: 10.1109/CIST.2018.8596511
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5. Maha Rahrouh, Nasser Taleb, and Elfadil A. Mohamed (2018). “Evaluating the Usefulness of E-Learning Management System Delivery in Higher Education.” Int. J. of Economics and Business Research 2018 - Vol. 16, No.2 pp. 162 – 181
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Nasser Taleb and Elfadil A. Mohamed (2020). “Cloud Computing Trends: A Literature Review.” Academic Journal of Interdisciplinary Studies, Vol (9), No (1), January 2020
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Balqis Al Breiki, Nazar Zaki, and Elfadil A. Mohamed (2019). “Using Educational Data Mining Techniques to Predict Student Performance.” The International Conference on Electrical and Computing Technologies and Applications, 2019, November 19 - 21, 2019, RAK, UAE. IEEE Xplore Digital Library, DOI: 10.1109/ICECTA48151.2019.8959676.
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Vasila Al Khaldi, Nazar Zaki, Essam Zaneldin, and Elfadil A. Mohamed (2019). “Using Data Mining Techniques to Identify Construction Claims Causes: A Case Study.” The International Conference on Electrical and Computing Technologies and Applications, 2019, November 19 - 21, 2019, RAK, UAE. IEEE Xplore Digital Library, DOI: 10.1109/ICECTA48151.2019.8959800
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Elfadil Abdalla Mohamed, Nazar Zaki, and Mohammad Marjan (2019). “Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review.” Advances in Science, Technology and Engineering Systems Journal, Vol. 4, No. 6, 244-254 (2019). DOI: 10.25046/aj040631
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M. M. Yagoub, Aishah A. Alsereidi, Elfadil A. Mohamed, Punitha Periyasamy, Reem Alameri, Salama Aldarmaki, and Yaqein Alhashmi. (2020). Newspapers as a validation proxy for GIS modelling in Fujairah, United Arab Emirates: identifying flood‑prone areas. Natural Hazards Journal, 17 July 2020, DOI: https://doi.org/10.1007/s11069-020-04161-y
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Nazar Zaki and Elfadil A. Mohamed (2021). The Estimations of the COVID-19 Incubation Period: A Scoping Reviews of the Literature. Journal of Infection and Public Health, 8 February 2021, https://doi.org/10.1016/j.jiph.2021.01.019.
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Elfadil A. Mohamed, Ibrahim Ahmed Elsiddig, Riyadh A.K. Mehdi, and Hanan Hussain (2021). Impact of corporate performance on stock price predictions in the UAE markets: Neuro-fuzzy model. Intelligent Systems in Accounting, Finance and Management. 18 January 2021, https://doi.org/10.1002/isaf.1484.
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Elgilani Elshareif and Elfadil A. Mohamed (2021). The Effects of E-Learning on Students' Motivation to Learn in Higher Education. Online Learning Journal, Volume 25 Issue 3 , Sept, 2021, pp. 128-143.
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Nazar Zaki, Harsh Singh and Elfadil A. Mohamed (2021). Identifying Protein Complexes in Protein-protein Interaction Data Using Graph Convolutional Network. IEEE ACCESS, Vol (9), 2021, Pages 123717 – 123726
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JournalTETIANA HABUZA, NAZAR ZAKI, ELFADIL A. MOHAMED, and YAUHEN STATSENKO, Deviation From Model of Normal Aging in Alzheimer's Disease: Application of Deep Learning to Structural MRI Data and Cognitive Tests, IEEE Access, Vol: 10, pp.53234 - 53249, May 2022, doi: 10.1109/ACCESS.2022.3174601
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JournalMohammed Azmi Al-Betar, Malik Sh. Braik, Elfadil A. Mohamed, Mohammed A. Awadallah & Mohamed Nasor, Augmented electric eel foraging optimization algorithm for feature selection with high-dimensional biological and medical diagnosis, Neural Comput & Applic, Sep 2024, doi: https://doi.org/10.1007/s00521-024-10288-x
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JournalNurul Athirah Nasarudin, Fatma Al Jasmi, Richard O. Sinnott, Nazar Zaki, Hany Al Ashwal, Elfadil A. Mohamed & Mohd Saberi Mohamad , A review of deep learning models and online healthcare databases for electronic health records and their use for health prediction, Artificial Intelligence Review, Vol: 57, Aug 2024, doi: https://doi.org/10.1007/s10462-024-10876-2
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JournalElfadil A. Mohamed Malik Sh. Braik Mohammed Azmi Al-Betar Mohammed A. Awadallah , Boosted Spider Wasp Optimizer for High-dimensional Feature Selection, Journal of Bionic Engineering, Jun 2024, doi: https://doi.org/10.1007/s42235-024-00558-8
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ConferenceElfadil A. Mohammed, Mahmoud Abou Naaj, Mirna Nachouki, & Riyad Mehdi , Predicting Student Grade Point Average: Comparison of Machine Learning Regression Algorithms., 24th International Arab Conference on Information Technology, ACIT 20232023 24th International Arab Conference on Information Technology, ACIT 2023Ajman6, Ajman
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JournalMirna Nachouki, Elfadil A. Mohamed, Riyadh Mehdi, &Mahmoud Abou Naaj, Student course grade prediction using the random forest algorithm: Analysis of predictors' importance, Trends in Neuroscience and Education, Sep 2023, doi: https://doi.org/10.1016/j.tine.2023.100214
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JournalNazar Zaki, Turaev Sherzod, Shuaib Khaled, Krishnan Anusuya, & Elfadil Mohamed, Automating the mapping of course learning outcomes to program learning outcomes using natural language processing for accurate educational program evaluation. E, Education and Information Technologies, May 2023, doi: https://doi.org/10.1007/s10639-023-11877-4
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JournalIbrahim Ahmed Elsiddig, Riyadh A.K. Mehdi, & Elfadil A. Mohamed, The role of artificial intelligence in developing a banking risk index: an application of Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), Artificial Intelligence Review, Apr 2023, doi: https://doi.org/10.1007/s10462-023-10473-9
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JournalMahmoud Abou Naaj, Riyadh Mehdi, Elfadil A. Mohamed and Mirna Nachouki, Analysis of the Factors Affecting Student Performance Using a Neuro-Fuzzy Approach, Education Sciences, Mar 2023, doi: https://doi.org/10.3390/educsci13030313
1. Shorouq Eletter, Nasser Taleb, Elfadil A. Mohamed, Loay Alnaji (2017). “Exploring interns’ satisfaction: A case study of United Arab Emirates.” International Journal of Applied Business and Economic Research, Volume 15, Issue 22, 2017, Pages 547-560
2. Elfadil A. Mohamed and Nasser Taleb (2017). “Framework for the Applications of Data Mining Technique in Viral Marketing Using Social Network.” 30th B&ESI CONFERENCE Abu Dhabi, UAE, January 8-11, 2017
4. Nayla Alkhatri , Nazar Zaki, Elfadil Mohammed, Musa Shallal (2016). “Data Mining Techniques to Predict the Ranking of E- Government Services.” 2016 12th International Conference on Innovations in Information Technology (IIT), IEEE Xplore Digital Library, Pages: 1 - 6, DOI: 10.1109/INNOVATIONS.2016.7880047
5. Alramzana Nujum Navaz, Elfadil Mohammed, Mohamed Adel Serhani, Nazar Zaki. (2016). “The Use of Data Mining Techniques to Predict Mortality and Length of Stay in an ICU.” 2016 12th International Conference on Innovations in Information Technology (IIT), IEEE Xplore Digital Library, Pages: 1 - 5, DOI: 10.1109/INNOVATIONS.2016.7880045 year: 2016
6. Noura AlDarmaki, Elfadil A. Mohamed , Noura AlMansouri, Ibrahim Elsiddig Ahmed, Nazar Zaki (2016). “Prediction of the Closing Price in the Dubai Financial Market: A Data Mining Approach.” 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC), IEEE Xplore Digital Library, Pages: 1 – 7, year: 2016
7. Elfadil A. Mohamed, Nasser Taleb, and Raed Ahmad Tawfeq Said (2016). “Evaluating the effectiveness of e-course delivery and e-assessment using Course Management System.” International Conference for Academic Disciplines, 31-January-2016, Al Ain, UAE
8. Nazar Zaki and Elfadil A. Mohamed (2016). “A Combination of Protein-Protein Interaction Network Topological and Biological Process Features for Multiprotein Complex Detection.” Book chapter in a book titled “Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology Systems and Applications”. Publisher: Elsevier Morgan Kaufman. page 305-315 , March, 22, 2016
9. Nasser Taleb and Elfadil Abdalla (2015). “Framework for Evaluating the Effectiveness of Data Mining in the Analysis of Dynamic Data for Supporting Financial Decision-Making,” 2015 Global Business & Economics Anthology, Vol. 1, USA. The paper also appeared in the Proceeding of the 26th B&ESI CONFERENCE Al Ain, UAE, March 1-4, 2015
10. Nazar Zaki, Elfadil A. Mohamed, and Antonio Mora (2015). “Characterization of Protein Complexes and Sub-complexes in Protein-protein Interaction Databases.” Journal of Biochemistry Research International. Volume 2015, Article ID 245075, USA
11. Elfadil A. Mohamed (2014). “Predicting Causes of Traffic Road Accidents Using Multi-class Support Vector Machines.” Journal of Communication and Computer, Vol. 11, Number 5, 2014, USA. The paper also appeared in the Proceeding of the 10th International Conference on Data Mining, July 23-24, 2014, Las Vegas, Nevada, USA
12. Elfadil A. Mohamed, F. H. Saad, and Omer I. E. Mohamed (2014). “Effects of Classification Techniques on Medical Reports Classification.” International Journal of Computers & Technology. Volume 13, No 2.
13. Omer I. E. Mohamed, F. H. Saad, and Elfadil A. Mohamed (2012). “Evaluation of Partitional Algorithms for Clustering Medical Documents.” International Journal of Emerging Technology and Advanced Engineering. Volume 2, Issue 4
14. Nasser Taleb and Elfadil A/Alla Mohamed (2012) “Using Ticket History Method to Serialize Transactions In Distributed Databases.” Proceeding of the International conference on Excellence in Business, University of Sharjah, Sharjah, United Arab Emirates, May 2012.
1. Elfadil A. Mohamed, Elgilani El. Elshareif, Omer Ishag Eldai Mohamed (2019). “Designing a Generic Information Systems Audit Framework to Improve the Quality of Audit in Higher Education.” Journal of Computer Science, Vol(15), Issue(5), PP. 664-672 May 10, 2019. DOI : 10.3844/jcssp.2019.664.672
2. Mariam AlMarzooqi, Srikanth Bezawada, Asayel AlNaqbi, Elfadil A. Mohamed, AlMheiri, A., Nazar Zaki (2018). “Increase the exploitation of mars satellite images via deep learning techniques.” 2018 International Conference on Robotics, Control and Automation Engineering, RCAE 2018; Beijing; China
3. Eiman Alothali, Nazar Zaki, Elfadil A. Mohamed, and Hany Alashwal (2018). “Detecting Social Bots on Twitter: A Literature Review. “ 2018 International Conference on Innovations in Information Technology (IIT). 18-19 Nov. 2018. IEEE Xplore Digital Library, DOI: 10.1109/INNOVATIONS.2018.8605995. Page : 175 – 180
4. Mohammad Marjan, Nazar Zaki, and Elfadil A. Mohamed (2018). “Link Prediction in Dynamic Social Networks: A Literature Review.” IEEE CiSt'18 5th Edition International IEEE Congress on Information Science and Technology, Marrakech, Morocco, October 21 - 27, 2018. IEEE Xplore Digital Library, DOI: 10.1109/CIST.2018.8596511
5. Maha Rahrouh, Nasser Taleb, and Elfadil A. Mohamed (2018). “Evaluating the Usefulness of E-Learning Management System Delivery in Higher Education.” Int. J. of Economics and Business Research 2018 - Vol. 16, No.2 pp. 162 – 181
Nasser Taleb and Elfadil A. Mohamed (2020). “Cloud Computing Trends: A Literature Review.” Academic Journal of Interdisciplinary Studies, Vol (9), No (1), January 2020
Balqis Al Breiki, Nazar Zaki, and Elfadil A. Mohamed (2019). “Using Educational Data Mining Techniques to Predict Student Performance.” The International Conference on Electrical and Computing Technologies and Applications, 2019, November 19 - 21, 2019, RAK, UAE. IEEE Xplore Digital Library, DOI: 10.1109/ICECTA48151.2019.8959676.
Vasila Al Khaldi, Nazar Zaki, Essam Zaneldin, and Elfadil A. Mohamed (2019). “Using Data Mining Techniques to Identify Construction Claims Causes: A Case Study.” The International Conference on Electrical and Computing Technologies and Applications, 2019, November 19 - 21, 2019, RAK, UAE. IEEE Xplore Digital Library, DOI: 10.1109/ICECTA48151.2019.8959800
Elfadil Abdalla Mohamed, Nazar Zaki, and Mohammad Marjan (2019). “Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review.” Advances in Science, Technology and Engineering Systems Journal, Vol. 4, No. 6, 244-254 (2019). DOI: 10.25046/aj040631
M. M. Yagoub, Aishah A. Alsereidi, Elfadil A. Mohamed, Punitha Periyasamy, Reem Alameri, Salama Aldarmaki, and Yaqein Alhashmi. (2020). Newspapers as a validation proxy for GIS modelling in Fujairah, United Arab Emirates: identifying flood‑prone areas. Natural Hazards Journal, 17 July 2020, DOI: https://doi.org/10.1007/s11069-020-04161-y
Nazar Zaki and Elfadil A. Mohamed (2021). The Estimations of the COVID-19 Incubation Period: A Scoping Reviews of the Literature. Journal of Infection and Public Health, 8 February 2021, https://doi.org/10.1016/j.jiph.2021.01.019.
Elfadil A. Mohamed, Ibrahim Ahmed Elsiddig, Riyadh A.K. Mehdi, and Hanan Hussain (2021). Impact of corporate performance on stock price predictions in the UAE markets: Neuro-fuzzy model. Intelligent Systems in Accounting, Finance and Management. 18 January 2021, https://doi.org/10.1002/isaf.1484.
Elgilani Elshareif and Elfadil A. Mohamed (2021). The Effects of E-Learning on Students' Motivation to Learn in Higher Education. Online Learning Journal, Volume 25 Issue 3 , Sept, 2021, pp. 128-143.
The recent COVID-19 pandemic has forced educational institutions worldwide to adopt e-learning. UAE higher education institutions have implemented e-learning systems and programs to cope with this unprecedented situation. This paper measured the strength of association between key aspects of e-learning systems and programs and students’ motivation to learn in Ajman University (AU). Cronbach’s coefficient alpha was used to test the internal consistency reliability of key aspects of e-learning (EL-8) and students’ motivation to learn (SML-16). Exploratory factor analysis was used to test the validity of, and coherence of patterns in, the data. Parametric and non-parametric methods were used to investigate the strength of association between key aspects of e-learning and students’ motivation to learn in AU. The results indicated that motivation variables were more strongly correlated with both e-teaching materials and e-assessments key aspects relative to others such as e-discussion, and e-grade checking and feedback
About the journal
Nazar Zaki, Harsh Singh and Elfadil A. Mohamed (2021). Identifying Protein Complexes in Protein-protein Interaction Data Using Graph Convolutional Network. IEEE ACCESS, Vol (9), 2021, Pages 123717 – 123726
Protein complexes are groups of two or more polypeptide chains that bind to form noncovalent networks of protein interactions. Over the past decade, researchers have created a number of means of computing the ways in which protein complexes and their members can be identied through these interaction networks. Although most of the existing methods identify protein functional complexes from the protein-protein interaction networks (PPIs) at a fairly decent level, the applicability of advanced graph network methods has not yet been adequately investigated. This paper proposes various graph convolutional network (GCN) methods to improve the detection of protein complexes. We rst formulate the protein complex detection problem as a node classication problem. Then, we developed a Neural Overlapping Community Detection (NOCD) model to cluster the nodes (proteins) using a complex afliation matrix. A representation learning approach, that combines a multi-class GCN feature extractor (to obtain the nodes' features) and a mean shift clustering algorithm (to perform the clustering), is also utilized. We convert the dense-dense matrix operations into dense-sparse or sparse-sparse matrix operations to improve the efciency of the multi-class GCN network by reducing space and time complexities. The proposed solution signicantly improves the scalability of the existing GCN. Finally, we apply clustering aggregation to nd the best protein complexes. A grid search is then performed on various detected complexes obtained via three well-known protein detection methods, namely ClusterONE, CMC, and PEWCC, with the help of the Meta-Clustering Algorithm (MCLA) and the Hybrid Bipartite Graph Formulation (HBGF). We test the proposed GCN-based methods on various publicly available datasets and nd that they perform signicantly better than previous state-of-the-art methods. The code/data are available for free download from https://github.com/Analystharsh/GCN_complex_detection.
About the journal
TETIANA HABUZA, NAZAR ZAKI, ELFADIL A. MOHAMED, and YAUHEN STATSENKO, Deviation From Model of Normal Aging in Alzheimer's Disease: Application of Deep Learning to Structural MRI Data and Cognitive Tests, IEEE Access, Vol: 10, pp.53234 - 53249, May 2022, doi: 10.1109/ACCESS.2022.3174601
Psychophysiological and cognitive tests as well as other functional studies can detect pre-symptomatic stages of dementia. When assembled with structural data, cognitive tests diagnose NDs more reliably thus becoming a multimodal diagnostic tool. Objective. Our main goal is to improve screening for dementia by studying an association between the brain structure and its function. Hypothetically, the brain structure-function association has features specific for either disease-related cognitive deterioration or normal neurocognitive slowing while aging. Materials and methods. We studied a total number of 287 cognitively normal cases, 646 of mild cognitive impairment, and 369 of Alzheimer's disease. To work out a new marker of neurodegeneration, we created a convolutional neural network-based regression model and predicted the cognitive status of the cognitively preserved examinee from the brain MRI data. This was a model of normal aging. A big deviation from the model suggests a high risk of accelerated cognitive decline. Results. The deviation from the model of normal aging can accurately distinguish cognitively normal subjects from MCI patients (AUC D 0.9957). We also achieved creditable performance in the MCI-versus-AD classification (AUC D 0.9793). We identified a considerable difference in the MMSE test between A-positive and A-negative demented individuals according to ATN-criteria ( 6.27 ± 1.82 vs 5.32 ± 1.9; p < 0:05). Conclusion. The deviation from the model of normal aging can be potentially used as a marker of dementia and as a tool for differentiating Alzheimer's disease from non-Alzheimer's dementia. To find and justify a reliable threshold levels, further research is required.
About the journal
Mohammed Azmi Al-Betar, Malik Sh. Braik, Elfadil A. Mohamed, Mohammed A. Awadallah & Mohamed Nasor, Augmented electric eel foraging optimization algorithm for feature selection with high-dimensional biological and medical diagnosis, Neural Comput & Applic, Sep 2024, doi: https://doi.org/10.1007/s00521-024-10288-x
This paper explores the importance of the electric eel foraging optimization (EEFO) algorithm in addressing feature selection (FS) problems, with the aim of ameliorating the practical benefit of FS in real-world applications. The use of EEFO to solve FS problems props our goal of providing clean and useful datasets that provide robust effectiveness for use in classification and clustering tasks. High-dimensional feature selection problems (HFSPs) are more common nowadays yet intricate where they contain a large number of features. Hence, the vast number of features in them should be carefully selected in order to determine the optimal subset of features. As the basic EEFO algorithm experiences premature convergence, there is a need to enhance its global and local search capabilities when applied in the field of FS. In order to tackle such issues, a binary augmented EEFO (BAEEFO) algorithm was developed and proposed for HFSPs. The following strategies were integrated into the mathematical model of the original EEFO algorithm to create BAEEFO: (1) resting behavior with nonlinear coefficient; (2) weight coefficient and confidence effect in the hunting process; (3) spiral search strategy; and (4) Gaussian mutation and random perturbations when the algorithm update is stagnant. Experimental findings confirm the effectiveness of the proposed BAEEFO method on 23 HFSPs gathered from the UCI repository, recording up to a 10% accuracy increment over the basic BEEFO algorithm. In most test cases, BAEEFO outperformed its competitors in classification accuracy rates and outperformed BEEFO in 90% of the datasets used. Thereby, BAEEFO has demonstrated strong competitiveness in terms of fitness scores and classification accuracy. When compared to its competitors, BAEEFO produced superior reduction rates with the fewest number of features selected. The findings in this research underscore the critical need for FS to combat the curse of dimensionality concerns and find highly useful features in data mining applications such as classification. The use of a new meta-heuristic algorithm incorporated with efficient search strategies in solving HFSPs represents a step forward in using this algorithm to solve other practical real-world problems in a variety of domains.
About the journal
Nurul Athirah Nasarudin, Fatma Al Jasmi, Richard O. Sinnott, Nazar Zaki, Hany Al Ashwal, Elfadil A. Mohamed & Mohd Saberi Mohamad , A review of deep learning models and online healthcare databases for electronic health records and their use for health prediction, Artificial Intelligence Review, Vol: 57, Aug 2024, doi: https://doi.org/10.1007/s10462-024-10876-2
A fundamental obstacle to healthcare transformation continues to be the acquisition of knowledge and insightful data from complex, high dimensional, and heterogeneous biological data. As technology has improved, a wide variety of data sources, including omics data, imaging data, and health records, have been available for use in healthcare research contexts. Electronic health records (EHRs), which are digitalized versions of medical records, have given researchers a significant chance to create computational methods for analyzing healthcare data. EHR systems typically keep track of all the data relating to a patient’s medical history, including clinical notes, demographic background, and diagnosis details. EHR data can offer valuable insights and support doctors in making better decisions related to disease and diagnostic forecasts. As a result, several academics use deep learning to forecast diseases and track health trajectories in EHR. Recent advances in deep learning technology have produced innovative and practical paradigms for building end-to-end learning models. However, scholars have limited access to online HER databases, and there is an inherent need to address this issue. This research examines deep learning models, their architectures, and readily accessible EHR online databases. The goal of this paper is to examine how various architectures, models, and databases differ in terms of features and usability. It is anticipated that the outcomes of this review will lead to the development of more robust deep learning models that facilitate medical decision-making processes based on EHR data and inform efforts to support the selection of architectures, models, and databases for specific research purposes.
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Elfadil A. Mohamed Malik Sh. Braik Mohammed Azmi Al-Betar Mohammed A. Awadallah , Boosted Spider Wasp Optimizer for High-dimensional Feature Selection, Journal of Bionic Engineering, Jun 2024, doi: https://doi.org/10.1007/s42235-024-00558-8
With the increasing dimensionality of the data, High-dimensional Feature Selection (HFS) becomes an increasingly difficult task. It is not simple to find the best subset of features due to the breadth of the search space and the intricacy of the interactions between features. Many of the Feature Selection (FS) approaches now in use for these problems perform significantly less well when faced with such intricate situations involving high-dimensional search spaces. It is demonstrated that meta-heuristic algorithms can provide sub-optimal results in an acceptable amount of time. This paper presents a new binary Boosted version of the Spider Wasp Optimizer (BSWO) called Binary Boosted SWO (BBSWO), which combines a number of successful and promising strategies, in order to deal with HFS. The shortcomings of the original BSWO, including early convergence, settling into local optimums, limited exploration and exploitation, and lack of population diversity, were addressed by the proposal of this new variant of SWO. The concept of chaos optimization is introduced in BSWO, where initialization is consistently produced by utilizing the properties of sine chaos mapping. A new convergence parameter was then incorporated into BSWO to achieve a promising balance between exploration and exploitation. Multiple exploration mechanisms were then applied in conjunction with several exploitation strategies to effectively enrich the search process of BSWO within the search space. Finally, quantum-based optimization was added to enhance the diversity of the search agents in BSWO. The proposed BBSWO not only offers the most suitable subset of features located, but it also lessens the data’s redundancy structure. BBSWO was evaluated using the k-Nearest Neighbor (k-NN) classifier on 23 HFS problems from the biomedical domain taken from the UCI repository. The results were compared with those of traditional BSWO and other well-known meta-heuristics-based FS. The findings indicate that, in comparison to other competing techniques, the proposed BBSWO can, on average, identify the least significant subsets of features with efficient classification accuracy of the k-NN classifier.
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Elfadil A. Mohammed, Mahmoud Abou Naaj, Mirna Nachouki, & Riyad Mehdi , Predicting Student Grade Point Average: Comparison of Machine Learning Regression Algorithms., 24th International Arab Conference on Information Technology, ACIT 20232023 24th International Arab Conference on Information Technology, ACIT 2023Ajman6, Ajman
Education data mining has emerged as a powerful technique for uncovering hidden patterns in educational data, forecasting academic achievement, and increasing retention rates. In this work, the performance of nine regression algorithms has been evaluated in predicting students’ academic success. Information from 650 students enrolled in three different computing majors has been assembled into a dataset. The following input attributes were chosen: attendance rate, course grade, gender, course category, delivery mode, school type, and high school score; the grade point average was the target variable. Findings indicate that Random Forest Regressor, Light Gradient Boosting Machine, Gradient Boosting Regressor, and Extra Tree are the four most effective regression algorithms in the order given. Except for the Light Gradient Boosting Machine approach, the other three algorithms showed that course grade is the most important predictor of a student's GPA, followed by high school score. All four algorithms showed that gender is the least reliable indicator of GPA. Future work will conduct sensitivity analysis to evaluate the impact of individual attributes on predictions to gain more insight into the factors affecting students’ performance. The effect of social, economic, and demographic features will also be investigated Keywords— Educational Data Mining, Machine Learning, Regression Models, Student Success Prediction.
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Mirna Nachouki, Elfadil A. Mohamed, Riyadh Mehdi, &Mahmoud Abou Naaj, Student course grade prediction using the random forest algorithm: Analysis of predictors' importance, Trends in Neuroscience and Education, Sep 2023, doi: https://doi.org/10.1016/j.tine.2023.100214
Background: Universities need to find strategies for improving student retention rates. Predicting student academic performance enables institutions to identify underachievers and take appropriate actions to increase student completion and lower dropout rates. Method: In this work, we proposed a model based on random forest methodology to predict students’ course performance using seven input predictors and find their relative importance in determining the course grade. Seven predictors were derived from transcripts and recorded data from 650 undergraduate computing students. Results: Our findings indicate that grade point average and high school score were the two most significant predictors of a course grade. The course category and class attendance percentage have equal importance. Course delivery mode does not have a significant effect. Conclusion: Our findings show that courses students at risk find challenging can be identified, and appropriate actions, procedures, and policies can be taken.
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Nazar Zaki, Turaev Sherzod, Shuaib Khaled, Krishnan Anusuya, & Elfadil Mohamed, Automating the mapping of course learning outcomes to program learning outcomes using natural language processing for accurate educational program evaluation. E, Education and Information Technologies, May 2023, doi: https://doi.org/10.1007/s10639-023-11877-4
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Ibrahim Ahmed Elsiddig, Riyadh A.K. Mehdi, & Elfadil A. Mohamed, The role of artificial intelligence in developing a banking risk index: an application of Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), Artificial Intelligence Review, Apr 2023, doi: https://doi.org/10.1007/s10462-023-10473-9
Banking risk measurement and management remain one of many challenges for managers and policymakers. This study contributes to the banking literature and practice in two ways by (a) proposing a risk ranking index based on the Mahalanobis Distance (MD) between a multidimensional point representing a bank’s risk measures and the corresponding critical ratios set by the banking authorities and (b) determining the relative importance of a bank’s risk ratios in affecting its financial standing using an Adaptive Neuro-Fuzzy Inference System. In this study, ten financial ratios representing five risk areas were considered, namely: Capital Adequacy, Credit, Liquidity, Earning Quality, and Operational risk. Data from 45 Gulf banks for the period 2016–2020 was used to develop the model. Our findings indicate that a bank is in a sound risk position at the 99%, 95%, and 90% confidence level if its Mahalanobis distance exceeds 4.82, 4.28, and 4.0, respectively. The maximum distance computed for the banks in this study was 9.31; only five out of the forty-five banks were below the 4.82 and one below the 4.28 and 4.0 thresholds at 3.96. Sensitivity analysis of the risks indicated that the Net Interest Margin is the most significant factor in explaining variations in a bank’s risk position, followed by Capital Adequacy Ratio, Common Equity Tier1, and Tier1 Equity in order. The remaining financial ratios: Non-Performing Loans, Equity Leverage, Cost Income Ratio, Loans to Total Assets, and Loans to Deposits have the least influence in the order given; the Provisional Loans Ratio appears to have no influence.
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Mahmoud Abou Naaj, Riyadh Mehdi, Elfadil A. Mohamed and Mirna Nachouki, Analysis of the Factors Affecting Student Performance Using a Neuro-Fuzzy Approach, Education Sciences, Mar 2023, doi: https://doi.org/10.3390/educsci13030313
Predicting students’ academic performance and the factors that significantly influence it can improve students’ completion and graduation rates, as well as reduce attrition rates. In this study, we examine the factors influencing student academic achievement. A fuzzy-neural approach is adopted to build a model that predicts and explains variations in course grades among students, based on course category, student course attendance rate, gender, high-school grade, school type, grade point average (GPA), and course delivery mode as input predictors. The neuro-fuzzy system was used because of its ability to implicitly capture the functional form between the dependent variable and input predictors. Our results indicate that the most significant predictors of course grades are student GPA, followed by course category. Using sensitivity analysis, student attendance was determined to be the most significant factor explaining the variations in course grades, followed by GPA, with course delivery mode ranked third. Our findings also indicate that a hybrid course delivery mode has positively impacted course grades as opposed to online or face-to-face course delivery alone. Keywords: educational data mining; neuro-fuzzy systems; student performance; prediction; neural networks; fuzzy systems