Dr. Abdullah A.Alqahtani Chair's Co-Supervisor for Scientific Research & Innvoation, Vice Dean for Postgraduate Studies & Scientific Research, Associate Professor
areas of expertise
- E-gov Service Integration
- Information Technology
- Privacy
- Data Mining
- Data Science & ML
qualifications
- PhD in Life Event Ontology Based E-Government Service Integration with Privacy Awareness, Australia
- Master of Information and communication Technology, Australia
- Bachelor, Saudi Arabia
publications
| # | Research Title | Publisher and Date of Publication |
|---|---|---|
| 1 | Digitalization of learning in Saudi Arabia during the COVID-19 outbreak: A survey | Informatics in Medicine Unlocked 25, 100632 |
| 2 | Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning | Sensors 22 (10), 3833 |
| 3 | Reversible and Fragile Watermarking for Medical Images | Computational and Mathematical Methods in Medicine 2018 |
| 4 | Investigating the effect of correlation based feature selection on breast cancer diagnosis using artificial neural network and support vector machines | 2017 International Conference on Informatics, Health & Technology (ICIHT), 1-7 |
| 5 | Robust and Fragile Medical Image Watermarking: A Joint Venture of Coding and Chaos Theories | Journal of Healthcare Engineering 2018 |
| 6 | Memory based cuckoo search algorithm for feature selection of gene expression dataset | Informatics in Medicine Unlocked 24, 100572 |
| 7 | Educational data mining for enhanced teaching and learning | Journal of Theoretical and Applied Information Technology 96 (14), 4417-4427 |
| 8 | Comorbidities and Risk Factors for Severe Outcomes in COVID-19 Patients in Saudi Arabia: A Retrospective Cohort Study | Journal of Multidisciplinary Healthcare 14, 2169 |
| 9 | Estimation of Curie temperature of manganite-based materials for magnetic refrigeration application using hybrid gravitational based support vector regression | AIP Advances 6 (10), 105009 |
| 10 | Ensemble-based support vector regression with gravitational search algorithm optimization for estimating magnetic relative cooling power of manganite refrigerant in magnetic | Journal of Superconductivity and Novel Magnetism 32 (7), 2107-2118 |
| 11 | Secure data aggregation scheme in wireless sensor networks for IoT | 2016 International Symposium on Networks, Computers and Communications |
| 12 | Incorporation of GSA in SBLLM-based neural network for enhanced estimation of magnetic ordering temperature of manganite | Journal of Intelligent & Fuzzy Systems 33 (2), 1225-1233 |
| 13 | Modeling of Curie temperature of manganite for magnetic refrigeration application using manual search and hybrid gravitational-based support vector regression | Soft Computing 22 (9), 3023-3032 |
| 14 | Support Vector Regression Ensemble for Effective Modeling of Magnetic Ordering Temperature of Doped Manganite in Magnetic Refrigeration | Journal of Low Temperature Physics 195 (1), 179-201 |
| 15 | Medical Image Watermarking for Fragility and Robustness: A Chaos, Error Correcting Codes and Redundant Residue Number System Based Approach | Journal of Medical Imaging and Health Informatics 8 (6), 1192-1200 |
| 16 | Adaptive Communication for Capacity Enhancement: A Hybrid Intelligent Approach | Journal of Computational and Theoretical Nanoscience 15 (4), 1182-1191 |
| 17 | Knowledge-based life event model for e-government service integration with illustrative examples | Intelligent Decision Technologies 8 (3), 189-205 |
| 18 | An Enhanced Particle Swarm Optimization for ITC2021 Sports Timetabling | CMC-COMPUTERS MATERIALS & CONTINUA 72 (1), 1995-2014 |
| 19 | Modeling energy band gap of doped TiO2 semiconductor using homogeneously hybridized support vector regression with gravitational search algorithm | AIP Advances 7 (11), 115225 |
| 20 | Modeling the magnetic cooling efficiency of spinel ferrite magnetocaloric compounds for magnetic refrigeration application using hybrid intelligent computational methods | Materials Today Communications 33, 104310 |
contact details
+966 13 3333474