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Professional Overview

Bio: Safwat Hamad is a highly accomplished professional in computer and information sciences, renowned for his contributions to academia and industry. With a Ph.D. in High-Performance Computing from Ain Shams University and the University of Connecticut, Dr. Hamad has amassed over a decade of experience in leadership roles, including serving as Chief Technology Officer at Ain Shams University Hospitals for four years. His expertise extends to consultancy in AI, NLP, ITSM, and Social Media Analytics, where he has provided invaluable insights to various organizations. Currently, Dr. Hamad holds the position of Professor of Data Science at Saint Mary's College of California in the Business Analytics Department, where he continues to blend technical acumen with innovative approaches in healthcare informatics and computational biology. His research, deeply rooted in AI, NLP, Machine Learning, and Quantum Computing, underscores his commitment to bridging theoretical understanding with practical applications, as reflected in his extensive publication record and active engagement within the professional community. Hamad's leadership, avant-garde educational methodologies, and impactful contributions epitomize his dedication to scientific and academic progression.

Courses Taught:

• Machine Learning
• Natural Language Processing
• Programming
• Advanced Data Analytics
• Data Science Capstone

Publications:

• Hamad, S. H. (2024). YOLO-based CAD framework with ViT transformer for breast mass detection and classification in CESM and FFDM images. Neural Computing and Applications.
• Hamad, S. H. (2023). Securing Patient Medical Records with Blockchain Technology in Cloud-based Healthcare Systems. International Journal of Advanced Computer Science and Applications, 14(11), 330-337.
• Hamad, S. H. (2023). Revolutionizing Medical Imaging through Deep Learning Techniques:An Overview. International Journal of Intelligent Computing and Information Sciences, 23(3), 59-72.
• Hamad, S. H. (2023). CONVOLUTIONAL NEURAL NETWORK MODELS FOR CANCER TREATMENT RESPONSE PREDICTION. International Journal of Intelligent Computing and Information Sciences, 23(1), 98-105.
• Hamad, S. H. (2023). Developing a mortality risk prediction model using data of 3663 hospitalized COVID-19 patients: a retrospective cohort study in an Egyptian University Hospital. BMC Pulmonary Medicine, 23.
• Hamad, S. H. (2023). Feedforward Neural Network in Cancer Treatment Response Prediction (vol. 164, pp. 119-128). Marrakesh: Springer Nature Switzerland.
• Mobark, N., Hamad, S. H., Rida, S. Z. CoroNet: Deep Neural Network-Based End-to-End Training for Breast Cancer Diagnosis. Applied Sciences, 12(14), 7080.
• Karim, F. K., Elmannai, H., Seleem, A., Hamad, S. H., Mostafa, S. M. Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy Measures. Electronics, 11(23), 3929.
• Ahmed, H., Hamad, S. H., Shedeed, H. A., Hussein, A. S. (2022). Enhanced Deep Learning Model for Personalized Cancer Treatment. IEEE Access, 10, 106050-106058.
• Abulkasim, H., Alabdulkreem, E., Hamad, S. H. (2022). Improved Multi-party Quantum Key Agreement with Four-qubit Cluster States. Computers, Materials & Continua, 73(1), 225-232.
• AlEisa, H. N., Hamad, S. H., Elhadad, A. (2022). K-Mer Spectrum-Based Error Correction Algorithm for Next-Generation Sequencing Data. Computational Intelligence and Neuroscience, 2022, 1-8.
• Hassan, N. M., Hamad, S. H., Mahar, K. (2022). Mammogram breast cancer CAD systems for mass detection and classification: a review. Multimedia Tools and Applications, 81(14), 20043-20075.
• Elsayad, D., Hamad, S. H., Shedeed, H. A., Tolba, M. F. (2021). Parallel network component analysis technique for gene regulatory network inference. Concurrency and Computation: Practice and Experience, 33(24).
• Elsayad, D., Hamad, S. H., Shedeed, H. A., Tolba, M. F. (2021). Parallel network component analysis technique for gene regulatory network inference. Concurrency and Computation: Practice and Experience, 33(24).
• Mostafa, S. M., Eladimy, A. S., Hamad, S. H., Amano, H. (2020). CBRG: A Novel Algorithm for Handling Missing Data Using Bayesian Ridge Regression and Feature Selection Based on Gain Ratio. IEEE Access, 8, 216969-216985.
• M. Mostafa, S., S. Eladimy, A., Hamad, S. H., Amano, H. (2020). CBRL and CBRC: Novel Algorithms for Improving Missing Value Imputation Accuracy Based on Bayesian Ridge Regression. Symmetry, 12(10), 1594.
• Bedair, K., Elhadad, A., Hamad, S. H., Ferguson, J., Donnan, P., Dawe, R. S. (2020). No association between whole-body ultraviolet A1 phototherapy and skin cancers in humans: a cancer registry linkage study. British Journal of Dermatology, 183(3), 586--587. https://onlinelibrary.wiley.com/doi/abs/10.1111/bjd.19041
• Safwat, S. M., Matta, M. E. (2020). Performance evaluation of electrocoagulation process using zinc electrodes for removal of urea. Separation Science and Technology, 55(14), 2500-2509.
• Elhadad, A., Abbas, S., Abulkasim, H., Hamad, S. H. (2020). Improving the security of multi-party quantum key agreement with five-qubit Brown states. Computer Communications, 159, 155-160.
• Abulkasim, H., Farouk, A., Hamad, S. H., Mashatan, A., Ghose, S. (2019). Secure dynamic multiparty quantum private comparison. Scientific Reports, 9(1).
• Abulkasim, H., Alsuqaih, H. N., Hamdan, W. F., Hamad, S. H., Farouk, A., Mashatan, A., Ghose, S. (2019). Improved Dynamic Multi-Party Quantum Private Comparison for Next-Generation Mobile Network. IEEE Access, 7, 17917-17926.
• Alanazi, F., Elhadad, A., Hamad, S. H., Ghareeb, A. (2019). Sensors data collection framework using mobile identification with secure data sharing model. International Journal of Electrical and Computer Engineering (IJECE), 9(5), 4258.
• Elhadad, A., Hamad, S. H., Khalifa, A., Abulkasim, H. (2020). A steganography approach for hiding privacy in video surveillance systems. Digital Media Steganography (pp. 165-187). Elsevier.
• Elsayad, D., Hamad, S. H., Shedeed, H. A., Tolba, M. F. (2020). Gene Regulatory Network Construction Parallel Technique Based on Network Component Analysis. Advances in Intelligent Systems and Computing (pp. 850-857). Springer International Publishing.
• Elsayad, D., Hamad, S. H., Shedeed, H. A., Tolba, M. F. (2020). Hybrid Parallel Computation for Sparse Network Component Analysis. Advances in Intelligent Systems and Computing (pp. 801-808). Springer International Publishing.
• Elsayad, D., Hamad, S. H., Shedeed, H. A., Tolba, M. F. (2020). Parallel Computation for Sparse Network Component Analysis. Advances in Intelligent Systems and Computing (pp. 918-927). Springer International Publishing.
• Ahmed, H., Shedeed, H. A., Hamad, S. H., Hussein, A. S. (2022). Convolutional Neural Network for Cancer Treatment Response Prediction. 2022 18th International Computer Engineering Conference (ICENCO). IEEE.
• Elhadad, A., Tibermacine, O., Hamad, S. H. (2022). Hiding Privacy Data in Visual Surveillance Video based on Wavelet and Flexible Function. 2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH). IEEE.
• Ahmed, H., Hamad, S. H., Shedeed, H. A., Saad, A. (2022). Review of Personalized Cancer Treatment with Machine Learning. 2022 5th International Conference on Computing and Informatics (ICCI). IEEE.

Education

• PhD, Computer and Information Sciences, High Performance Computing. Ain Shams University, 2008; 
• MSC, Modeling, Simulation and Visualization. Ain Shams University, 2004
• BSC, Computer and Information Sciences, Scientific Computing. Ain Shams University, 2000.