Prudence Munyaradzi Mavhemwa | Computer Science | Best Researcher Award

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Mr. Prudence Munyaradzi Mavhemwa | Computer Science | Best Researcher Award

University of Rwanda |Rwanda

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Early Academic Pursuits 🎓

Prudence Munyaradzi Mavhemwa embarked on his academic journey with an unwavering passion for computer science and technology. He earned a Bachelor of Science (Hons) in Computer Science from Bindura University of Science Education in 2008, laying a solid foundation in programming, system analysis, and database management.
Later, he pursued a Master of Science in Computer Science at the University of Zimbabwe (2010–2012), where he delved deeper into advanced computational methods and application development. Recognizing the need to foster education, he added a Postgraduate Diploma in Tertiary Education (2015–2016) from Bindura University, enhancing his pedagogical expertise.
Currently, he is completing a PhD in Embedded Computing Systems (2020–2024) at the University of Rwanda, focusing on improving user authentication in the Internet of Medical Things (IoMT) in Rwanda under the mentorship of distinguished supervisors from the University of Rwanda and the University of Pretoria.

Professional Endeavors 💻

Mr. Mavhemwa's professional journey spans over a decade of academic and technical contributions. He began his career as a Software Developer Intern at Provenance Support Company in Harare, developing payroll and MIS systems.
At Bindura University of Science Education, he ascended to the role of Chairman of the Computer Science Department (2019–2022) and served as a full-time lecturer (2010–2024), imparting knowledge in programming languages, artificial intelligence, and computer security.
In addition to his full-time commitments, he contributed to Zimbabwe Ezekiel Guti University as a part-time lecturer, showcasing his dedication to advancing computer science education across institutions.

Contributions and Research Focus 🔬

Mr. Mavhemwa’s research focuses on cutting-edge topics, including:

  • Usable security and IoT for development (IoT4D).
  • Health informatics, particularly adaptive authentication models for medical applications.
    He has contributed significantly through innovative projects such as an IoMT adaptive authentication system for the elderly and user-centered designs leveraging machine learning and wearable technology.
    His expertise has guided several undergraduate research projects, influencing the next generation of computing professionals.

Accolades and Recognition 🏆

Mr. Mavhemwa’s academic rigor and professional excellence have earned him recognition in multiple domains. As a peer reviewer for prestigious journals like Heliyon and the Journal of Cybersecurity and Privacy, he has demonstrated his commitment to advancing scientific discourse.
His published works span innovative topics such as mobile-based learning, adaptive authentication systems, and health informatics, with notable articles in Springer and IOP Publishing.

Impact and Influence 🌟

Through his teaching and leadership roles, Mr. Mavhemwa has significantly impacted the academic community. His tenure at Bindura University was marked by efforts to modernize computer science curricula and foster a culture of innovation. His research has implications for both academia and practical applications, particularly in enhancing security protocols in healthcare and improving educational accessibility through technology.

Legacy and Future Contributions 🔮

As Mr. Mavhemwa continues to refine his PhD research and publish groundbreaking studies, his work is set to influence the fields of IoT, health informatics, and user-centered design. His dedication to academic mentorship and innovative problem-solving ensures a lasting legacy in computer science education and research.

 

Publications

📖 An Android-Based Internet of Medical Things Adaptive User Authentication and Authorization Model for the Elderly
Journal of Cybersecurity and Privacy
Year: 2024
Authors: Prudence M. Mavhemwa, Marco Zennaro, Philibert Nsengiyumva, Frederic Nzanywayingoma

📖 Weighted Naïve Bayes Multi-User Classification for Adaptive Authentication
Journal: Journal of Physics Communications
Year: 2024
Authors: Prudence M. Mavhemwa, Marco Zennaro, Philibert Nsengiyumva, Frederic Nzanywayingoma

📖 An Android-Based Internet of Medical Things Adaptive User Authentication and Authorization Model for the Elderly
Journal: Preprint
Year: 2024
Authors: Prudence M. Mavhemwa, Marco Zennaro, Philibert Nsengiyumva, Frederic Nzanywayingoma

📖 User-Centred Design of Machine Learning-Based Internet of Medical Things (IoMT) Adaptive User Authentication Using Wearables and Smartphones
Journal: Book Chapter in Lecture Notes in Networks and Systems
Year: 2023
Authors: Prudence M. Mavhemwa, Marco Zennaro, Philibert Nsengiyumva, Frederic Nzanywayingoma

📖 Uniform Spatial Subdivision to Improve Boids Algorithm in a Gaming Environment
Journal: International Journal of Advance Research and Development
Year: 2018
Author: Prudence Munyaradzi Mavhemwa

 

Banafshe Felfeliyan | Health Professions | Best Researcher Award

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Dr. Banafshe Felfeliyan | Health Professions | Best Researcher Award

University of Alberta | Canada

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Early Academic Pursuits

Dr. Banafshe Felfeliyan embarked on her academic journey with a Bachelor's degree in Computer Engineering from Isfahan University of Technology, Iran, followed by a Master's focusing on coronary vessel segmentation. She then pursued a Ph.D. in Biomedical Engineering, specializing in medical imaging, from the University of Calgary, Canada. Her doctoral research delved into automatic quantification of osteoarthritis features using deep learning techniques.

Professional Endeavors

Dr. Felfeliyan's professional career has been marked by significant contributions in the field of medical imaging and artificial intelligence. She served as a Computer Research Engineer at the McCaig Institute, University of Calgary, where she worked on bone segmentation using deep learning. Later, she transitioned to the role of a Postdoctoral Research Fellow at the Radiology & Diagnostic Imaging Department, University of Alberta, leading projects focused on AI-driven MRI biomarker profiling for osteoarthritis.

Contributions and Research Focus

Her research primarily revolves around the intersection of medical imaging and artificial intelligence, with a focus on automated AI biomarker extraction, machine learning, deep learning, and domain adaptation. Dr. Felfeliyan has made significant contributions to the development of novel algorithms and methodologies for medical image segmentation and analysis, particularly in the context of osteoarthritis diagnosis and assessment.

Accolades and Recognition

Dr. Felfeliyan's outstanding contributions have been recognized through numerous honors and awards, including the Alberta Innovates Postdoctoral Recruitment Fellowship, Biomedical Engineering Research Excellence Award, and the AI Week Talent Bursary from the Alberta Machine Intelligence Institute. She was also honored as one of the top 15 young female scientists in Canada at the SCWIST Symposium.

Impact and Influence

Her research outputs, comprising publications in prestigious journals and presentations at international conferences, demonstrate the significant impact of her work on the scientific community. Dr. Felfeliyan's innovative approaches to medical image analysis have the potential to revolutionize clinical diagnosis and treatment planning, ultimately improving patient outcomes and healthcare delivery.

Legacy and Future Contributions

Dr. Felfeliyan's legacy lies in her pioneering work at the intersection of biomedical engineering and artificial intelligence, shaping the future of medical imaging and diagnostics. Her commitment to mentorship and teaching ensures the continuity of her legacy by nurturing the next generation of researchers and engineers. As she continues her academic journey, Dr. Felfeliyan remains dedicated to advancing the frontiers of knowledge and making meaningful contributions to healthcare innovation.

Notable Publications

OMERACT validation of a deep learning algorithm for automated absolute quantification of knee joint effusion versus manual semi-quantitative assessment 2024