Dr. vincebt majanga | Artificial intelligence | Best Researcher Award
Post doctoral research fellow, university of south africa, South Africa.
Dr. Vincent Idah Majanga is a dynamic and passionate researcher in Artificial Intelligence (AI), with over a decade of impactful experience in developing cutting-edge algorithms to solve complex real-world problems. His primary expertise lies in machine learning, deep learning, neural network optimization, and computer vision—especially for medical imaging and diagnostic tasks. Dr. Majanga is proficient in Python and Java, and his interdisciplinary skills extend to computer-aided diagnostics, simulation and modeling, computer forensics, and networking. A devoted academician and mentor, he has served in teaching and research capacities across renowned institutions in Kenya and South Africa. His current role as a Postdoctoral Researcher at the University of South Africa (UNISA) underlines his continued contributions to AI-driven healthcare solutions and intelligent systems.
Publication Profile
Education Background
Dr. Majanga completed his Ph.D. in Computer Science from the University of KwaZulu-Natal (2018–2022), focusing on dental image segmentation and AI-based diagnostic systems. He holds an MSc in Computer Science from the University of Nairobi (2012–2014), and a BSc in Computer Science (Upper Second Class) from Kabarak University (2009–2011). He also studied Computer Engineering at Moi University (2005–2008, credit transferred), and attended Nairobi School for his secondary education (2001–2004). His academic foundation forms the bedrock of his AI-driven research innovations.
Professional Experience
Dr. Majanga is currently a Postdoctoral Researcher at UNISA (Dec 2023–Present), where he works on deep learning, neural networks, transfer learning, and model optimization in image processing. He is also a part-time lecturer at Masinde Muliro University of Science and Technology since 2022. Previously, he served as an Assistant Lecturer at Laikipia University (2015–2023), contributing to curriculum development and student supervision. He has also lectured part-time at JKUAT Nakuru Campus, Dedan Kimathi University, and Kabarak University. Across these roles, he has consistently contributed to high-impact teaching, curriculum development, and academic mentorship.
Awards and Honors
Dr. Majanga has earned recognition through certifications in Research Ethics from the Clinical Trials Centre at The University of Hong Kong 
, completing three modules between March and April 2024—Introduction to Research Ethics, Research Ethics Evaluation, and Informed Consent. These certifications affirm his commitment to ethical research standards and responsible conduct in AI healthcare studies.
Research Focus
Dr. Majanga’s research focuses on Artificial Intelligence applications in medical imaging and diagnostics, with a specialization in deep learning, computer vision, and unsupervised segmentation. His significant contributions include blob detection and component analysis techniques for identifying cancerous lesions and dental caries in radiographs. His Ph.D. research and publications highlight strong applications of active contour models, connected component analysis, and dropout regularization in healthcare AI systems.
Conclusion
Dr. Vincent Idah Majanga is a dedicated AI researcher and academician with a rich educational and professional background that aligns with transformative applications of artificial intelligence in medical diagnostics. His teaching, ethical research approach, and cross-continental academic presence have made him a valuable contributor to the global AI and computer science communities.
Top Publications Highlights
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Automatic Blob Detection Method for Cancerous Lesions in Unsupervised Breast Histology Images
2025 | 
Bioengineering, 12(4), p.364
Cited by: 8 articles -
Active Contours Connected Component Analysis Segmentation Method of Cancerous Lesions in Unsupervised Breast Histology Images
2025 | Bioengineering, 12(6), p.642 Cited by: 5 articles -
A Survey of Dental Caries Segmentation and Detection Techniques
2022 | The Scientific World Journal, 2022 Cited by: 21 articles -
Automatic Blob Detection for Dental Caries
2021 | Applied Sciences, 11(19), p.9232 Cited by: 17 articles -
Dental Images’ Segmentation Using Threshold Connected Component Analysis
2021 | Computational Intelligence and Neuroscience, 2021 Cited by: 12 articles -
Dropout Regularization for Automatic Segmented Dental Images
2021 | Asian Conference on Intelligent Information and Database Systems, Springer Cited by: 6 articles -
A Deep Learning Approach for Automatic Segmentation of Dental Images
2019 | MIKE 2019, Springer Cited by: 18 articles -
Component Analysis
2025 | WIDECOM 2024, Vol. 237, p.139, Springer Nature Cited by: 2 articles
His skills span Java-based Android development, data integration, and advanced problem-solving in software, alongside a versatile understanding of multiple programming languages, including Python and Kotlin. Slimane has applied his AI knowledge to impactful projects, even authoring a research paper. 
, Slimane applied machine learning to improve battery health prediction, developing models that track and identify factors contributing to battery degradation. At CNAS in Algeria, he gained practical insights into network database applications and web app development. 
As a freelancer on Upwork, Slimane developed Android applications and managed web back-end services, demonstrating his versatility in real-world projects.
His projects include sentiment analysis and fake news detection in Arabic language datasets, alongside health management applications that leverage data-driven insights to enhance service quality. His work in battery health prediction highlights his proficiency in machine learning model development and evaluation.
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