Dr. vincebt majanga | Artificial intelligence | Best Researcher Award

Published on by Computer Scientists Awards

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

ORCID

📘 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

  1. Automatic Blob Detection Method for Cancerous Lesions in Unsupervised Breast Histology Images
    📅 2025 | 📰 Bioengineering, 12(4), p.364
    🔎 Cited by: 8 articles

  2. 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

  3. A Survey of Dental Caries Segmentation and Detection Techniques
    📅 2022 | 📰 The Scientific World Journal, 2022
    🔎 Cited by: 21 articles

  4. Automatic Blob Detection for Dental Caries
    📅 2021 | 📰 Applied Sciences, 11(19), p.9232
    🔎 Cited by: 17 articles

  5. Dental Images’ Segmentation Using Threshold Connected Component Analysis
    📅 2021 | 📰 Computational Intelligence and Neuroscience, 2021
    🔎 Cited by: 12 articles

  6. Dropout Regularization for Automatic Segmented Dental Images
    📅 2021 | 📰 Asian Conference on Intelligent Information and Database Systems, Springer
    🔎 Cited by: 6 articles

  7. A Deep Learning Approach for Automatic Segmentation of Dental Images
    📅 2019 | 📰 MIKE 2019, Springer
    🔎 Cited by: 18 articles

  8. Component Analysis
    📅 2025 | 📰 WIDECOM 2024, Vol. 237, p.139, Springer Nature
    🔎 Cited by: 2 articles

 

Mr. Lurui Wang | Machine Learning | Best Researcher Award

Published on by Computer Scientists Awards

Mr. Lurui Wang | Machine Learning | Best Researcher Award

Mr. Lurui Wang, Univeristy of toronto Mind lab, Canada.

Lurui Wang is a passionate and innovative researcher in the field of mechanical engineering, with a strong interdisciplinary interest in robotics, artificial intelligence, and sensor technologies. Currently pursuing his Bachelor of Science in Mechanical Engineering at the University of Toronto, he combines practical experience, academic excellence, and a drive for impactful innovation. With an impressive GPA of 3.75 and extensive involvement in machine learning and design projects, Lurui has contributed to multiple high-impact research areas such as cold spray coatings, aerosol systems for medical applications, and intelligent object detection models. His leadership skills are evident through various team-led design and AI projects, as well as his industry internship with Baylis Med Tech, where he made significant technical contributions.

Professional Profile

ORCID

🎓 Education Background

Lurui Wang began his academic journey at the University of Toronto in September 2020 and is expected to graduate in April 2025 with a Bachelor of Science in Mechanical Engineering. His curriculum includes key subjects such as Mechanical Engineering Design, Mechatronics, Fluid Mechanics, and Solid Mechanics, enhanced by the Professional Experience Year (PEY Co-op). He also undertook summer courses at Xiamen University in accounting, microeconomics, and macroeconomics, reflecting his interdisciplinary interests.

💼 Professional Experience

Lurui’s hands-on experience spans several high-impact projects and internships. He has been involved in developing deep learning models for acoustic emission sensor data in cold spray coatings, advanced object detection through SparseNetYOLOv8, and designing heater systems for aerosol deposition studies. Notably, at Baylis Med Tech, he served as an Equipment Engineer, leading the design of a cable coiling machine, improving manufacturing efficiency, and reducing operational costs. He has also led student design projects in robotics, AI traffic signal detection, and mechanical systems such as gearboxes and milling machines, showcasing his engineering versatility.

🏆 Awards and Honors

Lurui Wang’s dedication has been recognized through multiple accolades, including the Certified SolidWorks Professional (CSWP) in 2022 and Associate (CSWA) in 2021. In 2024, he earned a Kaggle Silver Medal in the “Eedi – Mining Misconceptions in Mathematics” competition, ranking among the top 67 out of 1,446 participants, underscoring his strong data science capabilities.

🔬 Research Focus

Lurui’s research focuses on the intersection of mechanical systems, intelligent computation, and biomimicry. His works explore robotic optimization using insect-inspired mechanisms, machine learning integration in engineering systems, sensor fusion for predictive manufacturing, and vision-based detection models using YOLO architecture enhancements. His projects aim to address real-world challenges in autonomous systems, medical technology, and intelligent manufacturing, driven by simulation tools, programming, and algorithmic innovation.

🔚 Conclusion

Lurui Wang stands out as a dynamic and driven early-career researcher, blending engineering design, data science, and real-world application with academic rigor. His proactive approach, technical skillset, and collaborative mindset mark him as a rising talent in the fields of intelligent mechanical systems and applied machine learning.

📚 Top Publications with Notes

  1. Design and Optimization of Monopod Robots for Continuous Vertical Jumping: A Novel Hopping Mechanism Inspired by Froghoppers and Grasshoppers

    • Authors: Suhang Xu, Feihan Li, Lurui Wang, Yujing Fu

    • Published Year: 2024

    • Journal: Proceedings of MLPRAE 2024

    • DOI: 10.1145/3696687.3696695

  2. SparseNetYOLOv8: Integrating Vision Transformers and Dynamic Probing for Enhanced Sparse Object Detection

    • Authors: Lurui Wang, Yanfeng Lyu

    • Published Year: 2024

    • Conference: 2024 International Conference on Computer Vision and Image Processing (CVIP 2024)

    • DOI: 10.1117/12.3058039

  3. A Machine Learning Approach for Predicting Particle Spatial, Velocity, and Temperature Distributions in Cold Spray Additive Manufacturing

    • Authors: Lurui Wang, Mehdi Jadidi, Ali Dolatabadi

    • Published Year: 2025

    • Conference: Applied Sciences

    • DOI: 10.3390/app15126418