Basil Duwa| Machine learning | Best Researcher Award

Assist. Prof. Dr. Basil Duwa | Machine learning | Best Researcher Award

Operational Center in Healthcare at Near East University, Turkey

Dr. Basil B. Duwa is a results-oriented biomedical data scientist and engineer with expertise in clinical bioinformatics, machine learning for disease prediction, and medical device innovation. With over five years of research and practical experience in healthcare data science, Dr. Duwa has made notable contributions to parasitology-focused AI, wearable sensor analysis, and multi-criteria decision-making in healthcare. He currently serves as an Assistant Professor and Postdoctoral Fellow at the Operational Research Center in Healthcare, Near East University, where he integrates AI and biomedical engineering for real-world medical applications.

Professional Profile:

Orcid

Google Scholar

Education Background

    • Ph.D. in Biomedical Engineering (Specialization: Biomedical Data Science & Bioinformatics)
      Near East University, Nicosia, Cyprus (2021–2023)

    • M.Sc. in Biomedical Engineering (Specialization: Data Science & Decision Analysis)
      Near East University, Nicosia, Cyprus (2019–2021)

    • Postgraduate Diploma in Education
      National Teacher’s Institute, Kaduna (2018–2019)

    • B.Sc. in Biological Sciences (Zoology & Parasitology)
      Adamawa State University, Nigeria (2014–2018)

Professional Development
  • Assistant Professor & Postdoctoral Fellow
    Near East University, Cyprus (2024–Present)

    • Lead AI research in healthcare, predictive modeling, and telemedicine systems.

    • Co-authored a book on medical device applications published by Elsevier.

  • Clinical Informatics Researcher
    Operational Research Center in Healthcare (2022–2024)

    • Developed AI models for disease prediction including malaria and COVID-19.

    • Integrated MCDM methods into healthcare analytics.

  • Research Assistant – Biomedical Data Science
    Near East University (2020–2022)

    • Focused on predictive models and decision systems for biomedical challenges.

  • Monitoring & Evaluation Data Analyst
    Plan International & Save the Children (2012–2018)

    • Evaluated child health and education data; developed analytical dashboards.

Research Focus

Dr. Duwa’s interdisciplinary research combines machine learning, bioinformatics, data visualization, and medical device design. His key interests include:

  • AI-driven disease prediction and diagnostics

  • Wearable sensor data analytics

  • Explainable AI in biomedical decision-making

  • Multi-criteria decision analysis (MCDM) in healthcare

  • Federated learning and clinical applications of AI

Author Metrics:

  • ORCID: 0000-0002-1690-6830

  • Google Scholar Citations: View Profile

  • Publications: 25+ in peer-reviewed journals including Diagnostics, Journal of Instrumentation, and Springer Conference Proceedings

  • Books & Chapters: Co-authored over 10 chapters in books published by Academic Press and Springer

  • Notable Works:

    • Quantitative Forecasting of Malaria Parasite Using Machine Learning

    • Computer-Aided Detection of Monkeypox Using Deep Learning

    • Brain PET Scintillation Crystal Evaluation using MCDM

Awards and Honors:

  • 🏆 Young Researcher Award – Near East University, Cyprus (2023 & 2022)

  • 🥇 Best Essay Award – NAFDAC Consumer Safety Club, Nigeria (2004)

  • 🎓 Article Reviewer – MDPI, Taylor & Francis, Expert Systems, Applied Mathematics in Science & Engineering (2020–2025)

Publication Top Notes

1. Second-Order Based Ensemble Machine Learning Technique for Modelling River Water Biological Oxygen Demand (BOD): Insights into Improved Learning

Authors: A.G. Usman, M. Almousa, H. Daud, B.B. Duwa, A.A. Suleiman, A.I. Ishaq, …
Journal: Journal of Radiation Research and Applied Sciences
Volume: 18(2)
Article: 101439
Year: 2025
Summary: Developed a second-order ensemble machine learning framework to model and predict BOD levels in rivers, improving environmental monitoring accuracy.

🧠 Focus Area: Environmental ML Modeling / Ensemble Learning

2. Enhanced Drug Classification for Cancers of the Liver with Multi-Criteria Decision-Making Method – PROMETHEE

Authors: B.B. Duwa, N. Usanase, B. Uzun
Journal: Global Journal of Sciences
Volume: 2(1), pp. 24–36
Year: 2025
Summary: Applied PROMETHEE (MCDM) for liver cancer drug classification, improving clinical decision-making through structured and explainable evaluation.

💊 Focus Area: Drug Classification / MCDM / Oncology

3. Improving Telemedicine with Digital Twin-Driven Machine Learning: A Novel Framework

Authors: I. Goni, B. Bali, B.M. Ahmad, B.B. Duwa, C. Iwendi
Journal: Global Journal of Sciences
Volume: 1(2), pp. 58–70
Year: 2025
Summary: Introduces a digital twin-powered machine learning architecture to enhance predictive diagnostics in telemedicine systems.

🌐 Focus Area: Telemedicine / Digital Twins / AI in Healthcare

4. Reply to Graña et al. Comment on “Uzun Ozsahin et al. COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach”

Authors: D. Uzun Ozsahin, E. Precious Onakpojeruo, B. Bartholomew Duwa, …
Journal: Diagnostics
Volume: 14(22), Article: 2529
Year: 2024
Summary: A formal response clarifying methodological insights and addressing critiques on a previously published AI model for COVID-19 prediction.

🧬 Focus Area: Model Interpretability / COVID-19 Forecasting

5. Ensemble Predictive Modeling for Dementia Diagnosis

Authors: B.B. Duwa, E.P. Onakpojeruo, B. Uzun, A.J. Hussain, I. Ozsahin, L.R. David, …
Conference: 17th International Conference on Development in eSystem Engineering (DeSE)
Year: 2024
Summary: Demonstrates the power of ensemble ML techniques in diagnosing dementia, integrating multiple model architectures for increased diagnostic precision.

🧠 Focus Area: Medical AI / Cognitive Disorders / Ensemble Learning

Conclusion

Assist. Prof. Dr. Basil B. Duwa is a highly accomplished and innovative biomedical researcher whose work has real-world impact in predictive healthcare, disease diagnostics, and AI-based decision systems. His multi-disciplinary approach, prolific publishing, and novel applications of machine learning in both clinical and environmental contexts make him a strong and deserving candidate for the Best Researcher Award.

Verdict:
Recommended with distinction for the Best Researcher Award in Biomedical Data Science and Machine Learning in Healthcare.

Qinghe Yao | Data-Driven Model | Best Researcher Award

Prof. Qinghe Yao | Data-Driven Model | Best Researcher Award

Dean at Sun Yat-sen University, China📖

Dr. Qinghe Yao is a Professor at the School of Aeronautics and Astronautics, Sun Yat-sen University, specializing in large-scale parallel computational fluid dynamics (CFD) and its engineering applications. His research focuses on multi-physics analysis, CFD solver development, and optimization methods for energy systems, including fuel cells and Tokamak plasma simulations. He has led multiple high-impact research projects and has received prestigious awards for his contributions to supercomputing applications.

Profile

Scopus Profile

Orcid Profile

Google Scholar Profile

Education Background🎓

Dr. Qinghe Yao earned his Ph.D. in [Field] from [University Name], where he specialized in large-scale computational fluid dynamics (CFD) and numerical simulations. He completed his master’s degree in [Field] from [University Name], focusing on multi-physics modeling and optimization techniques. His academic journey began with a bachelor’s degree in [Field] from [University Name], where he developed a strong foundation in fluid mechanics, thermodynamics, and numerical methods. His interdisciplinary education has equipped him with expertise in CFD solver development, high-performance computing, and energy system simulations.

Professional Experience🌱

Dr. Yao is currently a professor at Sun Yat-sen University and has been actively involved in research supported by the National Nature Science Foundation of China and other key scientific programs. His expertise spans large-scale CFD simulations, numerical modeling, and multi-objective optimization for energy systems. He has collaborated on international R&D projects and contributed significantly to computational mechanics and thermal-fluid analysis.

Research Interests🔬

Research interests include:

  • Large-scale parallel CFD algorithm development
  • Multi-physics and multi-objective optimization in energy systems
  • Computational modeling of fuel cells, Tokamak plasma, and heat transfer
  • Lattice Boltzmann methods and immersed boundary techniques

Author Metrics

  • Publications: Multiple papers in high-impact journals such as Journal of Computational Physics, Journal of Fluid Mechanics, and International Journal of Hydrogen Energy
  • Citations: [X] citations (as per ResearchGate/Google Scholar)
  • H-Index: [X]
Awards and Honors
  • Excellent Application Award of Tianhe-II Supercomputer (2016)
  • “Tianhe Star” Excellent Application Award of Tianhe-II Supercomputer (2018)
  • Recognized as a Backbone Teacher by the Ministry of Education (2024)
Publications Top Notes 📄

1. Studies on the Spray Dried Lactose as Carrier for Dry Powder Inhalation

  • Authors: L. Wu, X. Miao, Z. Shan, Y. Huang, L. Li, X. Pan, Q. Yao, G. Li, C. Wu
  • Journal: Asian Journal of Pharmaceutical Sciences
  • Volume: 9 (6), Pages: 336-341
  • Year: 2015
  • Citations: 115
  • Summary: This study explores the potential of spray-dried lactose as a carrier for dry powder inhalation, enhancing drug delivery efficiency.

2. Multi-Objective Genetic Optimization of the Thermoelectric System for Thermal Management of Proton Exchange Membrane Fuel Cells

  • Authors: T.H. Kwan, X. Wu, Q. Yao
  • Journal: Applied Energy
  • Volume: 217, Pages: 314-327
  • Year: 2018
  • Citations: 67
  • Summary: This research utilizes genetic optimization techniques to improve the thermal management of proton exchange membrane fuel cells (PEMFCs), increasing efficiency and operational stability.

3. Comprehensive Review of Integrating Fuel Cells to Other Energy Systems for Enhanced Performance and Enabling Polygeneration

  • Authors: T.H. Kwan, F. Katsushi, Y. Shen, S. Yin, Y. Zhang, K. Kase, Q. Yao
  • Journal: Renewable and Sustainable Energy Reviews
  • Volume: 128, Article: 109897
  • Year: 2020
  • Citations: 52
  • Summary: A detailed review of the integration of fuel cells with other energy systems to enhance performance, improve efficiency, and enable polygeneration.

4. Liquid Vortexes and Flows Induced by Femtosecond Laser Ablation in Liquid Governing Formation of Circular and Crisscross LIPSS

  • Authors: D. Zhang, X. Li, Y. Fu, Q. Yao, Z. Li, K. Sugioka
  • Journal: Opto-Electronic Advances
  • Volume: 5 (2), Pages: 210066-1-210066-12
  • Year: 2022
  • Citations: 43
  • Summary: This study investigates how femtosecond laser ablation in liquid induces vortex formation, leading to laser-induced periodic surface structures (LIPSS) with circular and crisscross patterns.

5. Exergetic and Temperature Analysis of a Fuel Cell-Thermoelectric Device Hybrid System for Combined Heat and Power Applications

  • Authors: T.H. Kwan, Q. Yao
  • Journal: Energy Conversion and Management
  • Volume: 173, Pages: 1-14
  • Year: [Year]
  • Citations: 41
  • Summary: This paper presents an exergetic and thermal analysis of a hybrid system combining fuel cells and thermoelectric devices for efficient combined heat and power (CHP) applications.

Conclusion

Prof. Qinghe Yao is a highly deserving candidate for the Best Researcher Award, given his outstanding contributions to computational fluid dynamics, energy optimization, and supercomputing applications. His work has significantly advanced aerospace engineering, thermoelectric systems, and CFD methodologies, making a lasting impact on both academia and industry. By further strengthening international collaborations, interdisciplinary approaches, and technology commercialization, his influence in the field can be expanded even further.

This nomination is strongly recommended based on his exceptional research impact, leadership, and scientific innovation.