Basil Duwa| Machine learning | Best Researcher Award

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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.

Manijeh Emdadi | Artificial Intelligence | Best Researcher Award

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Dr. Manijeh Emdadi | Artificial Intelligence | Best Researcher Award

Research Fellow at Islamic Azad University Science and Research Branch, Iran📖

Dr. Manijeh Emdadi is an accomplished Data Scientist and AI Specialist with 8 years of experience in designing, developing, and deploying machine learning models and data-driven solutions. Currently pursuing her Ph.D. in Artificial Intelligence at the Islamic Azad University, Tehran, her research focuses on exploring explainable AI models for healthcare decision support systems. Dr. Emdadi has a robust background in machine learning, neural networks, and deep learning, and she actively collaborates with cross-disciplinary teams to develop innovative AI solutions.

Profile

Scopus Profile

Google Scholar Profile

Education Background🎓

  • Ph.D. in Artificial Intelligence (In Progress)
    Islamic Azad University Science and Research Branch, Tehran, Iran
    Research Focus: Exploring Explainable AI Models for Healthcare Decision Support Systems
  • Master of Science in Data Science / Artificial Intelligence
    Islamic Azad University Qazvin Branch, Qazvin, Iran
    Thesis: Optimizing Neural Network Architectures for Image Recognition Tasks
  • Bachelor of Science in Computer Engineering
    Iran University of Science and Technology (IUST), Tehran, Iran
    Relevant Courses: Advanced Algorithms

Professional Experience🌱

Dr. Emdadi has a strong professional background as a Data Scientist, collaborating with cross-functional teams to integrate predictive analytics into business workflows. Her expertise spans programming in Python, SQL, and Java, as well as working with data science tools such as Pandas, NumPy, Scikit-Learn, TensorFlow, and PyTorch. Additionally, she has experience deploying AI/ML models on cloud platforms like Google Cloud. She also serves as a teaching assistant for graduate-level courses on deep learning, sharing her knowledge and expertise with the next generation of AI professionals.

Research Interests🔬

Dr. Emdadi’s primary research interests lie in the intersection of Artificial Intelligence, Machine Learning, and healthcare applications. She is particularly focused on exploring explainable AI models for decision support systems in healthcare, using machine learning and neural networks to solve complex problems in medical data analysis. Her research also includes advancements in deep learning and reinforcement learning, and she is dedicated to creating innovative AI solutions with real-world applications.

Author Metrics

Dr. Manijeh Emdadi has made significant contributions to the academic field, particularly in the domains of Artificial Intelligence, Machine Learning, and healthcare applications. She has authored several impactful publications in high-ranking journals, focusing on areas such as predictive modeling, explainable AI, and healthcare decision support systems. Notable works include her study on “Introducing effective genes in lymph node metastasis of breast cancer patients using SHAP values based on the mRNA expression data,” published in Plos One (2024), and her exploration of grid synchronization methods in power converters, published in Electrical Engineering (2023). Additionally, Dr. Emdadi has authored research on key molecular mechanisms in papillary thyroid carcinoma and developed advanced AI models for predicting cancer metastasis. Her work has been well-received in both the academic and industry sectors, reflecting her expertise in applying AI and machine learning techniques to solve real-world challenges. Her research continues to have a notable impact, especially in healthcare, where her AI-driven models aim to advance personalized medicine and decision support systems.

Publications Top Notes 📄

1. “Introducing effective genes in lymph node metastasis of breast cancer patients using SHAP values based on the mRNA expression data”

  • Authors: SZ Vahed, SMH Khatibi, YR Saadat, M Emdadi, B Khodaei, MM Alishani, et al.
  • Journal: Plos One
  • Volume: 19
  • Issue: 8
  • Article Number: e0308531
  • Year: 2024
  • DOI: 10.1371/journal.pone.0308531
  • Summary: This paper applies SHAP (Shapley Additive Explanations) values to identify genes associated with lymph node metastasis in breast cancer patients, utilizing mRNA expression data for enhanced model interpretability.

2. “D-estimation method for grid synchronization of single-phase power converters: analysis, linear modeling, tuning, and comparison with SOGI-PLL”

  • Authors: H Sepahvand, M Emdadi
  • Journal: Electrical Engineering
  • Year: 2023
  • Summary: The study proposes a D-estimation method for grid synchronization in single-phase power converters. It provides a detailed analysis, linear modeling, tuning methods, and compares the performance with the traditional SOGI-PLL (Second-Order Generalized Integrator Phase-Locked Loop).

3. “Uncovering key molecular mechanisms in the early and late-stage of papillary thyroid carcinoma using association rule mining algorithm”

  • Authors: SM Hosseiniyan Khatibi, S Zununi Vahed, H Homaei Rad, M Emdadi, et al.
  • Journal: Plos One
  • Volume: 18
  • Issue: 11
  • Article Number: e0293335
  • Year: 2023
  • DOI: 10.1371/journal.pone.0293335
  • Summary: This research uses association rule mining to explore the molecular mechanisms involved in papillary thyroid carcinoma at various stages. The findings aim to reveal biomarkers for early diagnosis and targeted treatment strategies.

4. “Graph Fuzzy Attention Network Model for Metastasis Prediction of Prostate Cancer Based on mRNA Expression Data”

  • Journal: International Journal of Fuzzy Systems
  • Year: 2024
  • Summary: This paper introduces a Graph Fuzzy Attention Network (GFAN) model for predicting metastasis in prostate cancer using mRNA expression data. The model leverages the strengths of fuzzy logic and graph-based learning for enhanced prediction accuracy.

5. “Load-aware Channel Assignment and Routing in Clustered Multichannel and Multi-radio Mesh Networks”

  • Authors: M Emdadi, MR Shahsavari, MD TakhtFouladi
  • Year: Unspecified
  • Summary: This work discusses the optimization of channel assignment and routing protocols in clustered multi-channel and multi-radio mesh networks, with a focus on load-awareness for efficient resource utilization and network performance.

Conclusion

Dr. Manijeh Emdadi is exceptionally well-suited for the Best Researcher Award due to her pioneering work in artificial intelligence and its application to healthcare decision-making systems. Her strong academic background, innovative research, and commitment to advancing AI for healthcare make her an outstanding candidate. By enhancing collaborations with the industry and expanding her research scope, Dr. Emdadi can continue to build upon her current achievements and make even more significant contributions to both academic and real-world advancements in AI and healthcare.

In summary, Dr. Emdadi’s impressive AI expertise, innovative healthcare solutions, and strong academic contributions strongly align with the qualities sought for the Best Researcher Award.