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Mr. Debasish Swapnesh Kumar Nayak | Medical Image Analysis | Best Researcher Award

Research scholar at ITER, SOA Deemed to be University, India

Summary:

Mr. Debasish Swapnesh Kumar Nayak is a prolific researcher and innovator in AI for healthcare and biomedical engineering. He has authored 10 SCI publications addressing critical topics like antimicrobial resistance, IoMT for patient monitoring, cancer genomics, LLMs for medical education, and medical image analysis. With a strong focus on fostering inclusivity and collaboration, he has contributed as a core committee member of the IEEE Returning Mother Conference 2024 at IISc Bengaluru. He has reviewed over 15 papers for journals such as IEEE Access and Biomedical Signal Processing and mentored three master’s students in healthcare-focused AI research, nurturing impactful innovations.

Professional Profile:

👩‍🎓Education:

  • Ph.D. (Ongoing):
    • Field: Computer Science and Engineering
    • Institution: Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar, India
    • Department: FET-ITER
  • Master’s Degree:
    • Field: Computer Science (Specialization not explicitly mentioned; presumed related to AI/Software Development)
  • Bachelor’s Degree:
    • Field: Computer Science and Engineering

Professional Experience:

Mr. Debasish Swapnesh Kumar Nayak has amassed 13 years of expertise in teaching, research and development, and software development. Currently pursuing his Ph.D. at Siksha ‘O’ Anusandhan (Deemed to be) University, he has contributed significantly to academia and industry through his research and publications. He has served as a core committee member and chair for IEEE RMC-2024 and ICAIHC-2025, showcasing his leadership and organizational skills. Mr. Nayak has published over 50 research papers in prestigious SCIE and Scopus-indexed journals and conferences. His innovative approach extends to mentoring graduate students and collaborating on projects in AI for healthcare, biomedical research, and IoT applications, making impactful contributions to these transformative fields.

Research Interests:

His research focuses on leveraging AI in healthcare, specifically in areas like deep learning for antimicrobial resistance analysis, cancer biology, biomedical engineering, IoMT (Internet of Medical Things), remote patient monitoring, and data mining.

Author Metrics:

  • Citation Index: 182 citations (Google Scholar) and 109 citations (Scopus).
  • Publications: 16 articles in SCI/Scopus-indexed journals.
  • Patents: Four published, with one under process.
  • Editorial Appointments: Reviewed over 15 papers for reputed journals.
  • Collaborations: Engaged in six research collaborations.
  • Memberships: Active member of IEEE.

Top Noted Publication:

“En-MinWhale: An ensemble approach based on MRMR and Whale optimization for Cancer diagnosis”

  • Authors: A. Panigrahi, A. Pati, B. Sahu, M. N. Das, D. S. K. Nayak, G. Sahoo, S. Kant
  • Journal: IEEE Access
  • Volume: 11
  • Pages: 113526-113542
  • Year: 2023
  • Citations: 32
  • Summary: This paper proposes En-MinWhale, an ensemble-based method integrating Minimum Redundancy Maximum Relevance (MRMR) and Whale Optimization for effective cancer diagnosis, demonstrating enhanced accuracy and robustness in gene selection for microarray data.

“Interpretation of optimized hyperparameters in associative rule learning using eclat and apriori”

  • Authors: D. Mohapatra, J. Tripathy, K. K. Mohanty, D. S. K. Nayak
  • Conference: 2021 5th International Conference on Computing Methodologies and Communication
  • Year: 2021
  • Citations: 19
  • Summary: The study explores the optimization of hyperparameters in associative rule learning using Eclat and Apriori algorithms, presenting an analytical framework for interpreting optimized rules in diverse datasets.

“Gene selection and enrichment for microarray data—a comparative network-based approach”

  • Authors: D. S. K. Nayak, S. Mahapatra, T. Swarnkar
  • Book Chapter: Progress in Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2018
  • Year: 2018
  • Citations: 18
  • Summary: This work introduces a comparative network-based methodology for gene selection and enrichment analysis in microarray data, highlighting its applicability in precision medicine and bioinformatics.

“Radiological differential diagnoses based on cardiovascular and thoracic imaging patterns: perspectives of four large language models”

  • Authors: P. K. Sarangi, A. Irodi, S. Panda, D. S. K. Nayak, H. Mondal
  • Journal: Indian Journal of Radiology and Imaging
  • Volume: 34 (02)
  • Pages: 269-275
  • Year: 2024
  • Citations: 16
  • Summary: This paper evaluates the diagnostic perspectives of four large language models in interpreting cardiovascular and thoracic imaging patterns, highlighting their potential in radiological differential diagnosis.

“Predicting pediatric appendicitis using ensemble learning techniques”

  • Authors: A. Pati, A. Panigrahi, D. S. K. Nayak, G. Sahoo, D. Singh
  • Journal: Procedia Computer Science
  • Volume: 218
  • Pages: 1166-1175
  • Year: 2023
  • Citations: 15
  • Summary: This research applies ensemble learning techniques to predict pediatric appendicitis, showcasing improvements in diagnostic accuracy and clinical decision-making processes.

Conclusion:

Mr. Debasish Swapnesh Kumar Nayak is a highly qualified and deserving candidate for the Best Researcher Award, with a robust track record of impactful publications, innovative patents, and interdisciplinary contributions. His work in AI for healthcare demonstrates a significant societal impact, positioning him as a leading researcher in this transformative domain.

While his research achievements are commendable, he could further strengthen his case by enhancing global collaborations, diversifying his research portfolio, and increasing his participation in high-profile international events. Overall, his credentials and contributions make him a strong contender for the award, especially in fields intersecting AI, healthcare, and biomedical engineering.

 

Debasish Swapnesh Kumar Nayak | Medical Image Analysis | Best Researcher Award

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