Prof. Neelamadhab Padhy | Soft Computing | Best Researcher Award

Prof. Neelamadhab Padhy | Soft Computing | Best Researcher Award

Prof. Neelamadhab Padhy, GIET University, India

Prof. Padhy is a distinguished academic with a Doctor of Letters (D.Litt.) from SouthAsian University, South Korea, and a Ph.D. in Computer Science and Engineering from Sri Satya Sai University of Technology and Medical Science. His research, which spans software engineering, machine learning, and software quality, has led to significant advancements, particularly in software metrics, refactoring, and reusability prediction. With over two decades of teaching experience, Prof. Padhy has mentored numerous students and authored influential publications, including highly cited works in data mining and brain tumor classification. His excellence in research and education has been recognized through various awards, including the Young Researcher award and Best Paper awards at international conferences.

Professional Profile:

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Suitability for the Award

Prof. Neelamadhab Padhy is a highly suitable candidate for the Research for Best Researcher Award. His extensive research in software quality, machine learning, and software engineering, combined with his notable academic achievements and awards, make him a distinguished figure in his field. His work not only advances academic knowledge but also has practical applications in improving software systems, making significant contributions to both academia and industry.

Academic Credentials:

Prof. Padhy holds a Doctor of Letters (D.Litt.) from SouthAsian University, South Korea, awarded in 2019, as an honorary recognition, highlighting his distinguished contributions to the field.

His Ph.D. in Computer Science and Engineering from Sri Satya Sai University of Technology and Medical Science (2015-2018) focused on Software Quality, a critical area in computer science, where his research has made significant advancements.

He also holds advanced degrees in Computer Science, including an M.Tech from Berhampur University and a Master of Computer Application from Biju Patnaik Technical University, solidifying his foundational expertise in the field.

Research Interests and Contributions:

Prof. Padhy’s research spans essential areas of software engineering, including Software Metrics, Software Quality, Machine Learning, Software Refactoring, Software Reusability Prediction, and Software Cost Estimation. His focus on improving software quality and leveraging machine learning techniques positions his work at the intersection of traditional software engineering and modern AI-driven approaches.

His influential publication, “The survey of data mining applications and feature scope,” has been cited 343 times, underscoring its impact on the academic community and its relevance in the evolving field of data mining.

His more recent work on “Brain tumor classification using dense efficient-net,” which has garnered 129 citations, reflects his continued contributions to cutting-edge research in applying machine learning to critical real-world problems.

Teaching and Mentoring:

With a teaching career spanning over two decades, Prof. Padhy has taught a wide range of subjects, from Database Management Systems and Data Mining to Secure Software Engineering and Statistical Machine Learning. His role as an educator has been recognized with the Best Faculty award in 2013 at GIET, Gunupur.

He has served as a Lecturer, Assistant Professor, and Associate Professor at prestigious institutions, mentoring numerous students and contributing to the academic community through his teaching and research supervision.

Awards and Recognition:

Prof. Padhy has received several awards that attest to his excellence in research and teaching, including the Young Researcher award and Best Paper award at the ICRIET 2016 conference.

His 1st prize award for a research paper presented at the 3rd IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON 2016) at IIT Varanasi further highlights his research prowess.

Publication Top Notes:

  1. Title: The Survey of Data Mining Applications and Feature Scope
    • Year: 2012
    • Cited by: 343
  2. Title: Brain Tumor Classification Using Dense Efficient-Net
    • Year: 2022
    • Cited by: 129
  3. Title: Malicious Node Detection Using Heterogeneous Cluster Based Secure Routing Protocol (HCBS) in Wireless Adhoc Sensor Networks
    • Year: 2020
    • Cited by: 94
  4. Title: Fundamentals, Present and Future Perspectives of Speech Enhancement
    • Year: 2021
    • Cited by: 82
  5. Title: Heart disease prediction by using novel optimization algorithm: A supervised learning prospective
    • Year: 2021
    • Cited by: 71