Md Raihan Subhan | Artificial Intelligence | Best Researcher Award

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Mr. Md Raihan Subhan | Artificial Intelligence | Best Researcher Award

PhD at Kumoh National Institute of Technology | South Korea

Md Raihan Subhan is a dedicated researcher in IT Convergence with a strong focus on intrusion detection systems, anomaly detection, AI-driven security, and meta-governance security. His research integrates reinforcement learning, deep learning, blockchain, and natural language processing to address complex cybersecurity challenges. His recent work emphasizes the convergence of blockchain technology with marine tactical systems to design advanced intrusion detection systems. He has also contributed to projects in digital twin-based warehouse logistics for military applications and anomaly detection in manufacturing systems. His publications in leading journals and conferences demonstrate his ability to advance intelligent systems and cybersecurity using innovative approaches.

Publication Profile 

Scopus

Orcid

Google Scholar

Educational Background 

He holds a Master of Engineering in IT Convergence from Kumoh National Institute of Technology, where he specialized in anomaly detection and intrusion detection, achieving an outstanding academic record. His master’s thesis explored blockchain-aided intrusion detection in marine tactical networks using reinforcement learning. Currently, he is pursuing a Ph.D. in IT Convergence Engineering with the same specialization, further strengthening his expertise in intelligent cybersecurity solutions. He also holds a Bachelor’s degree in Computer Science and Engineering from the International University of Business Agriculture and Technology, where he gained foundational knowledge in software engineering, networks, operating systems, and data management.

Professional Experience 

Raihan has experience as a Research Assistant in prominent IT convergence laboratories, where he worked on distributed systems, federated learning, and machine learning applications for cybersecurity. His research activities include developing anomaly detection models, implementing them on real-time edge devices, and integrating blockchain and reinforcement learning techniques for system optimization and security. Prior to his research career, he served as an Academic Counselor in Computer Science, supporting students with academic guidance, research assistance, and career mentoring. This role enhanced his communication and teaching skills while contributing to curriculum development and interdisciplinary research.

Research Interests 

His research interests include intrusion detection systems, anomaly detection, federated learning, blockchain integration, reinforcement learning, AI-driven security, and cybersecurity in industrial and military applications. He is particularly interested in distributed and continual learning approaches for intelligent systems and the application of digital twin technologies in logistics and defense systems.

Awards and Honors 

He has contributed to government-funded research projects in South Korea, including AI implementation on custom edge devices, digital twin solutions for military logistics, and AI-driven optimization in manufacturing execution systems. His work has been recognized through publications in high-impact journals such as IEEE Transactions on Network and Service Management and collaborations with international researchers in advanced IT security and intelligent systems.

Research Skills 

Raihan is proficient in multiple programming languages, including C, C++, Python, R, Java, and Solidity, and has strong experience with simulation and development tools such as MATLAB, TensorFlow, PyTorch, and Remix IDE. He has hands-on experience implementing blockchain consensus algorithms, deploying deep learning models on platforms such as Jetson Nano and Raspberry Pi, and designing AI-based anomaly detection systems. His expertise spans distributed computing, federated learning, digital twins, and AI integration in real-world industrial and defense applications.

Publications 

  1. Meta-governance: Blockchain-driven metaverse platform for mitigating misbehavior using smart contract and AI
    Cited by: 17
    Year: 2024

  2. Comparative study of cryptocurrency wallet security: A hybrid, hot, and cold wallet approach
    Cited by: 3
    Year: 2023

  3. A Blockchain-Based System for Efficient, Traceable, and Transparent Maritime Logistics
    Cited by: 2
    Year: 2024

  4. Federated semi-supervised digital twin for enhanced human-machine interaction in Industry 5.0
    Cited by: 1
    Year: 2024

  5. Elevating transparency in global maritime logistics through blockchain technology
    Cited by: 1
    Year: 2024

Conclusion 

Through his academic achievements, research experience, and innovative contributions, Md Raihan Subhan has established himself as a promising researcher in the field of IT convergence and cybersecurity. His ability to merge advanced technologies such as blockchain, AI, and reinforcement learning to solve real-world security challenges highlights his potential to significantly advance the future of intelligent systems and secure digital infrastructures.

Sushil Kumar | Machine Learning | Best Researcher Award

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Dr. Sushil Kumar | Machine Learning | Best Researcher Award

Assistant Professor at Central University of Haryana, India

Dr. Sushil Kumar is an Assistant Professor in the Department of Computer Science and Engineering at the Central University of Haryana, having joined on December 2, 2022. With a rich experience of 19 years in teaching, he specializes in Information Retrieval, Machine Learning, and Distributed Computing. Dr. Kumar holds a B.Tech, M.Tech, and Ph.D. in Computer Science and Engineering. He has published 7 papers in international journals and 1 book chapter, and has guided 16 Master’s students in their research. He has actively participated in 25 seminars and conferences, and organized 5 academic events. In addition, he has been recognized with the Youth Red Cross Award from the Honorable Governor of Haryana for 2016-17 and 2019-20. Currently, he also serves as the NBA Co-ordinator and NAAC Co-ordinator at the university.

Publication Profile : 

Google Scholar

Education 🎓

Dr. Sushil Kumar holds a B.Tech, M.Tech, and Ph.D. in Computer Science and Engineering, equipping him with a solid foundation in the field of technology and research.

Professional Experience💼

Assistant Professor at Central University of Haryana since 02-12-2022
With 19 years of teaching experience, Dr. Sushil Kumar has been dedicated to nurturing young minds in the area of computer science. His expertise in Information Retrieval, Machine Learning, and Distributed Computing has shaped his teaching methodology. While his focus remains on academia, he has not been involved in industry work yet. He has also taken up additional responsibilities as NBA Co-ordinator and NAAC Co-ordinator, ensuring quality assurance and accreditation standards in the department.

Research Interests 🔬

🔍 Information Retrieval
🤖 Machine Learning
🌐 Distributed Computing

Dr. Sushil Kumar’s research interests are focused on the areas of Information Retrieval, where he aims to improve search and data retrieval systems, Machine Learning, and the development of efficient algorithms for Distributed Computing systems.

Publications Top Notes 📚

  1. Kumar, S., Aggarwal, M., Khullar, V., Goyal, N., Singh, A., & Tolba, A. (2023). Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification. Agriculture, 13(5), 23.
  2. Kumar, S., & Bhatia, K. K. (2020). Semantic similarity and text summarization-based novelty detection. SN Applied Sciences, 2(3), 332.
  3. Kumar, S., & Chauhan, N. (2012). A context model for focused web search. International Journal of Computer Technology, 2(3).
  4. Gupta, C., Khullar, V., Goyal, N., Saini, K., Baniwal, R., Kumar, S., & Rastogi, R. (2023). Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram. Diagnostics, 14(1), 43.
  5. Kumar, S., & Bhatia, K. K. (2019). Clustering-based approach for novelty detection in text documents. Asian Journal of Computer Science and Technology, 8(2), 116-121.
  6. Dasari, K., Srikanth, V., Veramallu, B., Kumar, S. S., & Srinivasulu, K. (2014). A novelty approach of symmetric encryption algorithm. Proceedings of the International Conference on Information Communication and Embedded Systems (ICICES).
  7. Kumar, S., & Anand, S. (2006). Implementing Shared Data Services (SDS): A Proposed Approach. 2006 IEEE International Conference on Services Computing (SCC’06), 365-372.
  8. Singh, S., Kundra, H., Kundra, S., Pratima, P. V., Devi, M. V. A., Kumar, S., & Hassan, M. (2024). Optimal trained ensemble of classification model for satellite image classification. Multimedia Tools and Applications, 1-22.
  9. Kumar, S., & Bhatia, K. K. (2018). Document-to-Sentence Level Technique for Novelty Detection. In Speech and Language Processing for Human-Machine Communications: Proceedings (pp. xx-xx).
  10. Chawla, M., Panda, S. N., Khullar, V., Kumar, S., & Bhattacharjee, S. B. (2024). A lightweight and privacy-preserved federated learning ecosystem for analyzing verbal communication emotions in identical and non-identical databases. Measurement: Sensors, 34, 101268.
  11. Kumar, S. S. (2023). System Oriented Social Scrutinizer: Centered Upon Mutual Profile Erudition. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9), 2007–2017.
  12. Kumar, S. (2021). Design of novelty detection techniques for optimized search engine results. JC Bose University.
  13. Ishuka, S. K., & Bhatia, K. K. (2019). A Novel Approach for Novelty Detection Using Extractive Text Summarization. Journal of Emerging Technologies and Innovative Research, 6(6), 141-154.
  14. Pooja, K. K. B., & Kumar, S. (2019). Hashing and Clustering Based Novelty Detection. SSRG International Journal of Computer Science and Engineering, 6(6), 1-9.
  15. Kumar, S., & Bhatia, K. K. (2019). Clustering Based Approach for Novelty Detection in Text Documents. Asian Journal of Computer Science and Technology, 8(2), 121-126.