Mihir Parekh | Machine Learning | Best Researcher Award

Mr. Mihir Parekh | Machine Learning | Best Researcher Award

Research Scholar at Nirma University, India

Mihir Parekh is a passionate and dynamic cybersecurity enthusiast with a strong foundation in computer science, data analytics, and secure system design. With a proven track record of combining machine learning, blockchain, and cybersecurity for impactful solutions, he brings a multidisciplinary approach to solving complex technological challenges. Mihir has demonstrated excellence in both academic and industrial settings, contributing to innovative research in secure systems and earning accolades through peer-reviewed publications.

Publication ProfileΒ 

Google Scholar

Educational Background πŸŽ“

  • M.Tech in Computer Science and Engineering (Cyber Security)
    Nirma University, Ahmedabad
    08/2022 – 06/2024 | CGPA: 9.21
    Focus: Cybersecurity, Blockchain, Data Analysis, Machine Learning

  • Bachelor of Engineering in Information Technology
    G.H. Patel College of Engineering and Technology, Vallabh Vidyanagar
    07/2018 – 05/2022 | CGPA: 8.22

Professional Experience πŸ’Ό

  • Data Analyst
    Contrado Imaging India Pvt. Ltd., Ahmedabad
    06/2023 – 10/2023

    • Performed data cleaning and preprocessing using Python.

    • Developed SQL queries to fetch and analyze data.

    • Used Kibana and Elasticsearch for data visualization.

  • Business Process Analyst
    Kevit Technologies, Rajkot
    12/2021 – 07/2022

    • Designed chatbot workflows and managed client-specific SRS and change requests.

    • Handled software testing, project planning, and requirement gathering for custom chatbot solutions.

Research Interests πŸ”¬

  • Cybersecurity and Digital Forensics

  • Blockchain Applications and Cryptographic Protocols

  • Machine Learning and Deep Learning

  • Federated Learning & Secure Data Sharing

  • Anomaly Detection and Fraud Prevention

  • Secure Industrial IoT Systems

Awards and HonorsπŸ†βœ¨

  • πŸ† Published Journal Paper:
    Blockchain Forensics to Prevent Cryptocurrency Scams
    Computers & Electrical Engineering (Impact Factor: 5.5)

  • πŸ† Conference Presentation:
    Federated Learning-based Secure Data Dissemination Framework for IIoT Systems
    IEEE ICBDS 2024

  • πŸ† Journal Publication:
    Decentralized Data-Driven Analytical Framework for Ship Fuel Oil Consumption
    Ain Shams Engineering Journal

  • πŸŽ–οΈ Infineon Hackathon Finalist – AES-128 Cryptanalysis Challenge

Conclusion🌟

Mihir Parekh exemplifies the qualities of a modern-day technologist with a passion for innovation, research, and real-world problem solving. His academic rigor, hands-on experience in cybersecurity and AI, and commitment to continuous learning position him as a promising contributor to the field of secure intelligent systems. Eager to collaborate and make an impact, Mihir is actively seeking opportunities that align with his vision of building secure, intelligent, and efficient digital ecosystems.

Publications πŸ“š

πŸ“˜ Parekh, M., Jadav, N. K., Tanwar, S., Pau, G., Alqahtani, F., & Tolba, A. (2025). ANN and blockchain-orchestrated decentralized data-driven analytical framework for ship fuel oil consumption. Applied Ocean Research, 158, 104553.
πŸ”— https://doi.org/10.xxxxx/aor.2025.104553
πŸ“Š Keywords: Artificial Neural Networks, Blockchain, Maritime Fuel Analytics


πŸ“• Parekh, M., Jadav, N. K., Pathak, L., Tanwar, S., & Yamsani, N. (2024). Federated Learning-based Secure Data Dissemination Framework for IIoT Systems Underlying 5G. In 2024 IEEE International Conference on Blockchain and Distributed Systems (pp. xx–xx). IEEE.
πŸ“‘ Keywords: Federated Learning, 5G, IIoT, Cybersecurity
πŸ“ Conference Paper


πŸ“„ Parekh, M. (2024). Decentralized Data-Driven Analytical Framework for Ship Fuel Oil Consumption. Institute of Technology.
πŸ›οΈ Institutional publication / Thesis
🌐 Focus: Data Analytics, Maritime Efficiency


Chao Zhang | Machine Learning | Best Researcher Award

Prof. Chao Zhang | Machine Learning | Best Researcher Award

Professor at Shanghai University, China

Professor Chao Zhang is a distinguished academic and researcher specializing in mechanical engineering, particularly in tribology and engine component wear. With an extensive career spanning multiple prestigious institutions, including Shanghai University and Northwestern University, he has significantly contributed to the field through research, publications, and technical committee roles. His expertise integrates classical tribology with modern computational techniques such as machine learning and quantum chemical molecular dynamics.

Publication ProfileΒ 

Scopus

Educational Background πŸŽ“

  • Bachelor’s Degree: Mechanical Engineering, Shanghai Railway University (1983)

  • Master’s Degree: Mechanical Engineering, Shanghai Internal Combustion Engine Research Institute (1989)

  • Ph.D.: Mechanical Engineering, Shanghai University (1997)

Professional Experience πŸ’Ό

  • Senior Research Associate (1997–2002): Northwestern University, USA (Worked with Profs. H.S. Cheng and Qian Wang)

  • Professor:

    • Tongji University, China

    • Shanghai University, China

    • Kunming University of Science and Technology, China

  • Technical Committee Member: Engines and Powertrains, International Federation for the Promotion of Mechanism and Machine Science (IFToMM)

Research Interests πŸ”¬

  • Tribology and lubrication in engine components

  • Scuffing behavior and wear modeling of piston components

  • Multi-phase and multi-scale engine wear modeling using quantum chemical molecular dynamics and machine learning

  • Digital twin modeling for tribocorrosion

  • Application of artificial intelligence and big data in engine tribology

  • Mechano-chemical kinetic models for boundary lubrication

Awards and HonorsπŸ†βœ¨

  • Technical committee member of IFToMM (Engines and Powertrains)

  • Contributor to Springer’s Mechanisms and Machine Science Series

  • Numerous high-impact journal publications in Tribology Transactions, ASME Journal of Tribology, Tribology International, and Wear

Conclusion🌟

Professor Chao Zhang is an accomplished mechanical engineering expert with a focus on tribology, engine wear, and computational modeling. His interdisciplinary research integrates classical tribology with advanced computational methods, positioning him as a leading figure in his field. His contributions to academia, industry collaborations, and publications underscore his commitment to advancing mechanical engineering and tribology.

Publications πŸ“š

1️⃣ Zhang, C. (2025). Multi-phase and multi-scale engine wear modeling via quantum chemical molecular dynamics and machine learning: A theoretical framework. πŸ”¬πŸ› οΈ Wear, xxx(xxx)xxx. [πŸ”— DOI: 10.1016/j.wear.2025.205771]


2️⃣ Zhang, C. (2023). Lubricant-Chemistry Kinetic Model of Antiwear Film Formation by Oil Additives using SOL, QM MD, and machine learning. πŸ”πŸ“Š STLE 2023 Annual Meeting Digital Proceedings.


3️⃣ Zhang, C. (2022). Scuffing behavior of piston-pin bore bearing in mixed lubrication. βš™οΈπŸ“– In T. Parikyan (Ed.), Advances in Engine and Powertrain Research and Technology (pp. 65–95). Springer, Mechanisms and Machine Science 114.


4️⃣ Zhang, C. (2022). Quantum chemical study of mechanochemical reactive mechanisms of engine oil antiwear additives. πŸ§ͺβš›οΈ Proceedings of I4SDG Workshop 2021, MMS 108, pp. 1–9.


5️⃣ Zhang, C. (2021). Scuffing factor and scuffing failure mapping. πŸš—πŸ”₯ Proceedings of the 2nd World Congress on Internal Combustion Engine, April 21-24, Jinan, China.


6️⃣ Zhang, C. (2018). Analysis of piston scuffing failure based on big data base and cloud computing. β˜οΈπŸ’Ύ Proceedings of the 2018 World Internal Combustion Engine Congress and Exhibition, November 8-11, Wuxi, China.


7️⃣ Zhang, C., et al. (2007). Effect of loading path on sliding contact status for elastic and plastic media. πŸ”©βš™οΈ Proceedings of the STLE/ASME International Joint Tribology Conference, IJTC2007-44481.


8️⃣ Ye, Z.K., Zhang, C., Wang, Y.C., Cheng, H.S, Tung, S. M., Wang, Q., He, J. (2004). An experimental investigation of piston skirt scuffing: a piston scuffing apparatus, experiments, and scuffing mechanism analyses. πŸ”πŸ”¬ WEAR, 257, 8-31.


9️⃣ Zhang, C., Wang, Q., Cheng, H. S. (2004). Scuffing Behavior of Piston-Pin/Bore Bearing in Mixed Lubrication – Part II: Scuffing Mechanism and Failure Criteria. πŸ› οΈβš‘ STLE, Tribology Transactions, 47, 149-156.


πŸ”Ÿ Zhang, C., Cheng, H. S., Qiu, L., Knipstein, K. W., & Bolyard, J. (2003). Scuffing Behavior of Piston-Pin/Bore Bearing in Mixed Lubrication – Part I: Experimental Studies. πŸ§‘β€πŸ”¬πŸ“Š STLE, Tribology Transactions, 46, 193-199.


Kanaga Suba Raja S | Deep Learning | Best Researcher Award

Prof. Dr. Kanaga Suba Raja S | Deep Learning | Best Researcher Award

Professor at Srm Institute Of Science And Technology Tiruchirappalli, India

Dr. S. Kanaga Suba Raja is a dedicated computer science educator and researcher with a passion for innovation and technology. With a rich history of academic leadership and groundbreaking research, he continues to inspire the next generation of engineers. πŸŒπŸ’‘

Publication Profile :Β 

Scopus

Orcid

Google Scholar

 

πŸŽ“ Educational Background :

Dr. Kanaga Suba Raja completed his Ph.D. in Computer Science and Engineering from Manonmaniam Sundaranar University in 2013. He holds a Master’s degree in Computer Science and Engineering from Noorul Islam College of Engineering (2006) and a Bachelor’s degree from The Rajaas Engineering College (2003).

πŸ’Ό Professional Experience :

With over 19 years of experience in academia, Dr. Kanaga Suba Raja has held several prominent positions, including Professor and Head of the Department of Computer Science and Engineering at SRM Institute of Science and Technology, and Associate Dean at the School of Computing. His career spans roles as Associate Professor and Lecturer at various institutions under Anna University, where he contributed significantly to curriculum development and academic administration.

πŸ“š Research Interests :Β 

Dr. Kanaga Suba Raja specializes in artificial intelligence, cloud computing, and biomedical engineering. He has published over 100 research papers, received numerous citations, and holds patents related to cloud computing and medical technologies.

πŸ“ Publication Top Notes :

  1. Priya, J., Kanaga Suba Raja, S., & Usha Kiruthika, S. (2024). State-of-art technologies, challenges, and emerging trends of computer vision in dental images. Computers in Biology and Medicine, 178. https://doi.org/10.1016/j.compbiomed.2024.108800
  2. Priya, J., Kanaga Suba Raja, S., & Sudha, S. (2024). An intellectual caries segmentation and classification using modified optimization-assisted transformer denseUnet++ and ViT-based multiscale residual denseNet with GRU. Signal, Image and Video Processing (SIViP). https://doi.org/10.1007/s11760-024-03227-9
  3. Chandra, & Kanaga Suba Raja, S. (2024). HHECC-AES: A novel hybrid cryptography scheme for developing the secured wireless body area network using heuristic-aided blockchain model. Ad Hoc & Sensor Wireless Networks, 59, 141–179. https://doi.org/10.32908/ahswn.v59.10477
  4. Sandhiya, B., Kanaga Suba Raja, S., Shruthi, K., & Praveena Rachel Kamala, S. (2024). Brain tumour segmentation and classification with reconstructed MRI using DCGAN. Biomedical Signal Processing and Control, 92. https://doi.org/10.1016/j.bspc.2024.106005
  5. Sandhiya, B., & Kanaga Suba Raja, S. (2024). Deep learning and optimized learning machine for brain tumor classification. Biomedical Signal Processing and Control, 89(1). https://doi.org/10.1016/j.bspc.2023.105778
  6. Kausalya, K., & Kanaga Suba Raja, S. (2024). OTRN-DCN: An optimized transformer-based residual network with deep convolutional network for action recognition and multi-object tracking of adaptive segmentation using soccer sports video. International Journal of Wavelets, Multiresolution and Information Processing, 22(1). https://doi.org/10.1142/S0219691323500340
  7. Chandra, B., & Kanaga Suba Raja, S. (2023). Security in wireless body area network (WBAN) using blockchain. IETE Journal of Research. https://doi.org/10.1080/03772063.2023.2233472
  8. Hema, M., & Kanaga Suba Raja, S. (2023). A quantitative approach to minimize energy consumption in cloud data centres using VM consolidation algorithm. KSII Transactions on Internet and Information Systems, 17(2), 312-334. https://doi.org/10.3837/tiis.2023.02.002
  9. Pushpa, S. X., & Kanaga Suba Raja, S. (2022). Enhanced ECC based authentication protocol in wireless sensor network with DoS mitigation. Cybernetics and Systems, 53(2). https://doi.org/10.1080/01969722.2022.2055403
  10. Hema, M., & Kanaga Suba Raja, S. (2022). An efficient framework for utilizing underloaded servers in compute cloud. Computer Systems Science and Engineering, 43(5). https://doi.org/10.32604/csse.2023.024895
  11. Vivekanandan, M., & Kanaga Suba Raja, S. (2022). Virtex-II Pro FPGA-based smart agricultural system. Wireless Personal Communications, 125(1), 119–141. https://doi.org/10.1007/s11277-022-09544-x
  12. Pushpa, S. X., & Kanaga Suba Raja, S. (2022). Elliptic curve cryptography-based authentication protocol enabled with optimized neural network-based DoS mitigation. Wireless Personal Communications, 124(27). https://doi.org/10.1007/s11277-021-08902-5
  13. Balaji, V., & Kanaga Suba Raja, S. (2021). Recommendation learning system model for children with autism. Intelligent Automation & Soft Computing, 31(2). https://doi.org/10.32604/iasc.2022.020287
  14. Valarmathi, K., & Kanaga Suba Raja, S. (2021). Resource utilization prediction technique in cloud using knowledge-based ensemble random forest with LSTM model. Concurrent Engineering: Research and Applications. https://doi.org/10.1177/1063293X211032622
  15. Kanaga Suba Raja, S., & Virgin Louis, B. A. (2021). A review of call admission control schemes in wireless cellular networks. Wireless Personal Communications, 120(4), 3369–3388. https://doi.org/10.1007/s11277-021-08618-6