Dr. Partha Ghosh | AI in Engineering | Best Researcher Award

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Dr. Partha Ghosh | AI in Engineering | Best Researcher Award

Dr. Partha Ghosh | Netaji Subhash Engineering College | India

Dr. Partha Ghosh is a seasoned academic and researcher with more than 22 years of professional experience in Computer Science and Information Technology, currently serving as Associate Professor in the Department of Information Technology and Head of the Department of Computer Science and Business Systems at Netaji Subhash Engineering College, Kolkata. His research expertise spans Computer Networking, Machine Learning, Cloud Computing, Intrusion Detection Systems, Optimization Algorithms, Feature Selection and Classification Techniques, with a focus on developing secure, intelligent and high-performance cloud-based computational environments. His scholarly impact is reflected through 16 SCOPUS-indexed documents, 194 citations by 173 documents and an h-index of 7. Additionally, his ORCID profile lists 20 research works, and according to Google Scholar he has 333 citations (244 since 2020), an h-index of 10 (9 since 2020) and an i10-index of 10 (9 since 2020), demonstrating consistent and growing research visibility. To date, he has authored 24 publications including indexed journal papers, international conference papers and book chapters. He has taught a wide range of core and advanced courses such as Computer Organisation, Computer Networks, Advanced Computer Networking, Microprocessors and Microcontrollers and Database Management Systems at undergraduate and postgraduate levels. His academic engagement also includes serving as Editor-in-Chief and Editorial Board Member of reputed journals and holding multiple Fellow and Life Membership roles across professional bodies, underscoring his continued commitment to research innovation, knowledge dissemination and academic leadership.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Ghosh, P., Mandal, A. K., & Kumar, R. (2015). An efficient cloud network intrusion detection system. In Information Systems Design and Intelligent Applications: Proceedings of …

Ghosh, P., Karmakar, A., Sharma, J., & Phadikar, S. (2018). CS-PSO based intrusion detection system in cloud environment. In Emerging Technologies in Data Mining and Information Security: Proceedings …

Ghosh, P., & Mitra, R. (2015). Proposed GA-BFSS and logistic regression based intrusion detection system. In Proceedings of the 2015 Third International Conference on Computer …

Ghosh, P., Sarkar, D., Sharma, J., & Phadikar, S. (2021). An intrusion detection system using modified-firefly algorithm in cloud environment. International Journal of Digital Crime and Forensics, 13(2), 77–93.

Ghosh, P., Debnath, C., Metia, D., & Dutta, R. (2015). An efficient hybrid multilevel intrusion detection system in cloud environment. IOSR Journal of Computer Engineering, 16(4), 16–26.

Ghosh, P., Shakti, S., & Phadikar, S. (2016). A cloud intrusion detection system using novel PRFCM clustering and KNN based dempster-shafer rule. International Journal of Cloud Applications and Computing, 6(4), 18–35.

Mr. Adizue Ugonna | AI in Engineering | Best Researcher Award

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Mr. Adizue Ugonna | AI in Engineering | Best Researcher Award

Mr. Adizue Ugonna | Budapest University of Technology and Economics | Hungary

Mr. Adizue Ugonna Loveday is a Doctoral Researcher and Laboratory Instructor at the Budapest University of Technology and Economics, specializing in Mechanical Engineering with expertise in industrial and production systems. His research focuses on intelligent modelling and process optimization for ultra-precision machining of hard materials, integrating artificial intelligence, tribological analysis, and thermal modeling to enhance manufacturing precision and efficiency. Professionally, he has contributed to several major research initiatives including the Horizon 2020 Centre of Excellence in Production Informatics and Control (EPIC CoE), the iNext project on industrial digitalization, and multiple Hungarian Scientific Research Fund (OTKA) projects emphasizing AI-based predictive models for advanced machining and intelligent forming processes. His scholarly record demonstrates strong research performance, with 45 citations by 42 documents, 6 documents, and an h-index of 4 in Scopus; and 66 citations, an h-index of 5, and an i10-index of 2 in Google Scholar. In addition, his ORCID profile lists 6 professional activities and 8 published works, reflecting active engagement in international research collaboration, scientific reviewing, and production editing. Through these contributions, Mr. Loveday continues to advance smart and sustainable manufacturing technologies, bridging artificial intelligence and mechanical systems design in alignment with Industry 4.0 innovation goals.

Profiles: Scopus | Orcid | Google Scholar | LinkedIn | ResearchGate

Publication Details

  1. Adizue, U. L., Tura, A. D., Isaya, E. O., Farkas, B. Z., & Takács, M. (2023). Surface quality prediction by machine learning methods and process parameter optimization in ultra-precision machining of AISI D2 using CBN tool. The International Journal of Advanced Manufacturing Technology, 128(1), 1–28.

  2. Adizue, U. L., Nwanya, S. C., & Ozor, P. A. (2020). Artificial neural network application to a process time planning problem for palm oil production. Engineering and Applied Science Research, 47(2), 161–169.

  3. Adizue, U. L., & Takács, M. (2025). Exploring the correlation between design of experiments and machine learning prediction accuracy in ultra-precision hard turning of AISI D2 with CBN insert: A comparative study. The International Journal of Advanced Manufacturing Technology, 1–30.

  4. Elly, O. I., Adizue, U. L., Tura, A. D., Farkas, B. Z., & Takács, M. (2024). Analysis, modelling, and optimization of force in ultra-precision hard turning of cold work hardened steel using the CBN tool. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46(1), 1–18.

  5. Adizue, U. L., Balázs, B. Z., & Takács, M. (2022). Surface roughness prediction applying artificial neural network at micro machining. IOP Conference Series: Materials Science and Engineering, 1246(1), 012034.

  6. Tura, A. D., Isaya, E. O., Adizue, U. L., Farkas, B. Z., & Takács, M. (2024). Optimization of ultra-precision CBN turning of AISI D2 using hybrid GA-RSM and Taguchi-GRA statistic tools. Heliyon, 10(11), e24357.

  7. Adizue, U. L., Agbadah, S. E., Ibeagha, D. C., & Falade, Y. O. (2017). Design and construction of an automated adjustable-can foil sealing machine. International Journal of Engineering and Applied Sciences, 4(9), 257384.