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.

Ms. Asima Sarwar | AI in Engineering | Best Researcher Award

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Ms. Asima Sarwar | AI in Engineering | Best Researcher Award

Ms. Asima Sarwar | Ghulam Ishaq Khan Institute of Engineering Sciences and Technology | Pakistan

Ms. Asima Sarwar is a computer engineer and researcher with expertise in Artificial Intelligence, Data Engineering, and Machine Learning. She is pursuing a PhD in Computer Engineering with research focused on AI, data analytics, and distributed computing systems. Her academic background includes a master’s degree in Computer Systems Engineering, specializing in Smart Grids and the Internet of Things, and a bachelor’s degree in Electrical Engineering (Communication). She has professional experience as a computer engineer, research assistant, and lecturer, contributing to projects in secure IoT device development, cyber-secure systems, and AI-based data processing. Ms. Asima has taught various undergraduate and postgraduate courses including Big Data Analytics, Machine Learning, Generative AI, Operating Systems, and Ethical AI. Her work emphasizes technical innovation, algorithmic optimization, and the integration of intelligent systems for real-world applications. With strong analytical and problem-solving skills, she is actively involved in advancing research in AI-driven technologies, data engineering, and computer vision. Her contributions reflect a balance between academic rigor, applied research, and technological development aimed at improving system efficiency and advancing modern computing solutions.

Profile: Scopus

Featured Publications

  • Sarwar, A., Usman, M., Hussain, M., Jadoon, K. K., Manzoor, T., & Ali, S. (2025). AI-powered deep ultraviolet laser diode design for resource-efficient optimization. Journal of Computational Electronics, 24(4), 1–19.

  • Mahmood, M. A., Maab, I., Sibtain, M., Sarwar, A., Arsalan, M., & Hussain, M. (2025, March). Advancements in sentiment analysis: A methodological examination of news using multiple LLMs. In Proceedings of the 31st Annual Meeting of the Association for Natural Language Processing.

  • Sarwar, A., Khan, W. U., Marwat, S. N. K., & Ahmed, S. (2022). Enhanced anomaly detection system for IoT based on improved dynamic SBPSO. Sensors MDPI, 22(4926).

  • Sarwar, A., Hassan, S., Khan, W. U., Marwat, S. N. K., & Ahmed, S. (2022). Design of an advance intrusion detection system for IoT networks. In Proceedings of the 2nd International Conference on Artificial Intelligence (ICAI) (pp. 46–51).

  • Ijaz, A. Z., Ali, R. H., Sarwar, A., Khan, T. A., & Baig, M. M. (2022). Importance of synteny in homology inference. In Proceedings of the IEEE International Conference on Emerging Technologies (ICET).

  • Azam, T., Tahir, F. A., Sarwar, A., & Qayyum, M. A. (2023). A high gain wide band compact size dual band patch antenna for 5G application. In Proceedings of the IEEE International Conference on Emerging and Sustainable Technologies (ICEST) (pp. 1–3).

 

 

Dr. S. Thirunavukkarasu | AI in Engineering | Best Researcher Award

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Dr. S. Thirunavukkarasu | AI in Engineering | Best Researcher Award

Dr. S. Thirunavukkarasu | Indira Gandhi Centre for Atomic Research | India

Dr. S. Thirunavukkarasu research focuses on quantitative nondestructive evaluation (NDE), finite element (FE) modeling, digital signal and image processing, and the development of innovative sensors and instrumentation for advanced inspection applications. His work emphasizes multi-parametric linear and nonlinear regression, radial basis function (RBF), and multidimensional RBF neural networks for accurate flaw sizing in eddy current testing. He has contributed to FE modeling of electromagnetic NDE phenomena, including the optimization of remote field eddy current probe parameters for ferromagnetic steam generator tube inspections and modeling of magnetic flux leakage considering nonlinear magnetic permeability. His studies extend to the simulation of pulsed and sweep frequency eddy current methods to improve detection efficiency. Additionally, his research in wavelet transform–based digital signal processing enhances the interpretation of eddy current signals from complex regions such as bends and support plate intersections. He has also advanced in-house development of remote field eddy current techniques for the inspection of modified 9Cr-1Mo steel steam generator tubes. His computational expertise includes MATLAB, Python, and LabVIEW, alongside specialized software such as COMSOL, FEMM, and CIVA for modeling and simulation in electromagnetic and NDE applications.

Profile: Orcid

Featured Publications

Arun, A. D., Rajiniganth, M. P., Chandra, S., & Thirunavukkarasu, S. (2025). A numerical model of parallel disc capacitor probe used in nondestructive dielectric permittivity evaluation by algebraic topological method. International Journal of Applied Electromagnetics and Mechanics, 2025-09.

Sharatchandra Singh, W., Haneef, T. K., Thirunavukkarasu, S., & Kumar, A. (2025). In-situ measurement of tensile deformation-induced magnetic fields in high strength low alloy steels using GMR based metal magnetic memory technique. International Journal of Applied Electromagnetics and Mechanics, 2025-09-10.

Arun, A. D., Chandra, S., Thirunavukkarasu, S., Rajiniganth, M. P., Malathi, N., & Sivaramakrishna, M. (2025). A novel algebraic topological method-based approach for evaluating stored electrostatic energy and 3D Maxwellian capacitance. Journal of Electrostatics, 2025-06.

Thirunavukkarasu, S., Kumar, A., Martin, J. P., Harini, T., Reddy, S., Emil, S., & Balu, C. (2025). Automated detection of defects in eddy current inspection data using machine learning methods. International Journal of Applied Electromagnetics and Mechanics, 2025-06-03.

Balakrishnan, S., Das, C. R., Thirunavukkarasu, S., & Kumar, A. (2025). In-situ hardness evaluation of hard-faced coatings through eddy current NDE. International Journal of Applied Electromagnetics and Mechanics, 2025-05-23.

Vijayachandrika, T., Arjun, V., Thirunavukkarasu, S., & Kumar, A. (2025). Design, fabrication, and characterization of staggered array radial coil RFEC probe for small diameter ferritic steel tube. IEEE Sensors Journal, 2025-05-01.

Dr. Ren Jianji | AI in Engineering | Best Researcher Award

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Dr. Ren Jianji | AI in Engineering | Best Researcher Award

Dr. Ren Jianji | Henan Polytechnic University | China

Dr. Ren Jianji is an Associate Professor at the School of Software, Henan University of Technology. She earned her Doctoral and Master degrees in Computer Science and Technology from Dong-A University and her Bachelor degree in Information Management and Information Systems from Jinan University. Since joining Henan University of Technology in 2013, she has advanced from Lecturer to Associate Professor, making significant contributions to computer science and software engineering education and research. Over the past 5 years, she has led several major research projects, including a key provincial project on federated learning in edge computing, a collaborative algorithm study for edge intelligence based on complex networks, and multiple industrial projects focused on industrial big data analysis, digital twin systems, and Internet of Vehicles technologies. Dr. Ren’s research interests include edge computing, intelligent algorithms, digital twin systems, and applied big data analytics, reflecting a strong combination of theoretical innovation and practical implementation. She has authored 45 research documents, cited 976 times by 740 documents, with an h-index of 16. Her work has advanced intelligent computing applications in both academic and industrial settings, demonstrating her leadership in developing algorithms and systems that address real-world challenges and establishing her as a leading figure in intelligent computing in China.

Profile: Scopus

Featured Publications

  • Ren, J. (2025). A novel ensemble network based on CNN-AM-BiLSTM learner for temperature prediction of distillation columns. Canadian Journal of Chemical Engineering.

  • Ren, J. (2025). Short-term power load forecasting based on SKDR hybrid model. Electrical Engineering.

  • Ren, J. (2025). A method for intelligent information extraction of coal fractures based on µCT and deep learning. Meitiandizhi Yu Kantan Coal Geology and Exploration.

  • Ren, J. (2025). Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine. Quality and Reliability Engineering International.