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

Published on by Superior Awards

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

Published on by Superior Awards

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.