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.

Mr. Andreas Fezer | Data Driven Engineering | Best Researcher Award

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Mr. Andreas Fezer | Data Driven Engineering | Best Researcher Award

Mr. Andreas Fezer | Materials Testing Institute, University of Stuttgart | Germany

Mr. Andreas Fezer is a Scientific Associate at the Materials Testing Institute, University of Stuttgart, Germany, specializing in joining technology and additive manufacturing. He holds both bachelor’s and master’s degrees in mechanical engineering from the University of Stuttgart. Since joining the institute, he has contributed to advanced research on resistance spot welding, aluminum alloys, and the integration of experimental and machine learning approaches in welding technology. His published works focus on improving manufacturing efficiency and material performance. With expertise spanning mechanical engineering fundamentals and applied welding processes, Mr. Fezer plays an active role in advancing industrial materials testing and innovative manufacturing solutions.

Professional Profile

Orcid

Education and Experience

Mr. Andreas Fezer earned his bachelor’s and master’s degrees in mechanical engineering from the University of Stuttgart, Germany. Following his academic training, he began his professional career at the Materials Testing Institute, University of Stuttgart, where he works as a Scientific Associate in the Department of Joining Technology and Additive Manufacturing. His work involves both experimental and computational research, focusing on welding processes, material resistance evaluation, and the development of innovative manufacturing techniques. Through his combined academic background and applied industrial research, Mr. Fezer contributes to the advancement of materials engineering and welding technologies in both academic and industrial contexts.

Summary Suitability

Mr. Andreas Fezer is an outstanding candidate for the Best Researcher Award due to his significant contributions to advanced materials testing and welding technology. As a Scientific Associate at the Materials Testing Institute, University of Stuttgart, he has demonstrated expertise in joining technology and additive manufacturing, focusing on aluminum alloys and resistance spot welding processes. His work combines experimental investigations with innovative machine learning techniques, enabling improved understanding of dynamic resistance and contact behavior in metal joining.

Professional Development 

Mr. Andreas Fezer has cultivated expertise in resistance spot welding, aluminum alloy characterization, and additive manufacturing processes. He engages in collaborative research integrating experimental methods with machine learning to improve process understanding and efficiency in manufacturing. His professional growth has been shaped by active participation in scientific publications, interdisciplinary teamwork, and applied research projects that connect engineering theory with industrial practice. Working within the renowned Materials Testing Institute at the University of Stuttgart has allowed him to refine his analytical, problem-solving, and technical skills, positioning him as a valuable contributor to innovation in mechanical engineering and materials science.

Research Focus 

Mr. Andreas Fezer’s research is centered on welding technology, particularly resistance spot welding of aluminum alloys used in automotive and structural applications. His work addresses both the physical phenomena involved in material joining and the development of methods for evaluating contact and bulk resistance in metals. He explores dynamic resistance behavior using a combination of laboratory experimentation and machine learning techniques, aiming to enhance process reliability, material performance, and production efficiency. His research focus falls under the category of advanced manufacturing and materials engineering, with an emphasis on joining processes, welding quality control, and the integration of data-driven approaches in manufacturing.

Awards and Honors

Mr. Andreas Fezer’s professional recognition is reflected in his contributions to peer-reviewed scientific publications and his role in advancing welding technology research. His work has appeared in reputable international journals, showcasing the impact and quality of his studies in materials testing and manufacturing innovation. Through collaborative projects and research dissemination, he has earned professional respect within the mechanical engineering and materials science community. His achievements underscore his reputation as a researcher whose work supports both academic advancement and industrial application in the field of joining technology and additive manufacturing.

Publication Top Notes

Title: Method for Determining the Contact and Bulk Resistance of Aluminum Alloys in the Initial State for Resistance Spot Welding
Year: 2025

Title: Experimental and Machine Learning Investigation of Dynamic Resistance in Aluminum Resistance Spot Welding for the Body-in-White
Year: 2025

Conclusion

Mr. Andreas Fezer’s innovative research, combining experimental methods and machine learning in welding technology, has made a significant impact on materials science and manufacturing. His work demonstrates technical excellence, practical relevance, and academic rigor, establishing him as a leading researcher in his field. His contributions to understanding and improving aluminum resistance spot welding processes highlight both his scientific insight and his ability to drive industrial innovation, making him exceptionally deserving of the Best Researcher Award.