Dr. Zahra Beheshti | AI in Engineering | Best Researcher Award

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Dr. Zahra Beheshti | AI in Engineering | Best Researcher Award

Dr. Zahra Beheshti | Islamic Azad University | Iran

Dr. Zahra Beheshti is an Assistant Professor at the Islamic Azad University, Najafabad Branch, with a distinguished background in computer engineering and artificial intelligence. She holds a B.Sc. and M.Sc. in Computer Engineering (Software) and a Ph.D. in Computer Science with a focus on Artificial Intelligence, followed by postdoctoral research in Soft Computing. Dr. Beheshti has made significant contributions to the field, including the compilation of the book Centripetal Accelerated Particle Swarm Optimization and Applications. Her academic excellence has been recognized through scholarships awarded to top international Ph.D. students. She is actively involved in knowledge dissemination, having conducted multiple workshops on advanced topics such as Machine Learning, Fuzzy Expert Systems and their application in algorithm parameter determination, and Introduction to Fuzzy Logic along with the Design and Implementation of Fuzzy Expert Systems. Her research output includes 34 documents, cited 1,865 times by 1,698 publications, with an h-index of 20, reflecting the significant impact of her work. Through her teaching, research, and publications, Dr. Beheshti demonstrates a strong commitment to advancing computational intelligence, fostering innovation, and mentoring the next generation of researchers in AI and soft computing, combining both academic rigor and practical application.

Profile: Scopus | Google Scholar | Orcid

Featured Publications

  • Z Beheshti, SMH Shamsuddin, A review of population-based meta-heuristic algorithms, Int. J. Adv. Soft Comput. Appl 5 (1), 1-35, 744 citations, 2013

  • H Abedinpourshotorban, SM Shamsuddin, Z Beheshti, DNA Jawawi, Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm, Swarm and Evolutionary Computation 26, 8-22, 414 citations, 2016

  • M Jafarzadegan, F Safi-esfahani, Z Beheshti, Combining Hierarchical Clustering approaches using the PCA Method, Expert Systems with Applications 137, 1-10, 156 citations, 2019

  • Z Beheshti, SM Shamsuddin, S Hasan, Memetic binary particle swarm optimization for discrete optimization problems, Information Sciences 299, 58-84, 129 citations, 2015

  • Z Beheshti, SMH Shamsuddin, CAPSO: centripetal accelerated particle swarm optimization, Information Sciences 258, 54-79, 120 citations, 2014

  • M Banaie-Dezfouli, MH Nadimi-Shahraki, Z Beheshti, R-GWO: Representative-based grey wolf optimizer for solving engineering problems, Applied Soft Computing 106, 1-28, 108 citations, 2021

 

 

Prof. Ouajdi Korbaa | AI in Engineering | Innovative Research Award

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Prof. Ouajdi Korbaa | AI in Engineering | Innovative Research Award

Prof. Ouajdi Korbaa | University of Sousse | Tunisia

Prof. Ouajdi Korbaa is a distinguished researcher and professor at the Institute of Computer Science and Communication Techniques, University of Sousse, Tunisia, and a member of the Modeling of Automated Reasoning Systems Laboratory. His research focuses on modeling, discrete optimization, scheduling, and artificial intelligence, contributing significantly to the development of advanced methodologies in these areas. He has supervised numerous Master’s and PhD students and actively participates in academic juries, reflecting his commitment to mentoring the next generation of researchers. Prof. Korbaa has authored 157 documents cited by 998 sources, achieving an h-index of 18, demonstrating his strong impact and influence in the field. His work integrates theoretical foundations with practical applications, advancing computational techniques for problem-solving and decision-making. Recognized for his expertise in optimization and AI, he has made substantial contributions to both the academic community and the broader field of computer science, fostering innovation in modeling and automated reasoning systems.

Profile: Scopus | Google Scholar | Orcid

Featured Publications

  • Nssibi, M., Manita, G., & Korbaa, O. (2023). Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey. Computer Science Review, 49, 100559.

  • Jemili, F., Meddeb, R., & Korbaa, O. (2024). Intrusion detection based on ensemble learning for big data classification. Cluster Computing, 27(3), 3771–3798.

  • Benzarti, S., Triki, B., & Korbaa, O. (2017). A survey on attacks in Internet of Things based networks. In Proceedings of the 2017 International Conference on Engineering & MIS (ICEMIS) (pp. 1–7).

  • Meddeb, R., Jemili, F., Triki, B., & Korbaa, O. (2023). A deep learning-based intrusion detection approach for mobile Ad-hoc network. Soft Computing, 27(14), 9425–9439.

  • Abid, A., Jemili, F., & Korbaa, O. (2024). Real-time data fusion for intrusion detection in industrial control systems based on cloud computing and big data techniques. Cluster Computing, 27(2), 2217–2238.

  • Korbaa, O., Camus, H., & Gentina, J. C. (1997). FMS cyclic scheduling with overlapping production cycles. In Proceedings of the 18th International Conference on Application and Theory of Automation in Technology (pp. 1–10).

  • Lee, J., & Korbaa, O. (2004). Modeling and scheduling of ratio-driven FMS using unfolding time Petri nets. Computers & Industrial Engineering, 46(4), 639–653.

  • Meddeb, R., Triki, B., Jemili, F., & Korbaa, O. (2017). A survey of attacks in mobile ad hoc networks. In Proceedings of the 2017 International Conference on Engineering & MIS (ICEMIS) (pp. 1–7).

 

Dr. Goutam Khankari | Sustainable Engineering | Best Researcher Award

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Dr. Goutam Khankari | Sustainable Engineering | Best Researcher Award

Dr. Goutam Khankari | Damodar Valley Corporation | India

Dr. Goutam Khankari is a distinguished researcher and engineer specializing in thermal power systems. His PhD research, titled “Thermodynamic Analysis and Performance Improvement of Coal-Fired Thermal Power Plants,” focused on a comprehensive 4-E approach—Energy, Exergy, Environment, and Economic analysis—of various coal-fired steam power plants using high-ash Indian coals. The primary objective of his work was to enhance the overall efficiency and net power output of these plants by exploiting low-grade waste energy through the Kalina Cycle System, integrating solar energy, and optimizing operational conditions. Dr. Khankari’s research not only provides critical insights into energy and exergy efficiencies but also emphasizes sustainable practices and environmental considerations in coal-based power generation. His work has been widely recognized, reflected in 97 citations across 88 documents and an h-index of 5, demonstrating significant academic influence. Through his innovative approaches to performance improvement and waste energy utilization, Dr. Khankari has contributed meaningfully to the field of thermal engineering, offering practical solutions for maximizing energy output while minimizing environmental impact in India’s coal-fired power sector.

Profile: Scopus | Google Scholar

Featured  Publications

Khankari, G., & Karmakar, S. (2016). Power generation from coal mill rejection using Kalina cycle. Journal of Energy Resources Technology, 138(5), 052004.

Khankari, G., Munda, J., & Karmakar, S. (2016). Power generation from condenser waste heat in coal-fired thermal power plant using Kalina cycle. Energy Procedia, 90, 613–624.

Khankari, G., & Karmakar, S. (2018). Power generation from fluegas waste heat in a 500 MWe subcritical coal-fired thermal power plant using solar assisted Kalina Cycle System 11. Applied Thermal Engineering, 138, 235–245.

Khankari, G., & Karmakar, S. (2021). A novel solar assisted Kalina cycle system for waste heat utilization in thermal power plants. International Journal of Energy Research, 45(12), 17146–17158.

Roge, N. H., Khankari, G., & Karmakar, S. (2022). Waste heat recovery from fly ash of 210 MW coal fired power plant using organic rankine cycle. Journal of Energy Resources Technology, 144(8), 082107.

Khankari, G., & Karmakar, S. (2014). Operational optimization of turbo-generator (TG) cycle of a 500MW coal-fired thermal power plant. In 2014 6th IEEE Power India International Conference (PIICON) (pp. 1–6).

Khankari, G., Karmakar, S., Pramanick, A., & Biswas, M. (2013). Thermodynamic analysis of a 500MW coal-fired Indian power plant. In ESMOC Conference, NIT Durgapur, India.

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.