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.

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

 

 

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.

 

 

 

Dr. Licheng Sun| Multi-Agent systems | Best Researcher Award

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 Dr. Licheng Sun| Multi-Agent systems | Best Researcher Award

Dr. Licheng Sun , Beijing Institute of Technology , China.

Dr. Licheng Sun is a dedicated PhD student at the Beijing Institute of Technology 🎓, specializing in multi-agent systems, reinforcement learning, and game theory 🤖. As the first author, he has published four SCI-indexed journal papers, including one in a JCR Q1 journal 🏆. Actively engaged in academic exchanges, he has presented at five national and international conferences 🌍, delivering two oral presentations 🎤. His excellence is recognized with over ten national and provincial awards, including the Outstanding Graduate Student title in 2024 🏅. Licheng has also contributed to two national-level major research projects, strengthening his research foundation 🔬.

Publication Profile

Scopus
ORCID

Education & Experience 📚🔍

  • 🎓 PhD Student, Beijing Institute of Technology
  • 📖 Research Expertise: Multi-agent systems, reinforcement learning, and game theory
  • 📜 Published 4 SCI-indexed journal papers, including one in a JCR Q1 journal
  • 🎤 Presented at 5 conferences (2 oral presentations at international events)
  • 🔬 Contributed to 2 major national research projects
  • 🏆 Over 10 national/provincial awards, including Outstanding Graduate Student 2024

Suitability Summary

Dr. Licheng Sun is a distinguished PhD student at Beijing Institute of Technology, making outstanding contributions in the fields of  multi-agent systems, reinforcement learning, and game theory 🤖. His exceptional research output, including four SCI-indexed journal publications and contributions to national-level research projects, showcases his leadership in engineering and artificial intelligence. His groundbreaking studies on adaptive topological networks, AI-driven decision-making, and deep learning applications position him as a highly deserving candidate for the Best Researcher Award 🏅.

Professional Development 🚀

Dr. Licheng Sun has actively contributed to advancing research in multi-agent systems and reinforcement learning 🤖. His work includes adaptive grouping dynamic topological spaces and topological networks in Fourier space 📡. Through his publications in high-impact journals, he has significantly influenced the field of AI and game theory 🏅. Additionally, he has participated in national-level research projects, strengthening his expertise in cutting-edge technologies 🔬. His commitment to academic excellence is evident through his active conference engagements, oral presentations, and awards 🏆. His research aims to bridge theoretical advancements with practical applications in AI-driven decision-making ⚡.

Research Focus 🔬

Dr. Licheng Sun research is centered on multi-agent reinforcement learning, adaptive topological networks, and game theory 🤖. His innovative work introduces dynamic topological spaces for enhanced agent collaboration, optimizing decision-making processes 🧠. Through his highly cited publications, he explores deep reinforcement learning architectures, integrating topological adaptability 🌐. His contributions to national research projects focus on real-world AI applications, such as helmet-wearing detection systems 🏗️. With a strong emphasis on practical AI solutions, his research aims to develop more robust, scalable, and intelligent agent-based systems for industrial and technological advancements ⚡.

Awards & Honors 🏆

  • 🏅 Outstanding Graduate Student 2024
  • 🏆 Over 10 national and provincial awards
  • 🏅 Honored in prestigious AI and engineering competitions
  • 🎓 Recipient of top academic and research excellence awards
  • 📜 Published in high-impact journals (SCI: 4, EI: 8)
  • 🚀 Contributor to major national research projects
  • 🎤 Oral presenter at international conferences
  • 🏅 Recipient of multiple innovation and technology awards

Publication Top Notes

📌 Major Publications

1️⃣ GDT: Multi-agent reinforcement learning framework based on adaptive grouping dynamic topological space

  • 📖 Journal: Information Sciences
  • 📅 Publication Date: February 2025
  • 🔗 DOI: 10.1016/j.ins.2024.121646
  • 👥 Contributors: Licheng Sun, Hongbin Ma, Zhentao Guo
  • 🔍 Focus: Multi-agent reinforcement learning with adaptive grouping and dynamic topology.

2️⃣ Multi-agent reinforcement learning system framework based on topological networks in Fourier space

  • 📖 Journal: Applied Soft Computing
  • 📅 Publication Date: March 2025
  • 🔗 DOI: 10.1016/j.asoc.2025.112986
  • 👥 Contributors: Licheng Sun, Ao Ding, Hongbin Ma
  • 🔍 Focus: A novel multi-agent learning framework utilizing Fourier space topological networks.

3️⃣ HWD-YOLO: A New Vision-Based Helmet Wearing Detection Method

  • 📖 Journal: Computers, Materials & Continua
  • 📅 Publication Date: 2024
  • 🔗 DOI: 10.32604/cmc.2024.055115
  • 👥 Contributors: Licheng Sun, Heping Li, Liang Wang
  • 🔍 Focus: AI-driven safety monitoring system for helmet detection using YOLO-based deep learning.

4️⃣ CT-MVSNet: Curvature-guided multi-view stereo with transformers

  • 📖 Journal: Multimedia Tools and Applications
  • 📅 Publication Date: April 23, 2024
  • 🔗 DOI: 10.1007/s11042-024-19227-3
  • 👥 Contributors: Liang Wang, Licheng Sun, Fuqing Duan
  • 🔍 Focus: Transformer-based multi-view stereo depth estimation using curvature-guided enhancements.

Conclusion

Dr.Licheng Sun’s exceptional research, high-impact publications, and contributions to national research projects set him apart as an emerging leader in artificial intelligence and multi-agent learning. His ability to bridge theoretical advancements with real-world applications in AI and engineering makes him a perfect candidate for the Best Researcher Award 🏅. His dedication to advancing reinforcement learning frameworks, AI-driven safety systems, and topological networks reflects his pioneering role in superior engineering research 🚀.