Dr. Zhu Jingwen | AI in Engineering | Best Researcher Award

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Dr. Zhu Jingwen | AI in Engineering | Best Researcher Award

Dr. Zhu Jingwen | Jiangsu University | China

Dr. Zhu Jingwen is a researcher in control science and engineering whose work focuses on intelligent detection systems and advanced sensing technologies for agricultural safety. His research emphasizes the development of nondestructive testing methods for grains and edible oils through the integration of microwave, millimeter-wave, and near-infrared technologies with chemometric modeling and machine learning algorithms. Dr. Zhu has designed FPGA-based microwave detection systems capable of accurately identifying contaminants such as heavy metals and aflatoxins, contributing significantly to the field of food safety monitoring. His studies have been widely published in respected international journals, including Microchemical Journal, Sensors and Actuators A: Physical, and Spectrochimica Acta Part A. Beyond research, he has demonstrated leadership in innovation and entrepreneurship, leading projects recognized with national honors such as the China Postgraduate “Rural Revitalization – Sci-Tech Empowering Agriculture+” Competition. His scientific contributions are reflected through 13 published documents, cited 46 times by 41 other documents, demonstrating a growing academic impact and an h-index of 5. His efforts also led to the establishment of Dongfang Xiangyu (Jiangsu) Technology Co., Ltd., translating research outcomes into practical industrial applications. With a strong command of programming and embedded system development, Dr. Zhu continues to explore interdisciplinary approaches that merge intelligent algorithms with hardware systems to advance the precision and reliability of agricultural quality assessment technologies.

Profile: Scopus

Featured Publications

Zhu, J., Deng, J., Zhao, X., Xu, L., & Jiang, H. (2024). Quantitative determination of cadmium content in peanut oil using microwave detection method combined with multivariate analysis. Microchemical Journal, 110946.

Zhu, J., Deng, J., Zhao, X., Xu, L., & Jiang, H. (2024). Accurate identification of cadmium pollution in peanut oil using microwave technology combined with SVM-RFE. Sensors and Actuators A: Physical, 368, 115085.

Zhu, J., Chen, Y., Deng, J., & Jiang, H. (2024). Improving the accuracy of FT-NIR determination of zearalenone content in wheat using a characteristic wavelength optimization algorithm. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 313, 124169.

Ji, Z., Zhu, J., Deng, J., Jiang, H., & Chen, Q. (2024). Quantitative determination of zearalenone in wheat by the CSA-NIR technique combined with chemometric algorithms. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 323, 124858.

Zhu, J., Deng, J., Xu, L., & Jiang, H. (2024). Enhancing the performance of natural pigment sensor arrays for the detection of Procymidone residues in Allium tuberosum using outcome-corrected decision-making method. Journal of Food Composition and Analysis, 128, 107059.

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. 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.

 

 

 

Mr. Bimal Kumar Dora | AI in Engineering | Best Researcher Award

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Mr. Bimal Kumar Dora | AI in Engineering | Best Researcher Award

Mr. Bimal Kumar Dora | Visvesvaraya National Institute of Technology | India

Mr. Bimal Kumar Dora is a dedicated researcher in Electrical Engineering, currently pursuing his Doctor of Philosophy at Visvesvaraya National Institute of Technology, Nagpur, after completing his Master of Technology in Control, Power and Electric Drives from the National Institute of Technology, Sikkim, and a Bachelor of Technology in Electrical Engineering from Biju Patnaik University of Technology, Odisha. He recently broadened his international research experience as a Visiting Researcher at the Montefiore Institute, University of Liège, Belgium, where he contributed to advanced studies in renewable energy integration and the development of global electricity grids. His doctoral research, titled Global Electricity Interconnection with Renewable Energy Generation, emphasizes methods such as the Enhanced Critical Time Window Framework, Weibull distribution analysis, and temporal variability indexing to identify and optimize renewable energy sites across Indian onshore and offshore regions. He has designed several innovative hybrid algorithms including Modified Pelican Optimization Algorithm, Novel Modified Pelican Driven Optimization Algorithm, Enhanced Pelican Foraging Algorithm, Enhanced Dragonfly and Moth Optimization Algorithm, Modified Reptile Optimization Algorithm, Modified Harris Hawk and Pelican Optimization Algorithm, and Enhanced Harris Hawk and Pelican Optimization Algorithm.  With 97 citations from 75 documents, 16 publications, and an index rating of 7, he is building a growing academic reputation that combines computational intelligence, renewable energy, and futuristic large-scale power system design.

Profile: Scopus | Google Scholar | Orcid

Featured Publications

  1. Dora, B. K., Rajan, A., Mallick, S., & Halder, S. (2023). Optimal reactive power dispatch problem using exchange market based butterfly optimization algorithm. Applied Soft Computing, 147, 110833.

  2. Halder, S., Bhat, S., & Dora, B. K. (2022). Inverse thresholding to spectrogram for the detection of broken rotor bar in induction motor. Measurement, 198, 111400.

  3. Halder, S., Bhat, S., & Dora, B. (2023). Start-up transient analysis using CWT and ridges for broken rotor bar fault diagnosis. Electrical Engineering, 105(1), 221–232.

  4. Halder, S., Dora, B. K., & Bhat, S. (2022). An enhanced pathfinder algorithm based MCSA for rotor breakage detection of induction motor. Journal of Computational Science, 64, 101870.

  5. Dora, B. K., Bhat, S., Halder, S., & Srivastava, I. (2024). A solution to multi objective stochastic optimal power flow problem using mutualism and elite strategy based pelican optimization algorithm. Applied Soft Computing, 158, 111548.

  6. Dora, B. K., Bhat, S., Halder, S., & Sahoo, M. (2023). Solution of reactive power dispatch problems using enhanced dwarf mongoose optimization algorithm. 2023 International Conference for Advancement in Technology (ICONAT), 1–6.