Mr. Oussama El Gharras | Sustainable Engineering | Innovative Research Award

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Mr. Oussama El Gharras | Sustainable Engineering | Innovative Research Award

Mr. Oussama El Gharras | National Institute for Agricultural Research | Morocco

Mr. Oussama El Gharras is a distinguished agricultural engineering specialist with extensive expertise in mechanization, conservation agriculture, and the development of farm machinery, supported by a research record featuring 57 citations by 55 documents, 8 published documents, and an h-index of 4. Born on 22 November 1962 in Marrakech, he earned his Ingénieur d’application degree from IAV Hassan II, followed by a Master of Science in Agricultural Engineering from Oklahoma State University and PHD coursework at the University of Nebraska–Lincoln, later strengthening his managerial background through the Cycle Supérieur de Gestion at ENCG Settat. His career at INRA encompasses major contributions to small farm mechanization, animal traction, food legume production mechanisation, and the design and industrial development of No-Till seed drills and feed block units. He managed the INRA agricultural engineering laboratory and coordinated several national and international initiatives, including the INRA-IAV-DERD No-Till project, AAAID-CRRA Settat program, AusAID-ACIAR-INRA collaboration, and ConServeTerra. As Vice President of AGENDA and an active member of AMAC and the National Commission of Agricultural Mechanization, he continues to play a pivotal role in advancing sustainable agriculture in North Africa. His scientific publications address conservation agriculture, weed dynamics, and resilient farming systems in semi-arid regions.

Profile: Scopus

Featured Publications

El Gharras, O. (2024). Perceptions and sociocultural factors underlying adoption of conservation agriculture in the Mediterranean. Agriculture and Human Values.

El Gharras, O., El Mourid, M., & Boulal, H. (2016). Conservation agriculture in North Africa: Experiences, achievements and challenges. In A. Kassam et al. (Eds.), Conservation agriculture for Africa: Building resilient farming systems in changing climate. CAB International.

Tanji, A., El Gharras, O., Mayfield, A., & El Mourid, M. (2017). On-farm evaluation of integrated weed management in no-till rainfed crops in semi-arid Morocco. African Journal of Agricultural Research, 12(16), 1404–1410.

Tanji, A., El Gharras, O., Ouabbou, H., & Mladen, T. (2017). Weed dynamics in no-till rainfed crops in Chaouia, semi-arid Morocco. Journal of Agricultural Science, 9(11).

El Gharras, O., El Brahli, A., & El Mourid, M. (2009). No-till system applied to Northern Africa rainfed agriculture: Case of Morocco. In Proceedings of the 4th World Congress on Conservation Agriculture (pp. xx–xx). New Delhi, India.

El Gharras, O., & Idrissi, M. (2006). Contraintes technologiques au développement du semis direct au Maroc. Options Méditerranéennes, Série A: Séminaires Méditerranéens, 69, 121–124.

El Gharras, O., Ait Lhaj, A., & Idrissi, M. (2004). Développement d’un semoir non labour industriel. In Deuxièmes Rencontres Méditerranéennes sur le Semis Direct (pp. 74–81). FERT/RCM; AGER.

 

Assist. Prof. Dr. Yan Zeng | AI in Engineering | Best Researcher Award

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Assist. Prof. Dr. Yan Zeng | AI in Engineering | Best Researcher Award

Assist. Prof. Dr. Yan Zeng | Hangzhou Dianzi University | China

Assist. Prof. Dr. Yan Zeng, an accomplished associate professor at the School of Computer Science, Hangzhou Dianzi University, has made significant contributions in the fields of distributed and parallel computing, distributed machine learning, and big data analytics. After earning her PhD from the Institute of Software, Chinese Academy of Sciences in 2016, her research has focused on advancing large-scale computation and data-intensive systems.  The Key Research and Development Program of Zhejiang Province, the Yangtze River Delta Project, and the Natural Science Foundation of Zhejiang Province. Her academic influence is reflected in 173 citations by 161 documents, 42 published papers, and an h-index of 9, demonstrating strong research impact and visibility. With 10 peer-reviewed publications in SCI and Scopus-indexed journals, Yan Zeng’s scholarly output showcases innovation in computational frameworks and distributed systems. Furthermore, she has been actively involved in practical technological advancements, holding 34 patents that bridge theoretical insights with industrial applications. Through her extensive research, publication record, and innovation-driven approach, Yan Zeng continues to play a pivotal role in shaping advancements in computer science and data engineering.

Profile: Scopus

Featured Publications

Zeng, Y., et al. (2025). FedAMM: Federated learning for brain tumor segmentation with arbitrary missing modalities [Conference paper]. Proceedings of the International Conference on Artificial Intelligence and Machine Learning.

Zeng, Y., et al. (2025). TransAware: An automatic parallel method for deep learning model training with global model structure awareness [Conference paper]. Proceedings of the International Conference on Advanced Computing and Applications.

Zeng, Y., et al. (2025). A correlation analysis-based federated learning framework for defending against collusion-free-riding attacks. Cybersecurity, 2025(1), 1–12.

Zeng, Y., et al. (2025). FedAEF: Optimizing federated learning with mining and enhancing local data features. Cluster Computing, 2025(1), 1–15.

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).

 

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.