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.

Mr. Adizue Ugonna | AI in Engineering | Best Researcher Award

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Mr. Adizue Ugonna | AI in Engineering | Best Researcher Award

Mr. Adizue Ugonna | Budapest University of Technology and Economics | Hungary

Mr. Adizue Ugonna Loveday is a Doctoral Researcher and Laboratory Instructor at the Budapest University of Technology and Economics, specializing in Mechanical Engineering with expertise in industrial and production systems. His research focuses on intelligent modelling and process optimization for ultra-precision machining of hard materials, integrating artificial intelligence, tribological analysis, and thermal modeling to enhance manufacturing precision and efficiency. Professionally, he has contributed to several major research initiatives including the Horizon 2020 Centre of Excellence in Production Informatics and Control (EPIC CoE), the iNext project on industrial digitalization, and multiple Hungarian Scientific Research Fund (OTKA) projects emphasizing AI-based predictive models for advanced machining and intelligent forming processes. His scholarly record demonstrates strong research performance, with 45 citations by 42 documents, 6 documents, and an h-index of 4 in Scopus; and 66 citations, an h-index of 5, and an i10-index of 2 in Google Scholar. In addition, his ORCID profile lists 6 professional activities and 8 published works, reflecting active engagement in international research collaboration, scientific reviewing, and production editing. Through these contributions, Mr. Loveday continues to advance smart and sustainable manufacturing technologies, bridging artificial intelligence and mechanical systems design in alignment with Industry 4.0 innovation goals.

Profiles: Scopus | Orcid | Google Scholar | LinkedIn | ResearchGate

Publication Details

  1. Adizue, U. L., Tura, A. D., Isaya, E. O., Farkas, B. Z., & Takács, M. (2023). Surface quality prediction by machine learning methods and process parameter optimization in ultra-precision machining of AISI D2 using CBN tool. The International Journal of Advanced Manufacturing Technology, 128(1), 1–28.

  2. Adizue, U. L., Nwanya, S. C., & Ozor, P. A. (2020). Artificial neural network application to a process time planning problem for palm oil production. Engineering and Applied Science Research, 47(2), 161–169.

  3. Adizue, U. L., & Takács, M. (2025). Exploring the correlation between design of experiments and machine learning prediction accuracy in ultra-precision hard turning of AISI D2 with CBN insert: A comparative study. The International Journal of Advanced Manufacturing Technology, 1–30.

  4. Elly, O. I., Adizue, U. L., Tura, A. D., Farkas, B. Z., & Takács, M. (2024). Analysis, modelling, and optimization of force in ultra-precision hard turning of cold work hardened steel using the CBN tool. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46(1), 1–18.

  5. Adizue, U. L., Balázs, B. Z., & Takács, M. (2022). Surface roughness prediction applying artificial neural network at micro machining. IOP Conference Series: Materials Science and Engineering, 1246(1), 012034.

  6. Tura, A. D., Isaya, E. O., Adizue, U. L., Farkas, B. Z., & Takács, M. (2024). Optimization of ultra-precision CBN turning of AISI D2 using hybrid GA-RSM and Taguchi-GRA statistic tools. Heliyon, 10(11), e24357.

  7. Adizue, U. L., Agbadah, S. E., Ibeagha, D. C., & Falade, Y. O. (2017). Design and construction of an automated adjustable-can foil sealing machine. International Journal of Engineering and Applied Sciences, 4(9), 257384.

 

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.

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. 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.