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.
Featured Publications
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Ren, J. (2025). A novel ensemble network based on CNN-AM-BiLSTM learner for temperature prediction of distillation columns. Canadian Journal of Chemical Engineering.
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Ren, J. (2025). Short-term power load forecasting based on SKDR hybrid model. Electrical Engineering.
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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.
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Ren, J. (2025). Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine. Quality and Reliability Engineering International.