Prof. Dr. Raziyeh Pourdarbani | AI in Engineering | Research Excellence Award

Prof. Dr. Raziyeh Pourdarbani | University of Mohaghegh Ardabili | Iran

Prof. Dr. Raziyeh Pourdarbani is a distinguished professor in the Department of Biosystems Engineering at the University of Mohaghegh Ardabili, highly regarded for her academic and research contributions in smart and sustainable agriculture. She holds a Ph.D. in Agricultural Mechanization Engineering from the University of Tabriz and has developed deep expertise in precision agriculture, image processing, artificial intelligence, and machine vision with a focus on non-destructive quality evaluation of agricultural products. Her work advances the use of hyperspectral imaging, convolutional neural networks, metaheuristic algorithms, and Vis-NIR spectroscopy to address key challenges such as fruit bruise detection, nitrogen stress monitoring in plant leaves, and estimation of internal chemical properties in horticultural crops. She has also contributed impactful studies on sustainable energy systems related to agriculture, including biomethane production, hybrid geothermal–solar power plant optimization, and exergy-based diesel engine performance enhancement. Her research portfolio consists of 45 scientific documents with 762 citations from 639 citing documents, supported by an h-index of 17, demonstrating strong global visibility and scholarly influence. Through her innovative work integrating computational intelligence with biosystems engineering, she plays a leading role in advancing intelligent agriculture technologies that enhance productivity, reduce environmental impacts, and support long-term sustainability in the agricultural sector.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Pourdarbani, R., Ghassemzadeh, H. R., Seyedarabi, H., Nahandi, F. Z., & others. (2015). Study on an automatic sorting system for Date fruits. Journal of the Saudi Society of Agricultural Sciences, 14(1), 83-90.
Alibaba, M., Pourdarbani, R., Manesh, M. H. K., Ochoa, G. V., & Forero, J. D. (2020). Thermodynamic, exergo-economic and exergo-environmental analysis of hybrid geothermal-solar power plant based on ORC cycle using emergy concept. Heliyon, 6(4).
Pourdarbani, R., Sabzi, S., Kalantari, D., Karimzadeh, R., Ilbeygi, E., & Arribas, J. I. (2020). Automatic non-destructive video estimation of maturation levels in Fuji apple (Malus pumila) fruit in orchard based on colour (Vis) and spectral (NIR) data. Biosystems Engineering, 195, 136-151.
Pourdarbani, R., Sabzi, S., Kalantari, D., & Arribas, J. I. (2020). Non-destructive visible and short-wave near-infrared spectroscopic data estimation of various physicochemical properties of Fuji apple (Malus pumila) fruits at different stages. Chemometrics and Intelligent Laboratory Systems, 206, 104147.
Razieh Pourdarbani, D. K. J. M. M. M., Sabzi, S., Hernández-Hernández, M., & José Luis … (2019). Comparison of different classifiers and the majority voting rule for the detection of plum fruits in garden conditions. Remote Sensing, 11(2546).
Salimi, M., Pourdarbani, R., & Nouri, B. A. (2020). Factors affecting the adoption of agricultural automation using Davis’s acceptance model (case study: Ardabil). Acta Technologica Agriculturae, 23(1), 30-39.

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

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.