Mohsen Edalat | Machine Learning for Computer Vision | Editorial Board Member

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Assoc. Prof. Dr. Mohsen Edalat | Machine Learning for Computer Vision | Editorial Board Member

Associate Professor | Shiraz University | Iran

Dr. Mohsen Edalat an accomplished researcher from Shiraz University, Iran, has made notable contributions to the fields of machine learning geospatial modeling and smart agriculture. With an impressive research record comprising 39 scientific publications and over 614 citations Dr. Edalat has demonstrated sustained academic productivity and influence in computational and environmental sciences. His research emphasizes the integration of advanced data-driven algorithms with ecological and agricultural systems to enhance sustainability and decision-making processes.Among his recent works Dr. Edalat has explored diverse applications of machine learning for ecological and agricultural optimization. His 2025 publications include studies on predicting nepetalactone accumulation in Nepeta persica through machine learning and geospatial analysis modeling ecological preferences of Kentucky bluegrass under varying water conditions (Water Switzerland)  and mapping early-season dominant weeds using UAV-based imagery to support precision farming. These investigations reflect his innovative approach to merging remote sensing artificial intelligence and environmental modeling to address complex agroecological challenges.With an h-index of 11 and collaborations with more than 60 co-authors  Dr. Edalat’s work highlights strong interdisciplinary engagement and a commitment to advancing data-driven sustainability. His studies contribute not only to the scientific community but also to practical agricultural applications that promote resource efficiency and ecological resilience. Through his ongoing research Dr. Edalat continues to shape the evolving landscape of smart agriculture and environmental informatics demonstrating the global relevance and societal value of computational intelligence in natural systems.

Profiles:  Scopus | ORCID

Featured Publications

1. Edalat, M., et al. (2025). Predicting nepetalactone accumulation in Nepeta persica using machine learning algorithms and geospatial analysis. Scientific Reports.

2. Edalat, M., et al. (2025). Modeling the ecological preferences and adaptive capacities of Kentucky bluegrass based on water availability using various machine learning algorithms. Water (Switzerland).

3. Edalat, M., et al. (2025). Early season dominant weed mapping in maize field using unmanned aerial vehicle (UAV) imagery: Towards developing prescription map. Smart Agricultural Technology.

Dr. Mohsen Edalat’s research integrates machine learning, geospatial analytics, and agricultural science to enhance crop management and environmental sustainability. His innovative work advances precision agriculture, supporting data-driven decisions that improve resource efficiency, boost food security, and promote sustainable development at a global scale.

Nagaraj | Deep Learning for Computer Vision | Excellence in Research

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Dr. P. Nagaraj | Deep Learning for Computer Vision | Excellence in Research

Associate Professor | SRM Institute of Science and Technology  | India 

Dr. P. Nagaraj is an esteemed Associate Professor at the SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India. With research expertise spanning Artificial Intelligence, Data Science, Data Analytics, Machine Learning, and Recommender Systems, he has made substantial contributions to intelligent computing and healthcare analytics. His innovative work focuses on applying deep learning, fuzzy inference, and explainable AI (XAI) techniques to real-world challenges in medical diagnosis, cybersecurity, and sustainable automation.Dr. Nagaraj has an impressive research portfolio, with over 208 indexed publications, 2,736 citations, and an h-index of 32, reflecting the global relevance and scholarly influence of his work. His notable publications include advancements in diabetes prediction, brain tumor classification, Alzheimer’s disease analysis, and cyberattack detection using AI-driven frameworks. His studies on distributed denial-of-service (DDoS) detection, IoT-based healthcare systems, and intelligent recommendation models have been widely cited and applied across multiple interdisciplinary domains.In recognition of his outstanding research, Dr. Nagaraj has been consecutively listed among the World’s Top 2% Scientists (2023–2025), highlighting his sustained impact in computer science and data-driven innovation. He is also a two-time recipient of the prestigious India AI Fellowship (Ministry of Electronics and Information Technology, MeitY), each worth ₹1 Lakh, for his pioneering projects titled AgriTech of Next-Gen Automation for Sustainable Crop Production and A Deep Learning Approach to Improve Pulmonary Cancer Diagnosis Using CNN.Through collaborations with national and international scholars, Dr. Nagaraj continues to advance the frontier of intelligent data analytics for societal benefit. His research contributes significantly to sustainable digital transformation, healthcare improvement, and agricultural innovation, positioning him as a leading figure in India’s AI research landscape and a global advocate for technology-driven social progress.

Profiles: Google Scholar | ORCID  | Scopus

Featured Publications

1.Sudar, K. M., Beulah, M., Deepalakshmi, P., Nagaraj, P., & Chinnasamy, P. (2021). Detection of distributed denial of service attacks in SDN using machine learning techniques. In Proceedings of the 2021 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1–6). IEEE. Cited By : 158

2.Nagaraj, P., & Deepalakshmi, P. (2022). An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis. International Journal of Imaging Systems and Technology, 32(4), 1373–1396. Cited By : 100

3.Nagaraj, P., Muneeswaran, V., Reddy, L. V., Upendra, P., & Reddy, M. V. V. (2020). Programmed multi-classification of brain tumor images using deep neural network. In Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1–6). IEEE. Cited By : 85

4.Nagaraj, P., Deepalakshmi, P., & Romany, F. M. (2021). Artificial flora algorithm-based feature selection with gradient boosted tree model for diabetes classification. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 14, 2789–2802. Cited By : 79

.5.Nagaraj, P., & Deepalakshmi, P. (2020). A framework for e-healthcare management service using recommender system. Electronic Government, an International Journal, 16(1–2), 84–100. Cited By : 70

Dr. P. Nagaraj’s research advances global innovation by integrating artificial intelligence and data analytics to address critical challenges in healthcare, agriculture, and cybersecurity. His vision is to harness intelligent automation and explainable AI to create sustainable, data-driven solutions that enhance human well-being, industrial efficiency, and societal resilience.

Xiangfu Kong | BigData and LargescaleVision | Best Researcher Award

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Dr. Xiangfu Kong | BigData and LargescaleVision | Best Researcher Award

Assistant Researcher | Zhejiang Lab | China

Dr. Xiangfu Kong is a distinguished researcher at Zhejiang Lab, specializing in intelligent transportation systems (ITS), spatiotemporal data analytics, and urban mobility optimization. His work bridges computer science, artificial intelligence, and transportation engineering to develop data-driven models that enhance mobility efficiency safety, and sustainability in smart cities.With an Publications 6  h-index of 3, and 67 citations across recognized publications, Dr. Kong has made notable scholarly contributions to the field. He has published six peer-reviewed research articles, including influential works such as “Measuring Traffic Congestion with Taxi GPS Data and Travel Time Index and  A Scenario-Based Map-Matching Algorithm for Complex Urban Road Networks. His recent studies explore flood risk mapping, travel time reliability, and natural language processing for urban data interpretation, showcasing his interdisciplinary expertise.Dr. Kong’s research projects often involve large-scale real-world data, particularly GPS-based urban mobility and hydrological data, integrating AI algorithms and Bayesian frameworks to model and predict transportation dynamics under diverse conditions. His studies have direct implications for urban policy-making, disaster management, and infrastructure resilience.He has actively collaborated with industry and academic partners to design computational models that assist in traffic monitoring, path planning, and flood management, contributing to sustainable urban development initiatives. Dr. Kong’s innovative use of AI for understanding urban systems highlights his dedication to applying research outcomes to societal benefit.In addition to his publications, Dr. Kong contributes to the broader scientific community through editorial and peer-review roles in transportation and data science journals. His ongoing work in data-driven transportation intelligence and urban informatics positions him as a promising researcher contributing to the next generation of smart mobility systems.Through his research excellence and cross-disciplinary collaborations, Dr. Xiangfu Kong continues to push the boundaries of how AI and data analytics can transform urban transportation, improve public safety, and drive global sustainability efforts.

Profiles: Google Scholar | ORCID | Scopus 

Featured Publications

1. Kong, X., Yang, J., & Yang, Z. (2015). Measuring traffic congestion with taxi GPS data and travel time index. Proceedings of the CICTP 2015, 3751–3762. Cited By : 35

2. Kong, X., & Yang, J. (2019). A scenario-based map-matching algorithm for complex urban road network. Journal of Intelligent Transportation Systems, 23(6), 617–631.
Cited By : 19

3. Kong, X., Yang, J., Qiu, J., Zhang, Q., Chen, X., Wang, M., & Jiang, S. (2022). Post‐event flood mapping for road networks using taxi GPS data. Journal of Flood Risk Management,  Cited By : 8

4. Xiangfu, K., Bo, D., Xu, K., & Yongliang, T. (2023). Text classification model for livelihood issues based on BERT: A study based on hotline compliant data of Zhejiang province. Acta Scientiarum Naturalium Universitatis Pekinensis, 59(3), 456–466. Cited By : 3

5. Kong, X., & Yang, J. (2016). Path planning with information on travel time reliability. Proceedings of the CICTP 2016, 99–107. Cited By :  2

6. Kong, X., Yang, J., Xu, K., Dong, B., & Jiang, S. (2023). A Bayesian updating framework for calibrating hydrological parameters of road network using taxi GPS data. Hydrology and Earth System Sciences Discussions, 1–25.

Dr. Xiangfu Kong nresearch advances data-driven intelligent transportation and urban informatics, fostering safer, more efficient, and sustainable mobility systems. His innovative integration of AI, GPS analytics, and hydrological modeling contributes to scientific progress, climate-resilient infrastructure, and smart city innovation with lasting global impact.