Mehdi Moayed Mohseni | Machine Learning | Best Researcher Award

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Assist. Prof. Dr. Mehdi Moayed Mohseni | Machine Learning | Best Researcher Award

Islamic Azad University Science and Research Branch | Iran

Assist. Prof. Dr. Mehdi Moayed Mohseni is a distinguished chemical engineer at Islamic Azad University, Tehran, Iran, with expertise in non-Newtonian fluid mechanics, convective heat transfer, viscoelastic fluids, rheology, and exergy analysis. He earned his Ph.D. in Chemical Engineering from Amirkabir University of Technology, Iran, focusing on hydrodynamic and heat transfer modeling and entropy analysis of viscoelastic fluids in centric and eccentric annuli, followed by an M.Sc. on heat transfer of Giesekus viscoelastic fluids and a B.Sc. on biological natural gas sweetening processes. His research integrates analytical and semi-analytical methods (HPM, perturbation, homotopy) with computational fluid dynamics (CFD) and mass/heat transfer studies. Assist. Prof. Dr. Mehdi Moayed Mohseni has authored 17 publications, including studies on thermal and rheological performance of nanofluids, contributing to 229 citations and an h-index of 8. He actively participates in conferences, such as the National Iranian Chemical Engineering Congress, and his work demonstrates a strong commitment to advancing understanding of complex fluid behavior and transport phenomena.

Profile: Scopus | Google Scholar

Featured Publications

Mohseni, M. M., Jouyandeh, M., Sajadi, S. M., Hejna, A., Habibzadeh, S., … (2022). Metal-organic frameworks (MOF) based heat transfer: A comprehensive review. Chemical Engineering Journal, 449, 137700.

Montazeri, N., Salahshoori, I., Feyzishendi, P., Miri, F. S., Mohseni, M. M., … (2023). pH-sensitive adsorption of gastrointestinal drugs (famotidine and pantoprazole) as pharmaceutical pollutants by using the Au-doped@ ZIF-90-glycerol adsorbent: Insights from … Journal of Materials Chemistry A, 11(47), 26127–26151.

Salahshoori, I., Vaziri, A., Jahanmardi, R., Mohseni, M. M., Khonakdar, H. A. (2024). Molecular simulation studies of pharmaceutical pollutant removal (rosuvastatin and simvastatin) using novel modified-MOF nanostructures (UIO-66, UIO-66/chitosan, and UIO-66 …). ACS Applied Materials & Interfaces, 16(20), 26685–26712.

Mohseni, M. M., & Rashidi, F. (2010). Viscoelastic fluid behavior in annulus using Giesekus model. Journal of Non-Newtonian Fluid Mechanics, 165(21-22), 1550–1553.

Bateni, A., Salahshoori, I., Jorabchi, M. N., Mohseni, M. M., Asadabadi, M. R., … (2025). Molecular simulation-based assessing of a novel metal-organic framework modified with alginate and chitosan biopolymers for anionic reactive black 5 and cationic crystal violet … Separation and Purification Technology, 354, 128986.

Kachi Anvesh | Machine Learning | Best Researcher Award

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Mr. Kachi Anvesh | Machine Learning | Best Researcher Award

Vardhaman College of Engineering | India

Mr. Kachi Anvesh is an Assistant Professor in the Department of Information Technology at Vardhaman College of Engineering, Hyderabad, with over 12 years of teaching and research experience. He is currently pursuing a Ph.D. in Computer Science at Visvesvaraya Technological University, Belagavi, and holds an M.Tech in Software Engineering with distinction and a B.Tech in Information Technology. His research focuses on medical image processing, deep learning, machine learning, and intelligent systems, with notable contributions including the detection of tessellated retinal disease, hypertensive retinopathy, glaucoma, cataract, and wheat head detection using advanced AI models. He has published in reputed journals and conferences such as JIKM, TSP-CMES, and Journal of Autonomous Intelligence, accumulating 13 citations and an h-index of 2. Mr. Anvesh has led innovative projects including bone age detection from X-ray images, facial expression recognition, emotion detection, foreign object debris detection, and predictive analytics systems, and holds certifications in AI and deep learning from IIT Ropar and other platforms, reflecting his strong contribution to engineering and AI research.

Profile: Scopus | Orcid | Google Scholar

Featured Publications

Anvesh, K., Prasad, S., Laxman, V. V. S. R., & Narayana, B. S. (2019). Automatic student analysis and placement prediction using advanced machine learning algorithms. International Journal of Innovative Technology and Exploring Engineering, 8, 9.

Suma, K., Sunitha, G., Karnati, R., Aruna, E. R., Anvesh, K., Kale, N., & Kishore, P. K. (2024). CETR: CenterNet-Vision transformer model for wheat head detection. Journal of Autonomous Intelligence, 7(3), 6.

Venkatesh, M., Dhanalakshmi, C., Adapa, A., Manzoor, M., & Anvesh, K. (2023). Criminal face detection system.

Anvesh, K., Srilatha, M., Raghunadha Reddy, T., Gopi Chand, M., & Jyothi, G. D. (2018). Improving student academic performance using an attribute selection algorithm. Proceedings of the First International Conference on Artificial Intelligence and Cognitive…, 3.

Rajendar, B., Bhavana, K., Divya, C., Swarna, M., & Anvesh, K. (2017). Evaluation of cardiac tonic activity of methanolic leaf extract of Moringa oleifera. International Journal of Pharma Sciences and Research, 8(6), 152–156.

Temitayo Fagbola | Machine Learning | Best Researcher Award

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Dr. Temitayo Fagbola | Machine Learning | Best Researcher Award

Dr. Temitayo Fagbola, University of Hull, England, United Kingdom

Dr. Temitayo Matthew Fagbola is a Teaching Fellow at the University of Hull, UK, specializing in Applied Artificial Intelligence, with research interests in generative AI, medical imaging, NLP, and ethical AI systems. He holds a PhD in Computer Science from LAUTECH, Nigeria, and has extensive academic experience in Nigeria, South Africa, and the UK. A Fellow of the Higher Education Academy (FHEA), he has earned multiple research grants and awards, including excellence in feedback and teaching. Dr. Fagbola has over 480 citations and serves on several editorial boards and technical committees.

Publication Profile

Scopus

Google Scholar

🎓 Educational Background

Dr. Temitayo Fagbola possesses a strong academic foundation in Computer Science. He recently completed a Postgraduate Certificate in Academic Practice at the University of Hull, UK (2023–2024) 🎓. He earned his Ph.D. in Computer Science from Ladoke Akintola University of Technology, Nigeria (2012–2015) 🧠, following an M.Sc. in Computer Science from the University of Ibadan (2009–2011) 💻. His academic journey began with a B.Tech. (Hons) in Computer Science from LAUTECH (2002–2007) 📘. This diverse educational background underpins his expertise in AI, data science, and academic teaching and research.

💼 Professional Experience

Dr. Temitayo Fagbola is currently a Teaching Fellow at the Centre of Excellence in Data Science, AI, and Modelling, University of Hull, UK (Oct. 2022–Present) 🇬🇧. He has served as a Senior Lecturer at FUOYE, Nigeria (2021–2022) and held research roles at Durban University of Technology, South Africa 🇿🇦. His academic journey includes roles as Lecturer and Assistant Lecturer at FUOYE (2012–2018) 👨‍🏫. His work focuses on Applied AI in Health 🧠, with expertise in CNNs, LLMs, denoising autoencoders, transfer learning, computer vision, NLP, and AI ethics

🏅 Honours, Awards

Dr. Temitayo Fagbola was awarded the prestigious Fellowship of the Higher Education Academy (FHEA), UK 🇬🇧 in June 2024. He won the Excellence in Feedback award and was a finalist for Excellence in Teaching at the University of Hull 🏆. His accolades include travel grants to NeurIPS 2019 in Canada 🇨🇦, FAT* Conference in the USA 🇺🇸, and Deep Learning events in South Africa 🇿🇦. He held a Postdoctoral Fellowship at Durban University of Technology and received a Best Paper Award in 2014 📝. His recognitions span academia, teaching excellence, and global AI forums

📜 Professional Certifications

Dr. Temitayo Fagbola holds multiple certifications including Aviatrix Multicloud Network Associate 🌐, Machine Learning Applications from Global AI Hub 🤖, and two Huawei ICT Associate credentials in Big Data and Routing & Switching 📊📡. He actively contributes to academic service as a reviewer on the FoSE Research Ethics Committee 🧪 and a member of the Recognised Teacher Status Working Group at the University of Hull 🇬🇧. As a module leader and lecturer in Applied AI 📘, he has co-supervised seven MSc dissertations and one PhD thesis, nurturing the next generation of AI and CS researchers

🔍 Research Focus

Dr. Temitayo Fagbola’s research lies at the intersection of Artificial Intelligence 🤖, Machine Learning 📈, and Cloud Computing ☁️, with impactful work in email classification ✉️, timetabling optimization 📅, and AI ethics ⚖️. His contributions span Natural Language Processing 🗣️, Computer Vision 🖼️, and human-centered AI systems 👥, often integrating metaheuristic algorithms and deep learning for real-world challenges. He’s also active in educational technology 🎓, COVID-19 smart health solutions 😷, and AI-powered predictive systems, showing a strong commitment to applied AI in public services and education sectors 🌍. His publications are widely cited, reflecting global scholarly influence

Conclusion

Dr. Temitayo Fagbola’s innovative research, international recognition, publication impact, and commitment to academic excellence, he is an excellent candidate for the Best Researcher Award. His work addresses real-world problems through advanced AI methods, making him not only a researcher of merit but a contributor to the global AI and data science community.

Publication Top Notes

📘 Computer-based test (CBT) system for university academic enterprise examination – 108 citations – 📅 2013
☁️ The Impact and Challenges of Cloud Computing Adoption on Public Universities – 93 citations – 📅 2014
📩 Hybrid GA-SVM for efficient feature selection in e-mail classification – 51 citations – 📅 2012
📚 Cloud Computing: Concepts, Architecture & Applications – 37 citations – 📅 2019
😷 Smart face masks for COVID-19 management – 21 citations – 📅 2022
🧠 Towards AI-based systems: Human-centered requirements – 20 citations – 📅 2019
🧮 Hybrid Metaheuristic Feature Extraction for Timetabling – 19 citations – 📅 2012
📱 Mobile ML Models for Student Performance Prediction – 15 citations – 📅 2018
📧 Optimized Feature Selection for Email Classification – 15 citations – 📅 2014
🎓 Transformational Roles of Edge Intelligence (Special Issue) – 12 citations – 📅 2024
🚀 Survey on Mobile Agent Migration Process – 12 citations – 📅 2016
🏥 ERP Implementation in Hospital Systems – 11 citations – 📅 2023