R S Shaji | AI | Outstanding Educator Award

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Dr. R S Shaji | AI | Outstanding Educator Award

Professor at St. Xavier’s Catholic College of Engineering, India

Dr. R.S. Shaji is a distinguished academician, administrator, and researcher with over 26 years of teaching and 22 years of administrative experience in Computer Science and Engineering. Currently serving as Dean (Systems) and Professor at St. Xavier’s Catholic College of Engineering, Tamil Nadu, he is also a recognized NAAC Assessor and a doctoral supervisor at Anna University and Noorul Islam University. With extensive contributions to the domains of Machine Learning, Smart Grid Computing, Cyber Security, and Cloud Computing, he has successfully produced 8 Ph.D. graduates and is presently guiding 10 doctoral scholars.

🔹Professional Profile:

Scopus Profile

Orcid Profile

Google Scholar Profile

🎓Education Background

  • Ph.D. in Computer Science and Engineering (2012) – Manonmaniam Sundaranar University, Tirunelveli

  • M.Tech. in Computer Science and Engineering (2002) – Pondicherry University (Central University), Puducherry

💼 Professional Development

Dr. Shaji has held prominent academic leadership roles including Dean (Research), Head of Department, and Director of Admissions across reputed institutions like St. Xavier’s Catholic College of Engineering and Noorul Islam University. He is a recognized faculty member and supervisor under AICTE, UGC, and Anna University. His experience also extends to three years in the software industry, and he has been deeply involved in curriculum design, institutional accreditation processes, and national missions such as Unnat Bharat Abhiyan and MHRD’s Institution Innovation Council.

🔬Research Focus

His core research domains include:

  • Machine Learning

  • Smart Grid Computing

  • Cyber Security

  • Cloud Computing

  • Blockchain Applications

  • Healthcare and Medical Informatics

📈Author Metrics:

  • Publications: 72 research articles in SCI, Scopus, Web of Science indexed journals, and Google Scholar

  • Conference Papers: 23 papers in reputed national and international conferences (IEEE, etc.)

  • Books & Chapters: 1 National Book, 5 Book Chapters

  • Patents: 1 Design Patent Granted, 1 Technology Patent Published, 1 Design Patent Examined

Awards & Honors

  • Recognized as a NAAC Peer Team Member

  • Reviewer for prestigious publishers: IEEE, Elsevier, Springer, Wiley, Taylor & Francis, IET, and Inderscience

  • Consultant for industry and academia in software and cloud architecture, cybersecurity, healthcare informatics, and e-governance systems

  • Editorial roles in 6 refereed journals (3 international, 3 national)

  • Institutional Coordinator and President for national innovation and safety programs

📝Publication Top Notes

🔐 1. Hybrid-CID: Securing IoT with Mongoose Optimization

  • Authors: SM Sheeba, R.S. Shaji
  • Journal: International Journal of Computational Intelligence Systems, Vol. 18(1), pp. 1–18
  • Year: 2025
  • Summary: Proposes a hybrid Cryptographic-Identification (Hybrid-CID) framework enhanced by Mongoose Optimization for robust IoT security.

🚘 2. Enhancing Security in VANETs: Adaptive Bald Eagle Search Optimization-Based Multi-Agent Deep Q Neural Network for Sybil Attack Detection

  • Authors: M. Ajin, R.S. Shaji
  • Journal: Vehicular Communications, Article ID: 100928
  • Year: 2025
  • Summary: Introduces an advanced Sybil attack detection mechanism in Vehicular Ad-Hoc Networks using Adaptive Bald Eagle Search Optimization with Multi-Agent Deep Q-Networks.

🎥 3. Design of Approximate Multiplier for Multimedia Application in Deep Neural Network Pre-Processing

  • Authors: M.D.S., R.S. Shaji, Nelmin Bathlin
  • Conference: 3rd Congress on Control, Robotics and Mechatronics (CCRM)
  • Year: 2025
  • Summary: Develops an energy-efficient approximate multiplier for DNN-based multimedia pre-processing.

➗ 4. Design of Approximate Multiplier Using Highly Compressed 5:2 Counter

  • Authors: R.S. Shaji, S. Hariprasad, S. Shettygari, J.K. Vasan, V. Vijayan
  • Conference: 6th International Conference on Mobile Computing and Sustainable Informatics
  • Year: 2025
  • Summary: Presents a high-performance 5:2 counter-based multiplier aimed at improving computational efficiency in mobile systems.

⚡ 5. Enhancing Smart Grid Security Using BLS Privacy Blockchain With Siamese Bi-LSTM for Electricity Theft Detection

  • Authors: G. Johncy, R.S. Shaji, T.M. Angelin Monisha Sharean, U. Hubert
  • Journal: Transactions on Emerging Telecommunications Technologies, Vol. 36(1), e70033
  • Year: 2025
  • Summary: Proposes a secure smart grid framework using BLS Privacy Blockchain and Siamese Bi-LSTM to detect electricity theft with improved precision.

.Conclusion:

Dr. R.S. Shaji emerges as a strong and deserving candidate for the Research for Outstanding Educator Award. His long-standing commitment to research, mentorship, education leadership, and recent impactful publications in futuristic domains mark him as a transformative academician.

With minor enhancements in global research footprint, commercialization, and metrics transparency, he can not only justify this award but also aspire for national/international fellowships and innovation recognitions.

✔️ Verdict: Highly Suitable and Strongly Recommended for the award.

Mohammad Reza Nikpour | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Mohammad Reza Nikpour | Artificial Intelligence | Best Researcher Award

Mohammad Reza Nikpour at University of Mohaghegh Ardabili, Iran📖

Dr. Mohammad Reza Nikpour is an esteemed scholar in Water Engineering, currently serving as a faculty member at the University of Mohaghegh Ardabili, Iran. His expertise lies in hydrodynamics, river engineering, and water resource management, with extensive contributions to computational modeling and environmental sustainability.

Profile

Scopus Profile

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Google Scholar Profile

Education Background🎓

  • Ph.D. in Water Engineering, University of Mohaghegh Ardabili, Iran
  • M.Sc. in Water Engineering, University of Mohaghegh Ardabili, Iran
  • B.Sc. in Water Engineering, University of Mohaghegh Ardabili, Iran

Professional Experience🌱

Dr. Nikpour has been actively involved in academic research and teaching at the University of Mohaghegh Ardabili. His work focuses on computational hydrodynamics, groundwater quality assessment, and flood prediction modeling. He has collaborated with international researchers and contributed to innovative water management solutions through data-driven models.

Research Interests🔬

Her research interests include:

  • Hydrodynamics and River Engineering
  • Groundwater Quality Assessment
  • Soft Computing and AI Applications in Water Resource Management
  • Flood Prediction and Climate Change Impact Studies

Author Metrics

Dr. Mohammad Reza Nikpour has established a strong academic presence with numerous publications in high-impact journals, including River Research and Applications, Journal of Cleaner Production, and Stochastic Environmental Research and Risk Assessment. His research contributions have been widely recognized, earning him a growing citation count on Google Scholar and an impressive h-index on Scopus (to be verified). As a highly cited researcher in water engineering, his work has significantly influenced hydrodynamics, groundwater quality assessment, and computational water resource management. His ORCID ID is 0000-0003-4332-0525, and his research continues to shape innovative solutions in environmental sustainability and AI-driven water system modeling.

Awards and Honors
  • Recognized for outstanding contributions in hydrodynamic modeling and water resource sustainability.
  • Published multiple high-impact research papers in top-tier journals such as River Research and Applications, Journal of Cleaner Production, and Stochastic Environmental Research and Risk Assessment.
  • Recipient of research grants and funding for pioneering studies in environmental and computational water management.
Publications Top Notes 📄

1. Estimation of daily pan evaporation using two different adaptive neuro-fuzzy computing techniques

  • Authors: H. Sanikhani, O. Kisi, M.R. Nikpour, Y. Dinpashoh
  • Journal: Water Resources Management
  • Volume: 26
  • Pages: 4347-4365
  • Year: 2012
  • Citations: 70
  • Summary: This study applies adaptive neuro-fuzzy inference system (ANFIS) models to estimate daily pan evaporation, comparing their accuracy and efficiency in hydrological forecasting.

2. Experimental and numerical simulation of water hammer

  • Authors: M.R. Nikpour, A.H. Nazemi, A.H. Dalir, F. Shoja, P. Varjavand
  • Journal: Arabian Journal for Science and Engineering
  • Volume: 39
  • Pages: 2669-2675
  • Year: 2014
  • Citations: 48
  • Summary: This paper investigates water hammer phenomena using both experimental methods and numerical simulations, providing insights into fluid dynamics and pipeline safety.

3. Exploring the application of soft computing techniques for spatial evaluation of groundwater quality variables

  • Authors: F. Esmaeilbeiki, M.R. Nikpour, V.K. Singh, O. Kisi, P. Sihag, H. Sanikhani
  • Journal: Journal of Cleaner Production
  • Volume: 276
  • Article: 124206
  • Year: 2020
  • Citations: 31
  • Summary: This research explores soft computing techniques, such as machine learning, for the spatial analysis of groundwater quality, enhancing environmental monitoring and sustainability.

4. Hydrodynamics of river-channel confluence: toward modeling separation zone using GEP, MARS, M5 Tree, and DENFIS techniques

  • Authors: O. Kisi, P. Khosravinia, M.R. Nikpour, H. Sanikhani
  • Journal: Stochastic Environmental Research and Risk Assessment
  • Volume: 33 (4-6)
  • Pages: 1089-1107
  • Year: 2019
  • Citations: 28
  • Summary: The study applies various data-driven models, including gene expression programming (GEP) and M5 Tree, to model separation zones in river confluences, improving hydrodynamic predictions.

5. Application of novel data mining algorithms in prediction of discharge and end depth in trapezoidal sections

  • Authors: P. Khosravinia, M.R. Nikpour, O. Kisi, Z.M. Yaseen
  • Journal: Computers and Electronics in Agriculture
  • Volume: 170
  • Article: 105283
  • Year: 2020
  • Citations: 16
  • Summary: This paper investigates the use of advanced data mining techniques to predict discharge and end depth in trapezoidal channels, optimizing water resource management and agricultural planning.

Conclusion

Dr. Mohammad Reza Nikpour is an exceptional researcher in AI-driven water resource management, making him a strong candidate for the Best Researcher Award. His pioneering work in soft computing and AI applications for hydrology and environmental sustainability sets him apart in his field. Expanding into deep learning, increasing industry collaborations, and engaging in AI conferences could further solidify his leadership in AI for water engineering.

Raheleh Ghouchan Nezhad Noor Nia | Artificial Intelligence | Best Researcher Award

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Dr. Raheleh Ghouchan Nezhad Noor Nia | Artificial Intelligence | Best Researcher Award

Postdoc Researcher at Mashhad University of Medical Sciences, Mashhad, Iran📖

Dr. Raheleh Ghouchan Nezhad Noor Nia is a Senior Data Scientist and Postdoctoral Researcher specializing in the application of machine learning, artificial intelligence, and medical informatics. She is currently based at the Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. Her extensive academic and professional experience spans multiple domains, including medical informatics, AI in medicine, and data science.

Profile

Scopus Profile

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Education Background🎓

  • Postdoc in Medical Informatics – Data Science & AI in Medicine (2022 – Present), Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Ph.D. in Computer Engineering – Software (2016 – 2022), Department of Computer Engineering, Azad University, Mashhad, Iran.
  • Master’s in Computer Engineering – AI and Robotics (2013 – 2015), Department of Computer Engineering, Azad University, Mashhad, Iran.
  • Bachelor’s in Computer Engineering – Software (2009 – 2013), Department of Computer Engineering, Azad University, Mashhad, Iran.

Professional Experience🌱

Dr. Ghouchan Nezhad Noor Nia currently serves as a Postdoctoral Researcher and Senior Data Scientist at Mashhad University of Medical Sciences, where she is involved in several pioneering research projects related to AI in healthcare. In addition to her role as a researcher, she is a lecturer at various institutions, including the Department of Computer Engineering at Mashhad Azad University, Khayyam University, and Toos University. She is also actively contributing as a reviewer for prestigious journals such as Materials Today Communication and the Medical Informatics Europe conferences.

Her collaborative efforts extend internationally, having worked with prominent researchers at Karlsruhe Institute of Technology, Germany. Dr. Ghouchan Nezhad Noor Nia has also led and contributed to numerous conferences and workshops focused on AI in medical sciences and health technology.

Research Interests🔬

Dr. Ghouchan Nezhad Noor Nia’s research interests include the intersection of AI, machine learning, and medical informatics. Her focus is on big data mining, social mining, graph mining, material science, and AI applications in medical diagnostics, specifically in diseases like lupus nephritis and pulmonary thromboembolism. She is also interested in ontology engineering, metadata management, health social networks, deep learning, and point-of-interest recommendation systems

Author Metrics

Dr. Ghouchan Nezhad Noor Nia has contributed to numerous high-impact publications and has an active research profile with publications in reputed journals and conferences. Her work focuses on innovative solutions and machine learning methods to solve complex challenges in healthcare and material science. She has co-authored papers presented at various prestigious international conferences, including the 12th Neuroscience Congress and International Health Literacy Congress. Her Google Scholar profile reflects her growing influence in the field.

Publications Top Notes 📄

1. A Graph-Based k-Nearest Neighbor (KNN) Approach for Predicting Phases in High-Entropy Alloys

  • Authors: R Ghouchan Nezhad Noor Nia, M Jalali, M Houshmand
  • Journal: Applied Sciences
  • Volume: 12
  • Issue: 16
  • Article ID: 8021
  • Year: 2022
  • DOI: 10.3390/app12168021
  • Summary: This paper introduces a graph-based k-nearest neighbor (KNN) algorithm for phase prediction in high-entropy alloys, leveraging machine learning techniques for material science applications.

2. Machine Learning Approach to Community Detection in a High-Entropy Alloy Interaction Network

  • Authors: R Ghouchan Nezhad Noor Nia, M Jalali, M Mail, Y Ivanisenko, C Kübel
  • Journal: ACS Omega
  • Volume: 7
  • Issue: 15
  • Pages: 12978-12992
  • Year: 2022
  • DOI: 10.1021/acsomega.2c02625
  • Summary: This work focuses on community detection in a high-entropy alloy interaction network using machine learning methods, exploring the structure and relationships between various alloy elements.

3. Non-Alcoholic Fatty Liver Disease Diagnosis with Multi-Group Factors

  • Authors: A Arzehgar, RG Nezhad Noor Nia, V Dehdeleh, F Roudi, S Eslami
  • Journal: Healthcare Transformation with Informatics and Artificial Intelligence
  • Pages: 503-506
  • Year: 2023
  • Summary: This paper proposes a novel methodology for diagnosing non-alcoholic fatty liver disease (NAFLD) by considering multiple influencing factors and utilizing advanced informatics and artificial intelligence.

4. RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm

  • Authors: RG Nezhad Noor Nia, M Jalali
  • Journal: Computational Intelligence and Neuroscience
  • Article ID: 8714870
  • Year: 2022
  • DOI: 10.1155/2022/8714870
  • Summary: The paper presents RecMem, a time-aware recommender system that integrates a memetic evolutionary clustering algorithm, aiming to improve recommendation accuracy in dynamic environments.

5. A Community Detection-based Approach in Social Networks to Improve the Equation Analysis in Material Science

  • Authors: R Ghouchan Nezhad Noor Nia, M Jalali, M Houshmand
  • Journal: Journal of Iranian Association of Electrical and Electronics Engineers
  • Volume: 21
  • Issue: 1
  • Year: 2024 (upcoming)
  • Summary: This study proposes a community detection-based approach within social networks to enhance equation analysis methods used in material science, specifically in the context of high-entropy alloys.

Conclusion

Dr. Raheleh Ghouchan Nezhad Noor Nia is a highly deserving candidate for the Best Researcher Award. Her contributions to AI, machine learning, and medical informatics are transformative and have the potential to address critical healthcare challenges. She exhibits strengths in multidisciplinary research, with a particular focus on medical diagnostics and healthcare innovation. With further growth in clinical and practical applications, Dr. Ghouchan Nezhad Noor Nia’s work could have an even broader and more profound impact on both academia and real-world healthcare solutions. Her dedication to research, teaching, and international collaboration makes her an exemplary figure in her field.