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.

Tzu-Chien Wang | AI | Best Researcher Award

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Assist. Prof. Dr. Tzu-Chien Wang | AI | Best Researcher Award

Tzu-Chien Wang at Department of Computer Science and Information Management Soochow University, Taiwan

Dr. Tzu-Chien Wang is an Assistant Professor in the Department of Computer Science and Information Management at Soochow University. He specializes in artificial intelligence, data mining, decision support systems, and process improvement techniques. With a strong background in machine learning, natural language processing, and predictive modeling, he has contributed significantly to both academia and industry by developing proof-of-concept models for operational processes.

Professional Profile:

Orcid

Google Scholar

Education Background

Dr. Tzu-Chien Wang earned his Ph.D. in Business Administration from National Taiwan University, where he specialized in data-driven decision-making, artificial intelligence applications, and business intelligence. His doctoral research focused on leveraging machine learning, data mining, and optimization techniques to enhance decision support systems and operational efficiency. His academic training has provided him with a strong foundation in predictive modeling, natural language processing, and process improvement methodologies, which he has effectively applied in both research and industry settings.

Professional Development

Dr. Wang has a diverse professional background, spanning academia, industry, and research institutions. Before joining Soochow University in 2025, he served as an Assistant Professor at Mackay Junior College of Medicine, Nursing, and Management. He also held managerial roles in data development at VisualSoft Information System Co., Ltd. and worked as a Senior Data Analyst at Fubon Life Insurance Co., Ltd. Additionally, he contributed as an Assistant Research Fellow at the Commerce Development Research Institute, focusing on international digital commerce.

Research Focus

His research interests include artificial intelligence, data mining, decision support systems, natural language processing, optimization, clustering, classification, and predictive model building. He is particularly engaged in developing AI-driven solutions for business intelligence, healthcare applications, and digital transformation.

Author Metrics:

Dr. Wang has published extensively in AI, data analytics, and business intelligence. His research contributions can be found on Google Scholar, reflecting his impact on data science and AI applications.

Awards and Honors:

  • High-Age Health Smart Medical Care Industry-Academia Alliance, National Science and Technology Council, Taiwan (2025–2028)

  • AI+BI Agile Development Data Platform Project, Ministry of Economic Affairs, Taiwan (2022)

  • Consumer Data-Driven Precision R&D and Manufacturing (C2M) Promotion Project, Bureau of Energy, Taiwan (2021)

Publication Top Notes

1. Deep Learning-Based Prediction and Revenue Optimization for Online Platform User Journeys

  • Author: T.C. Wang
  • Journal: Quantitative Finance and Economics (2024)
  • Type: Research Article
  • Citations: 6
  • Summary: This study utilizes deep learning techniques to predict user behavior and optimize revenue generation on online platforms, improving personalized recommendations and business strategies.

2. An Integrated Data-Driven Procedure for Product Specification Recommendation Optimization with LDA-LightGBM and QFD

  • Authors: T.C. Wang, R.S. Guo, C. Chen
  • Journal: Sustainability (2023)
  • Type: Research Article
  • Citations: 5
  • Summary: This research presents a hybrid framework combining Latent Dirichlet Allocation (LDA), LightGBM, and Quality Function Deployment (QFD) to optimize product specification recommendations, improving efficiency in sustainable manufacturing.

3. Integrating Latent Dirichlet Allocation and Gradient Boosting Tree Methodology for Insurance Product Development Recommendation

  • Authors: W.Y. Chen, T.C. Wang, R.S. Guo, C. Chen
  • Conference: Proceedings of the 9th International Conference on Big Data Analytics (ICBDA) (2024)
  • Type: Conference Paper
  • Citations: 1
  • Summary: This paper integrates LDA and Gradient Boosting Trees to refine insurance product development recommendations, offering a data-driven approach for personalized insurance solutions.

4. Data Mining Methods to Support C2M Product-Service Systems Design and Recommendation System Based on User Value

  • Authors: T.C. Wang, R.S. Guo, C. Chen
  • Conference: 2022 Portland International Conference on Management of Engineering and Technology (PICMET)
  • Type: Conference Paper
  • Citations: 1
  • Summary: This study explores data mining techniques to enhance Consumer-to-Manufacturer (C2M) product-service system design, optimizing recommendation systems based on user value analysis.

5. Customer Demand Evaluation Method

  • Author: T.C. Wang
  • Patent: TW Patent TW202,414,306 A (2024)
  • Type: Patent
  • Summary: This patent presents a novel method for evaluating customer demand using AI-driven analytics, enhancing precision in product development and market segmentation.

Conclusion

Dr. Tzu-Chien Wang is a strong candidate for the Best Researcher Award, given his expertise in AI, machine learning, and business intelligence, along with his demonstrated contributions to academia and industry. His innovative research, patents, and funded projects underscore his impact. By expanding global collaborations, diversifying his research themes, and increasing engagement in AI policy and ethics, he can further solidify his standing as a leading researcher in artificial intelligence

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

Orcid Profile

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.