Tag: AI Innovation Award

🎓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.

Rania Loukil | Deep Learning | Best Scholar Award

Published on 03/05/202503/05/2025 by Network Science

Mr. Rania Loukil | Deep Learning | Best Scholar Award

Maitre Assistant at Ecole Nationale d’Ingenieurs de Tunis, Tunisia

Dr. Rania Loukil is a Tunisian researcher and academic specializing in Artificial Intelligence, Embedded Systems, and Control Engineering. Currently serving as a Maître Assistant (Assistant Professor) at the Higher Institute of Technology and Computer Science (ISTIC), University of Carthage, she has over a decade of experience in teaching, research, and interdisciplinary collaboration. Her research merges deep learning with practical domains like IoT, smart grids, and fault diagnosis, reflecting a strong commitment to innovation and applied AI solutions.

🔹Professional Profile:

🎓Education Background

Ph.D. in Electrical Engineering, National Engineering School of Sfax (ENIS), University of Sfax, Tunisia | 2010–2014
Master Project, INRIA Paris / ENIS | 2008–2009
Engineering Degree in Electrical Engineering, ENIS, Sfax | 2005–2008
Preparatory Classes (MP), IPEIS, Sfax | 2003–2005
Baccalaureate in Mathematics, Tunisia | 2002–2003 – Mention Bien

💼 Professional Development

Maître Assistant in Artificial Intelligence, ISTIC, University of Carthage | Jan 2018–Present
Coach Junior, BIAT Foundation | Nov 2018–Present
Maître Assistant in AI, ISI Gabes | Sep 2015–Dec 2017
Head of Electrical Engineering Department, Ecole Polytechnique Centrale Privée de Tunis | Feb 2015–Aug 2015
Permanent Faculty, Ecole Polytechnique Centrale Privée de Tunis | Oct 2014–Jan 2015

🔬Research Focus

Artificial Intelligence & Deep Learning (RNNs, Transformers, Bayesian Networks)
Fault Diagnosis and Nonlinear Control (Sliding Mode, Observers)
IoT and Embedded Systems
Smart Grids and Microgrid Energy Management
Nanocomposite Classification and Materials Informatics

📈Author Metrics:

Published in leading journals including Expert Systems with Applications and Scientific Reports
Recent works involve hybrid deep learning approaches for nanocomposite classification and smart energy systems
Selected publications:
- Classification of Nanocomposites using RNN Transformer & Bayesian Network, ESWA, 2025
- Probabilistic and Deep Learning Approaches for Conductivity-Driven Nanocomposite Classification, Scientific Reports, 2025
- IoT Solution for Energy Management, IREC 2023

🏆Awards and Honors:

Recognized contributor to interdisciplinary AI projects
Regular presenter at international conferences on AI, control systems, and energy informatics
Acknowledged for excellence in education and mentorship through BIAT Foundation coaching initiatives

📝Publication Top Notes

1. Classification of a Nanocomposite Using a Combination Between Recurrent Neural Network Based on Transformer and Bayesian Network for Testing the Conductivity Property

Journal: Expert Systems with Applications
Publication Date: April 2025
DOI: 10.1016/j.eswa.2025.126518
ISSN: 0957-4174
Authors: Wejden Gazehi, Rania Loukil, Mongi Besbes
Abstract: This study presents a hybrid AI model combining Transformer-based RNN and Bayesian Networks to classify nanocomposites based on conductivity, demonstrating improved interpretability and predictive accuracy.

2. Probabilistic and Deep Learning Approaches for Conductivity-Driven Nanocomposite Classification

Journal: Scientific Reports
Publication Date: March 7, 2025
DOI: 10.1038/s41598-025-91057-1
ISSN: 2045-2322
Authors: Wejden Gazehi, Rania Loukil, Mongi Besbes
Abstract: This paper explores probabilistic learning and deep learning methods for classifying nanocomposites with a focus on electrical conductivity, emphasizing model generalizability.

3. Enhanced Nanoparticle Classification Through Optimized Artificial Neural Networks

Conference: 2024 International Conference on Decision Aid Sciences and Applications (DASA)
Presentation Date: December 11, 2024
DOI: 10.1109/dasa63652.2024.10836425
Authors: Wejden Gazehi, Rania Loukil, Mongi Besbes
Abstract: The paper demonstrates how optimized ANN architectures can significantly improve nanoparticle classification in terms of conductivity profiling, offering an efficient pipeline for smart material characterization.

4. Improving the Classification of a Nanocomposite Using Nanoparticles Based on a Meta-Analysis Study, Recurrent Neural Network and Recurrent Neural Network Monte-Carlo Algorithms

Journal: Nanocomposites
Publication Date: July 8, 2024
DOI: 10.1080/20550324.2024.2367181
ISSN: 2055-0324, 2055-0332
Authors: Rania Loukil, Wejden Gazehi, Mongi Besbes
Abstract: Through a comparative analysis using RNN and Monte-Carlo RNN algorithms, this work proposes a robust framework for classifying nanocomposites, supported by meta-analytical insights.

5. Design and Implementation of an IoT Solution for Energy Management\

Conference: 14th International Renewable Energy Congress (IREC 2023)
Presentation Date: December 16, 2023
Authors: Rania Loukil, Neila Bediou, Hatem Oueslati, Majdi Hazami
Abstract: This contribution introduces a practical IoT-based architecture for optimizing energy consumption and monitoring within renewable energy systems, aligning with smart grid principles.

.Conclusion:

Dr. Rania Loukil stands out as an exemplary scholar combining deep learning, embedded systems, and energy informatics. Her cross-disciplinary work addresses both academic challenges and societal needs, aligning well with the objectives of a Best Scholar Award. Given her solid track record, thematic relevance, and academic leadership, she is highly deserving of this recognition.

➡️ Recommendation: Strongly endorse her nomination for the Best Scholar Award, with suggestions to highlight international collaborations, quantitative metrics, and applied impacts during the award presentation or application.

Reham AlDayil | Speech Recognition | Best Researcher Award

Published on 19/02/202519/02/2025 by Network Science

Assist. Prof. Dr. Reham AlDayil | Speech Recognition | Best Researcher Award

Assistant Professor at Imam Mohammed bin Saud Islamic university, Saudi Arabia📖

Dr. Reham Abdulaziz Al-Dayil is an Assistant Professor at Imam Mohammed bin Saud Islamic University, specializing in computer engineering, cybersecurity, and artificial intelligence. With a strong academic and research background, she has contributed to cutting-edge advancements in open-set classification, remote sensing, and cyber threat detection. She has published extensively in prestigious journals and international conferences, focusing on machine learning applications in cybersecurity and geospatial analysis.

Profile

Education Background🎓

Dr. Al-Dayil earned her Ph.D. in Computer Engineering from King Saud University (2017-2022), where she researched mobile botnet detection using artificial immune systems and user activity correlation. She completed her Master’s in Computer Engineering from King Saud University (2010-2015), with a thesis on social media-based botnet detection. Her academic journey began with a Bachelor’s degree in Computer Science from King Saud University (2002-2006), where she developed a License Plate Extraction System as her final project.

Professional Experience🌱

Dr. Al-Dayil has over 17 years of experience spanning academia and industry. Since 2023, she has been an Assistant Professor at Imam Mohammed bin Saud Islamic University, where she previously served as a Lecturer (2017-2023). Before that, she was a Teaching Assistant at Shaqra University (2009-2017), contributing to curriculum development and student mentorship. Her industry experience includes working as a Developer at AlFanar Company (2006-2009), where she gained hands-on expertise in software development, programming, and database systems. She has taught courses in data communication systems, networking, information security, web programming, database management, and digital logic design.

Research Interests🔬

Dr. Al-Dayil’s research focuses on artificial intelligence, cybersecurity, machine learning, and remote sensing. Her work explores advanced methodologies for open-set classification, domain adaptation, and adversarial learning in cybersecurity. She has contributed to research on vision transformers for remote sensing image classification, cyber threat detection frameworks, and deep learning techniques for cross-scene classification.

Author Metrics

Dr. Al-Dayil has authored multiple research papers published in high-impact journals such as Remote Sensing, International Journal of Remote Sensing, and IEEE IGARSS. Her work has been cited widely in the fields of machine learning and cybersecurity. She collaborates with leading researchers and has presented at international conferences.

Awards & Honors

Dr. Al-Dayil has received several accolades for her academic and research excellence. Her undergraduate project, License Plate Extraction System, secured third place in the Final Project Competition (2006). She has also been recognized for her contributions to cybersecurity and AI-driven research in remote sensing and open-set classification.

Publications Top Notes 📄

1. Vision Transformers for Remote Sensing Image Classification

Authors: Y. Bazi, L. Bashmal, M. M. A. Rahhal, R. A. Dayil, N. A. Ajlan
Journal: Remote Sensing, Volume 13, Issue 3, Article 516
Year: 2021
Citations: 460
Summary: This study explores the use of Vision Transformers (ViTs) for remote sensing image classification, demonstrating their effectiveness in capturing spatial dependencies in satellite imagery compared to traditional CNN models.

2. Deep Open-Set Domain Adaptation for Cross-Scene Classification Based on Adversarial Learning and Pareto Ranking

Authors: R. Adayel, Y. Bazi, H. Alhichri, N. Alajlan
Journal: Remote Sensing, Volume 12, Issue 11, Article 1716
Year: 2020
Citations: 34
Summary: This research presents a novel deep learning framework using adversarial learning and Pareto ranking for open-set domain adaptation, improving classification performance in remote sensing applications with unseen data.

3. Detecting Social Media Mobile Botnets Using User Activity Correlation and Artificial Immune System

Authors: R. A. Al-Dayil, M. H. Dahshan
Conference: 2016 7th International Conference on Information and Communication Systems (ICICS)
Year: 2016
Citations: 10
Summary: This paper introduces a botnet detection method leveraging user activity correlation and artificial immune systems to identify malicious activities on social media-based mobile networks.

4. Energy-Based Learning for Open-Set Classification in Remote Sensing Imagery

Authors: M. M. Al Rahhal, Y. Bazi, R. Al-Dayil, B. M. Alwadei, N. Ammour, N. Alajlan
Journal: International Journal of Remote Sensing, Volume 43, Issues 15-16, Pages 6027-6037
Year: 2022
Citations: 9
Summary: The study introduces an energy-based learning approach to improve open-set classification in remote sensing imagery, enhancing the detection of unknown classes in satellite data.

5. Exploring Cybersecurity Metrics for Strategic Units: A Generic Framework for Future Work

Authors: M. Arafah, S. H. Bakry, R. Al-Dayel, O. Faheem
Book Chapter: Advances in Information and Communication: Proceedings of the 2019 Future of Information and Communication Conference
Year: 2020
Citations: 5
Summary: This paper proposes a framework for cybersecurity metrics, offering insights into measuring and assessing security performance in strategic IT units.

Conclusion

Dr. Reham Abdulaziz Al-Dayil is an exceptional candidate for the Best Researcher Award due to her high-impact publications, interdisciplinary expertise, strong academic presence, and contributions to AI-driven cybersecurity and remote sensing. With continued focus on industry collaborations, research funding, and public engagement, she can further elevate her global impact in research.

Mohammad Reza Nikpour | Artificial Intelligence | Best Researcher Award

Published on 30/01/202530/01/2025 by Network Science

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

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.

Qinglai Wei | Self-Learning Systems | Best Researcher Award

Published on 16/12/202416/12/2024 by Network Science

Prof. Dr. Qinglai Wei | Self-Learning Systems | Best Researcher Award

Associate Director, at Institute of Automation, Chinese Academy of Sciences, China.

Professor Qinglai Wei is a distinguished researcher and educator specializing in control systems, computational intelligence, and learning-based optimization. Serving as the Associate Director at The State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences, he has made significant contributions to adaptive dynamic programming, nonlinear control, and reinforcement learning. With an illustrious academic journey from Northeastern University and rich professional experience, Prof. Wei has authored numerous influential papers, books, and book chapters. His awards include multiple IEEE honors and recognition as a Clarivate Highly Cited Researcher. He is a prominent figure in advancing intelligent control systems and their applications in complex scenarios.

Professional Profile

Scopus

Google Scholar

Education 🎓

Ph.D. in Control Theory and Control Engineering (2009): Northeastern University, China. Advised by Prof. Huaguang Zhang, his research focused on intelligent control systems.
M.S. in Control Theory and Control Engineering (2005): Northeastern University, China, under Prof. Xianwen Gao’s mentorship.
B.S. in Automation (2002): Northeastern University, China, advised by Baodong Xu.
These academic milestones laid the foundation for his expertise in adaptive dynamic programming and intelligent systems.

Professional Experience 💼

Associate Director (2018–Present): The State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences.
Professor (2016–Present): The State Key Laboratory and the School of Artificial Intelligence, University of Chinese Academy of Sciences.
Visiting Scholar roles at University of Rhode Island (2018) and University of Texas at Arlington (2014) reflect his international collaboration and academic outreach.
Earlier roles include Associate and Assistant Professor positions at The State Key Laboratory, showcasing steady growth in his academic career.

Research Interests 🔬

Prof. Wei’s research spans:

Computational Intelligence & Intelligent Control
Learning Control & Reinforcement Learning
Optimal & Nonlinear Control
Adaptive Dynamic Programming
Applications include process control, smart grids, and multi-agent systems. His innovative methods continue to drive advancements in control theory and intelligent systems.

Awards 🏆

Prof. Wei’s excellence is marked by accolades like:

Best Paper Awards (2023 & 2022): International CSIS-IAC and China Automation Congress.
IEEE Outstanding Paper Awards (2018): Recognition for impactful contributions to the IEEE journals.
Highly Cited Researcher (2018 & 2019): By Clarivate Analytics for his influential publications.
Other honors include National Natural Science Foundation Awards and Young Researcher Awards, emphasizing his leadership in the field.

Top Noted Publications 📚

“Learning and Controlling Multiscale Dynamics in Spiking Neural Networks” (2024, IEEE Transactions on Cybernetics): This study employs Recursive Least Square (RLS) modifications to manage multiscale dynamics in spiking neural networks. It advances neural control methods for adaptive tasks in dynamic environments【8】.
“Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent Systems” (2024, IEEE/CAA Journal of Automatica Sinica): This paper focuses on event-triggered mechanisms to ensure robust consensus in multiagent systems under parallel optimal control.
“Primal-Dual Adaptive Dynamic Programming for Nonlinear Systems” (2024, Automatica): A framework using primal-dual adaptive dynamic programming tackles the stabilization and optimization of nonlinear systems.
“Class-Incremental Learning with Balanced Embedding Discrimination” (2024, Neural Networks): This work enhances class-incremental learning by introducing techniques to balance embeddings and improve discrimination among new and existing classes.

Conclusion

Qinglai Wei is exceptionally suited for the Research for Best Researcher Award. His prolific contributions to control theory, computational intelligence, and reinforcement learning, combined with his global recognition and leadership, exemplify his stature as a world-class researcher. With a proven track record of innovative research, impactful publications, and numerous accolades, he stands out as a strong candidate for this prestigious honor. Continued expansion into interdisciplinary collaborations and mentorship initiatives will further solidify his legacy as a pioneering researcher.

Sathishkumar Moorthy | Computer Vision | Best Researcher Award

Published on 13/12/202413/12/2024 by Network Science

Dr. Sathishkumar Moorthy | Computer Vision | Best Researcher Award

Post-Doctoral Researcher at Sejong University, South Korea📖

Dr. Sathishkumar Moorthy is an accomplished researcher specializing in artificial intelligence (AI), machine learning (ML), and deep learning (DL) with a focus on computer vision applications. With a proven track record in innovative research, he has developed cutting-edge techniques for video object detection, human emotion recognition, and intelligent surveillance systems. His expertise includes self-attention-based models, image processing, and multimodal data analysis. Dr. Moorthy has contributed to academia and industry through impactful publications and collaborative research projects, striving to advance computer vision and AI technology.

Profile

Education Background🎓

Dr. Sathishkumar Moorthy earned his Doctorate of Philosophy (Ph.D.) from Kunsan National University, South Korea (2017–2024), with a commendable CGPA of 4.16. His doctoral thesis focused on developing an enhanced self-attention-based Vision Transformer model for robust video object detection systems. He completed his Master of Engineering (M.E.) in 2013 from Karpagam Academy of Higher Education, Tamil Nadu, India, achieving an impressive CGPA of 9.05. His master’s thesis explored automatic diagnosis of breast cancer lesions using Gaussian Mixture Model and Expectation-Maximization algorithms. He holds a Bachelor of Engineering (B.E.) in Computer Science and Engineering from Anna University, Tamil Nadu, India (2011), graduating with a CGPA of 7.87. His undergraduate thesis analyzed and compared parsing techniques for asynchronous messages.

Professional Experience🌱

Dr. Sathishkumar has accumulated extensive experience across academia, industry, and research roles. He is currently a Post-Doctoral Researcher at Sejong University, South Korea (2024–Present), focusing on multimodal human emotion recognition using advanced Transformer-based models. Prior to this, he served as Manager of the AI Research Team at Smart Vision Tech Inc., Seoul, where he specialized in developing advanced object detection and segmentation algorithms, leveraging frameworks such as YOLO and Faster R-CNN. His teaching experience includes roles as Assistant Professor at Karpagam College of Engineering (2017) and J.K.K. Munirajah College of Technology (2013–2016) in Tamil Nadu, India, where he delivered lectures on programming, data structures, and algorithms and conducted workshops on mobile application development and genetic algorithms.

Research Interests🔬

Dr. Moorthy’s research focuses on:

Computer Vision: Video object detection, intelligent surveillance systems, and multimodal emotion recognition.
Artificial Intelligence: Deep learning, Transformer models, and advanced neural network architectures.
Industry Applications: Real-time fault detection, anomaly tracking, and autonomous systems using AI/ML techniques.
Medical Imaging: Image segmentation and diagnosis using probabilistic and ML algorithms.

Author Metrics

Dr. Sathishkumar Moorthy has made significant contributions to the field of computer vision and artificial intelligence through his research and publications. His works focus on advanced AI/ML techniques, including Vision Transformers, multimodal emotion recognition, and object detection, particularly for real-world applications such as video surveillance and medical imaging.

He has authored several high-impact research papers in reputable journals and conferences, reflecting his expertise in image processing, deep learning, and robotics. His research output has garnered notable citations, showcasing the relevance and influence of his work in the academic and research communities. Dr. Sathishkumar’s Google Scholar profile highlights his active contributions to advancing AI-driven solutions for complex problems, affirming his position as a dedicated researcher in the field.

Publications Top Notes 📄

1. Distributed Leader-Following Formation Control for Multiple Nonholonomic Mobile Robots via Bioinspired Neurodynamic Approach

Authors: S. Moorthy, Y.H. Joo
Journal: Neurocomputing
Volume: 492
Pages: 308–321
Year: 2022
Citations: 43
DOI/Link: [Check Neurocomputing journal for more details]

2. Gaussian-Response Correlation Filter for Robust Visual Object Tracking

Authors: S. Moorthy, J.Y. Choi, Y.H. Joo
Journal: Neurocomputing
Volume: 411
Pages: 78–90
Year: 2020
Citations: 31
DOI/Link: [Check Neurocomputing journal for more details]

3. Adaptive Spatial-Temporal Surrounding-Aware Correlation Filter Tracking via Ensemble Learning

Authors: S. Moorthy, Y.H. Joo
Journal: Pattern Recognition
Volume: 139
Article Number: 109457
Year: 2023
Citations: 21
DOI/Link: [Check Pattern Recognition journal for more details]

4. Multi-Expert Visual Tracking Using Hierarchical Convolutional Feature Fusion via Contextual Information

Authors: S. Moorthy, Y.H. Joo
Journal: Information Sciences
Volume: 546
Pages: 996–1013
Year: 2021
Citations: 21
DOI/Link: [Check Information Sciences journal for more details]

5. Instinctive Classification of Alzheimer’s Disease Using fMRI, PET, and SPECT Images

Authors: E. Dinesh, M.S. Kumar, M. Vigneshwar, T. Mohanraj
Conference: 7th International Conference on Intelligent Systems and Control (ISCO)
Year: 2013
Citations: 15
Pages: Available in the ISCO conference proceedings.

Conclusion

Dr. Sathishkumar Moorthy is an exemplary researcher whose work significantly contributes to advancing AI, ML, and computer vision. His combination of academic rigor, industry experience, and impactful research publications makes him a strong candidate for the Best Researcher Award.

Nithya Rekha Sivakumar | Deep Learning | Best Researcher Award

Published on 18/11/202418/11/2024 by Network Science

Dr. Nithya Rekha Sivakumar | Deep Learning | Best Researcher Award

Associate Professor, Princess Nourah Bint Abdulrahman University, Saudi Arabia📖

Dr. Nithya Rekha Sivakumar is an accomplished academician and researcher, currently serving as an Associate Professor of Computer Science at the College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia. She holds a Ph.D. in Computer Science from Periyar University, India, specializing in Mobile Computing and Wireless Networks with Fuzzy and Rough Set Techniques, funded by a prestigious UGC BSR Fellowship. Dr. Sivakumar also earned her M.Phil. in Data Mining, MCA in Computer Applications, and B.Sc. in Computer Science. With over 15 years of academic experience, she has served in diverse roles across reputed institutions in India and Saudi Arabia. Her research interests include wireless networks, mobile computing, data mining, and intelligent systems, with extensive contributions as a researcher, reviewer, and speaker in international conferences and journals. A recipient of multiple awards, including the “Best Distinguished Researcher Award,” she has secured research grants and actively evaluates Ph.D. theses globally. Dr. Sivakumar is also a member of IEEE and IAENG and continues to contribute to advancements in computing through teaching, research, and scholarly activities.

Profile