Winston Dunn | Artificial Intelligence |  Best Researcher Award

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Dr. Winston Dunn | Artificial Intelligence |  Best Researcher Award

The University Of Kansas Medical Center | United States

Author Profiles

Scopus

Orcid ID

Early Academic Pursuits

Dr. Winston Dunn began his academic journey with a Bachelor of Science in Biochemistry from the University of British Columbia. He pursued his medical degree at Ross University School of Medicine and later completed advanced training at The Chicago Medical School. His strong foundation in internal medicine was built through residency at the Mayo Graduate School of Medicine, followed by specialized fellowships in Gastroenterology at the University of California, San Diego, and in Transplant Hepatology at Mayo Clinic. Parallel to his clinical training, he enriched his expertise with a Master’s in Public Health, highlighting his commitment to blending clinical practice with population health perspectives.

Professional Endeavors

Dr. Dunn’s academic career began as an Instructor in Medicine at the Mayo Clinic College of Medicine (2009–2010). He then joined the University of Kansas Medical Center as an Assistant Professor (2010–2016), rising to Associate Professor (2016–2025), and currently serves as Professor in the Division of Gastroenterology. His steady progression reflects consistent excellence in teaching, research, and clinical leadership. Additionally, his professional licensure across multiple states and board certifications in Internal Medicine, Gastroenterology, and Transplant Hepatology highlight his depth of expertise.

Contributions and Research Focus

Dr. Dunn’s research is primarily centered on liver diseases, with a special focus on nonalcoholic steatohepatitis (NASH), metabolic dysfunction-associated steatohepatitis (MASH), cirrhosis, and outcomes after hepatitis C treatment. He has been the principal investigator in numerous Phase II and Phase III clinical trials, collaborating with leading pharmaceutical companies and institutions. His studies explore genetic predictors of liver disease outcomes, non-invasive biomarkers, and novel therapeutics aimed at reversing fibrosis and improving survival. He also contributed to improving models of care, diagnosis, and patient referral systems in hepatology.

Impact and Influence

Dr. Dunn’s work has had significant clinical implications, advancing knowledge in liver transplantation, NASH therapeutics, and hepatology practice. His role in high-impact trials, including those supported by Gilead Sciences, Novo Nordisk, and Madrigal Pharmaceuticals, positions him as a leader in shaping the future of liver disease management. His active service on the AASLD Alcoholic Liver Disease Steering Committee and Education Subcommittee further demonstrates his influence on clinical guidelines, education, and policy within the hepatology community.

Academic Citations and Recognition

Dr. Dunn’s contributions have been recognized through multiple awards, including the Sheila Sherlock Clinical and Translational Research Award (2012), the AASLD Advanced Transplant Hepatology Fellowship Award (2009), and induction as a Fellow of the American Association for the Study of Liver Diseases (FAASLD) in 2023. His research has generated numerous publications and citations, reflecting his impact on both scientific literature and clinical practice.

Legacy and Future Contributions

With an established record of academic excellence, groundbreaking research, and mentorship, Dr. Dunn’s legacy lies in advancing hepatology through both innovative research and translational impact. His ongoing leadership in pivotal trials will likely redefine therapeutic standards for NASH and related liver conditions. As a Professor at the University of Kansas Medical Center, he is poised to continue mentoring future hepatologists while contributing to international collaborations that influence the global fight against liver disease.

Conclusion

In summary, Dr. Winston Dunn exemplifies the qualities of a distinguished academic physician-scientist. His rigorous training, progressive academic career, and pioneering research in hepatology underscore his suitability for honors such as the Best Researcher Award. With a career dedicated to bridging clinical care, research innovation, and education, Dr. Dunn stands as a role model whose work continues to shape the future of liver disease management and patient outcomes worldwide.

Notable Publications

“Artificial Intelligence for Predictive Diagnostics, Prognosis, and Decision Support in MASLD, Hepatocellular Carcinoma, and Digital Pathology" 

  • Author: Nicholas Dunn; Nipun Verma; Winston Dunn
  • Journal: Journal of Clinical and Experimental Hepatology
  • Year: 2025

"Prevalence and Predictors of Suspected Metabolic Dysfunction‐Associated Steatotic Liver Disease in Adolescents in the United States" 

  • Author: Sheila L. Noon; Lauren F. Chun; Tin Bo Nicholas Lam; Nhat Quang N. Thai; Winston Dunn; Jeffrey B. Schwimmer‏
  • Journal: Alimentary Pharmacology & Therapeutics
  • Year: 2025

"Comparison Between Dynamic Models for Predicting Response to Corticosteroids in Alcohol‐Associated Hepatitis: A Global Cohort Study" 

  • Author: Francisco Idalsoaga; Luis Antonio Díaz; Leonardo Guizzetti; Winston Dunn; Heer Mehta; Jorge Arnold; Gustavo Ayares; Rokhsana Mortuza; Gurpreet Mahli; Alvi H. Islam et al.
  • Journal: Alimentary Pharmacology & Therapeutics
  • Year: 2025

"Moderate alcohol-associated hepatitis: A real-world multicenter study" 

  • Author: Francisco Idalsoaga; Luis Antonio Díaz; Winston Dunn; Heer Mehta; Karen Muñoz; Vicente Caldentey; Jorge Arnold; Gustavo Ayares; Rokhsana Mortuza; Shiv K. Sarin et al.
  • Journal: Hepatology Communications
  • Year: 2025

"An artificial intelligence-generated model predicts 90-day survival in alcohol-associated hepatitis: A global cohort study" 

  • Author: Winston Dunn; Yanming Li; Ashwani K. Singal; Douglas A. Simonetto; Luis A. Díaz; Francisco Idalsoaga; Gustavo Ayares; Jorge Arnold; María Ayala-Valverde; Diego Perez et al.
  • Journal: Hepatology
  • Year: 2024

Faisal Alshami | Machine Learning | Best Researcher Award

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Faisal Alshami | Machine Learning | Best Researcher Award

Dalian University of Technology | China

Author Profile

Google Scholar

Early Academic Pursuits

Faisal Alshami’s academic journey reflects a deep commitment to software engineering and technological innovation. He began his undergraduate studies at Sana’a University, Yemen, earning a BSc in Network Technology and Computer Security (2008–2012). His undergraduate thesis, “General Management System for Plant Protection,” showcased his early ability to integrate security and system management using ASP.NET, C#, and VPN with OSPF protocols, signaling his strong foundation in both networking and software development. Building on this groundwork, Faisal pursued a Master’s in Software Engineering at Northeastern University, China (2019–2022), where he specialized in advanced machine learning techniques. His master’s thesis, “Design and Implementation of Web API Recommendation System Based on Deep Learning,” utilized CNN, BLSTM, K-modes, and Word2Vec, demonstrating his growing expertise in AI-driven software solutions. Currently, Faisal is advancing his academic pursuits with a PhD in Software Engineering at Dalian University of Technology, China, focusing on federated learning, distributed systems, blockchain, edge computing, and graph neural networks (GNNs).

Professional Endeavors

Alongside his academic progression, Faisal has accumulated over 5 years of professional experience in the software and networking industry. His early career as a VoIP Engineer/Developer at Communication Services Company (2013–2015) allowed him to develop communication APIs and optimize large-scale systems. As a Network Manager and Systems Engineer at EliteTecs (2015–2016), he designed high-reliability networks using advanced protocols such as OSPF, EIGRP, and WiMAX, showcasing his expertise in secure and resilient infrastructures. His role as Full-Stack Developer and DevOps Lead at Almorisi Exchange Company (2016–2018) highlighted his ability to manage mission-critical systems with real-time performance and security. Here, Faisal excelled in building scalable architectures, simulation frameworks, and automated DevOps pipelines, which contributed to operational excellence.

Contributions and Research Focus

Faisal’s research is strategically positioned at the intersection of distributed systems, intelligent computing, and aerospace applications. His focus includes:

  • Federated learning and secure communication for multi-agent systems such as satellite constellations.

  • Edge computing and real-time distributed systems tailored for resource-constrained environments.

  • Robust machine learning frameworks for aerospace, automation, and high-reliability embedded systems.

  • Blockchain integration with AI to enhance security in data networks.

  • Simulation and testing methodologies to ensure fault tolerance in mission-critical software.
    This body of research reflects his ambition to address pressing challenges in space exploration, aerospace engineering, and advanced communication networks.

Impact and Influence

Faisal’s impact lies in bridging the gap between theory and applied innovation. His academic research is not confined to publications alone but extends into real-world applications in secure communications, high-availability systems, and intelligent software architectures. By combining his professional experience with cutting-edge research, Faisal has influenced the fields of network security, distributed computing, and AI-driven system optimization, making his contributions valuable to both academia and industry.

Academic Cites

His work has strong potential for academic citations due to its interdisciplinary nature—linking software engineering, AI, networking, and aerospace technologies. His focus on federated learning, blockchain, and edge computing positions his research at the forefront of emerging scholarly and industrial discussions, ensuring that his publications will attract citations in journals focusing on AI, distributed systems, cybersecurity, and aerospace software engineering.

Legacy and Future Contributions

Faisal Alshami is on a trajectory to build a lasting legacy in intelligent, secure, and scalable software engineering systems. His research is particularly impactful in aerospace applications and secure communications, areas that are becoming increasingly vital in a digital and space-driven era. As he progresses with his doctoral research, Faisal is expected to contribute significantly to the development of resilient federated learning frameworks, advanced distributed architectures, and mission-critical simulations. His blend of academic depth and industry experience ensures that his future work will leave a lasting influence on next-generation computing systems and aerospace engineering technologies.

Other Notable Highlights

  • Certifications: Faisal holds multiple certifications, including Neural Networks & Deep Learning (DeepLearning.AI), CCNP, CCNA, and advanced language certifications (Chinese HSK4, English YALI).

  • Training: He gained practical exposure at NEUSOFT Project Training, where he contributed to developing the Borrow-Seller System (BSS) using Java, Spring Boot, Vue.js, and Android Studio.

  • Core Competencies: His expertise spans software architecture, DevOps, distributed systems, full-stack development, secure networking, and agile collaboration.

Conclusion

In conclusion, Faisal Alshami is an emerging leader in the domain of software engineering, distributed systems, and intelligent computing. His academic journey, professional experiences, and research pursuits demonstrate a rare combination of technical mastery, innovation, and practical problem-solving skills. With his ongoing doctoral work and focus on future technologies such as federated learning, blockchain, and aerospace applications, Faisal is poised to make significant contributions that will influence both academia and industry for years to come.

Notable Publications

"A detailed analysis of benchmark datasets for network intrusion detection system" 

  • Author: M Ghurab, G Gaphari, F Alshami, R Alshamy, S Othman
  • Journal: Asian Journal of Research in Computer Science
  • Year: 2021

"Intrusion detection model for imbalanced dataset using SMOTE and random forest algorithm" 

  • Author: R Alshamy, M Ghurab, S Othman, F Alshami
  • Journal: International Conference on Advances in Cyber Security
  • Year: 2021

 

 

Thiru Nirai Senthil | Computer Science | Best Academic Researcher Award

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Dr. S. Thiru Nirai Senthil | Computer Science | Best Academic Researcher Award

Jawahar Science College | India

Author Profile

Google Scholar

Early Academic Pursuits

Dr. S. Thiru Nirai Senthil began his academic journey with a strong foundation in computer science and engineering, developing expertise that would later encompass diverse domains such as bioinformatics, artificial intelligence, network security, and data mining. His early exposure to both computational technologies and biological systems enabled him to adopt an interdisciplinary approach to research. Over the years, he built a rich portfolio of technical skills, contributing to areas from molecular modeling and protein analysis to large-scale network optimization and machine learning applications.

Professional Endeavors

With an extensive career in academia, Dr. Senthil has served in pivotal roles, including Head of the Department of Computer Science and Engineering at PRIST University, overseeing curriculum design, departmental administration, and faculty development. His professional experience extends to acting as Chief Superintendent and Additional Chief Superintendent for examination processes, member of various academic boards, and leader in event organization. He has consistently bridged the gap between theoretical research and practical application, guiding students, designing academic programs, and managing university-level technological systems such as ERP CAMU for admissions and data management.

Contributions and Research Focus

Dr. Senthil’s research spans multiple high-impact areas, notably artificial intelligence, machine learning, data mining, cloud computing, IoT, wireless sensor networks, bioinformatics, and cybersecurity. His contributions include the development of algorithms for optimized clustering, secure cloud storage auditing, intelligent e-learning systems, and AI-driven healthcare solutions. His interdisciplinary publications address both technological and societal challenges, such as AI-assisted medical devices, data-driven pandemic analysis, and sentiment analysis for social media monitoring. He has presented papers at prestigious national and international conferences, including ICICACS, ASCIS, ICRTSM, and the Asian Mycological Congress, demonstrating global engagement in research dissemination.

Impact and Influence

Dr. Senthil’s work has had a significant influence on both academic and applied technology communities. His AI-based patents in healthcare, autonomous navigation, and social media analytics showcase his commitment to impactful innovation. He has authored books on core computing subjects-Artificial Intelligence, Machine Learning, Data Mining and Warehousing, and Client-Server Computing-providing valuable academic resources for students and professionals alike. His leadership in faculty development programs, organization of technical workshops, and delivery of expert lectures has shaped the learning environment for countless students and educators.

Academic Citations and Recognition

His research publications have been widely cited, particularly in areas involving AI for agriculture, healthcare, and network optimization. Recognition of his scholarly contributions includes prestigious honors such as the Researcher Excellence Award (2025) and the Global Eminent Academician Award (2021), acknowledging both his research impact and his dedication to teaching excellence.

Legacy and Future Contributions

Dr. Senthil’s academic legacy lies in his ability to integrate multidisciplinary domains, creating solutions that address real-world problems while advancing theoretical frameworks. His role as a research guide and doctoral committee member ensures the training of future scholars, while his patents lay the groundwork for continued innovation. Moving forward, his work is poised to expand into emerging areas of generative AI, advanced machine learning models, and AI-driven biomedical devices, promising further contributions to science, technology, and society.

Conclusion

Dr. S. Thiru Nirai Senthil exemplifies the modern academician—innovative, interdisciplinary, and dedicated to the advancement of knowledge. His career reflects a rare combination of research excellence, pedagogical commitment, and visionary leadership. With an impressive record of publications, patents, and academic service, he continues to influence the trajectory of research in computer science and its allied fields, leaving a lasting mark on both academia and industry.

Notable Publications

"LCNFN: LeNet‐Cascade Neuro‐Fuzzy Network for Grape Leaf Disease Segmentation and Multi‐Classification" 

  • Author: G Selvaraj, SV Puthenkaleelkal, P Alaguchamy, STN Senthil
  • Journal: Journal of Phytopathology
  • Year: 2025

"COVID-19 Adaptive E-Learning: Data-Driven Student Engagement Analysis" 

  • Author: LL Rani, ST Senthil
  • Journal: International Conference on Integrated Circuits
  • Year: 2024

"Text Classification with Automatic Detection of COVID-19 Symptoms from Twitter Posts Using Natural Language Programming (NLP)" 

  • Author: N Manikandan, S Thirunirai Senthil
  • Journal: International Conference on Advancements in Smart Computing
  • Year: 2023

"Efficient College Students Higher Education Prediction Using Machine Learning Approaches" 

  • Author: L Lalli Rani, S Thirunirai Senthil
  • Journal: International Conference on Advancements in Smart Computing
  • Year: 2023

"Improved Genetic Algorithm Based k-means Cluster for Optimized Clustering" 

  • Author: FM Ilyas, ST Senthil
  • Journal: International Conference on Advancements in Smart Computing
  • Year: 2023

 

Mian Usman Sattar | Artificial Intelligence | Best Researcher Award

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Dr. Mian Usman Sattar | Artificial Intelligence | Best Researcher Award

University of Derby | United Kingdom

Author Profiles

Scopus

Orcid ID

Google Scholar

Early Academic Pursuits

Dr. Mian Usman Sattar’s academic journey reflects a sustained commitment to excellence in computing, informatics, and information systems. He began with a Postgraduate Diploma in Communication and Computer Technology from Government College University, Lahore (2002), followed by an M.Sc. in Computer Science (2004). His pursuit of international exposure led him to the United Kingdom, where he earned a Postgraduate Diploma in Computer Science (2008) and an MS in IT Management from the University of Sunderland (2010). His academic trajectory culminated in a Ph.D. in Informatics from the Malaysian University of Science and Technology (2022), under the guidance of Prof. Dr. Ang Ling Weay. Currently, he is further enhancing his expertise through a PG Certificate leading to FHEA from the University of Derby, UK (expected 2025).

Professional Endeavors

Dr. Sattar’s career spans academia, industry, and research leadership. His current role as Lecturer and Program Leader (Information Technology) at the University of Derby involves teaching diverse modules such as IT Product Design, Web Technologies, and Analytics Ethics. Prior to this, he served as Assistant Professor of Business Intelligence at Beaconhouse National University (2020–2023), where he introduced contemporary courses in analytics and emerging technologies. His earlier tenure as Assistant Professor of Information Systems at the University of Management and Technology (2014–2020) saw him direct academic programs, establish industry collaborations, and lead departmental initiatives. Beyond academia, he has contributed to industry as Deputy Manager (MIS) at AIAK International, UK, and as Unit Head for Training at Haseen Habib Corporation in Pakistan.

Contributions and Research Focus

Dr. Sattar’s research is anchored in Business Intelligence, Data Analytics, Enterprise Systems, and Information Security. He has secured multiple high-value research grants, including funding from the Pakistan Science Foundation, TWAS-COMSTECH, Malaysia Digital Economy Corporation, and the Malaysia Toray Science Foundation. His contributions extend beyond individual research, encompassing the creation of specialized academic tracks, development of curricula in disruptive technologies, and integration of industrial alliances such as with Microsoft Dynamics, Oracle, SAP, and Coursera.

Impact and Influence

Over two decades, Dr. Sattar has influenced academic landscapes in Pakistan, Malaysia, and the UK. He has mentored students on cutting-edge topics like Generative AI, Industry 4.0, and immersive technologies. As a conference chair, keynote speaker, and session leader, he has shaped dialogues on emerging business technologies. His role as a reviewer for numerous high-impact journals-including Sustainability, Frontiers in Medicine, and ACM Transactions-demonstrates his standing in the scholarly community.

Academic Citations and Recognitions

Dr. Sattar’s scholarly work is recognized through fellowships, travel grants, and the Higher Education Commission’s approval as a Ph.D. supervisor. His funded projects, often exceeding £30,000–£60,000 in value, have advanced applied research in artificial intelligence, data analytics, and enterprise systems. He is regularly invited to deliver talks at international conferences, reflecting the academic community’s acknowledgment of his expertise.

Legacy and Future Contributions

Dr. Sattar’s legacy lies in building academic bridges between industry and education, modernizing curricula, and fostering innovation-driven learning environments. His future trajectory points toward deepening his engagement with AI-driven business intelligence, strengthening global research collaborations, and influencing policy in higher education technology integration. By combining pedagogical innovation with robust research, he continues to prepare students for the demands of a data-driven global economy.

Conclusion

Dr. Mian Usman Sattar’s career exemplifies the synergy between scholarship, industry expertise, and educational leadership. From pioneering business intelligence programs to mentoring the next generation of data scientists, his work reflects both depth and breadth in the evolving field of information systems. His international academic footprint, sustained research output, and leadership roles position him as a transformative figure whose contributions will continue to shape the intersection of technology and business education.

Notable Publications

"Beyond Polarity: Forecasting Consumer Sentiment with Aspect- and Topic-Conditioned Time Series Models" 

  • Author: Mian Usman Sattar; Raza Hasan; Sellappan Palaniappan; Salman Mahmood; Hamza Wazir Khan
  • Journal: Information
  • Year: 2025

"From promotion to empathy: a content analysis of brand responses to social justice movements" 

  • Author: Dilshad, W.; Sattar, U.; Ghaffar, A.
  • Journal: Bulletin of Management Review
  • Year: 2025

"Enhancing Supply Chain Management: A Comparative Study of Machine Learning Techniques with Cost–Accuracy and ESG-Based Evaluation for Forecasting and Risk Mitigation" 

  • Author: Mian Usman Sattar; Vishal Dattana; Raza Hasan; Salman Mahmood; Hamza Wazir Khan; Saqib Hussain
  • Journal: Sustainability
  • Year: 2025

"Exploring the impact of augmented reality on medical students’ intrinsic motivation: a three-dimensional analysis" 

  • Author: Sattar, U.; Khan, H. W.; Ghaffar, A.; Raza, S.
  • Journal: Journal of Management & Social Science
  • Year: 2025

"Enhancing customer segmentation through factor analysis of mixed data (FAMD)-based approach using K-means and hierarchical clustering algorithms" 

  • Author: Sattar, U.; Ufeli, C. P.; Hasan, R.; Mahmood, S.
  • Journal: information
  • Year: 2025

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.

Rania Loukil | Deep Learning | Best Scholar Award

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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:

Scopus Profile

Orcid 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

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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

Scopus Profile

Orcid Profile

Google Scholar 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

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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

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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.

Qinglai Wei | Self-Learning Systems | Best Researcher Award

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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

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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

Google Scholar 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.