Jianjun Yuan | Network Resilience and Robustness | Research Excellence Award

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Prof. Jianjun Yuan | Network Resilience and Robustness | Research Excellence Award

Southwest University | China

Prof. Jianjun Yuan is an established researcher whose work spans intelligent systems, deep learning, and secure networked applications, with strong contributions to intrusion detection, image understanding, and robust optimization models. His research emphasizes lightweight and resilient learning architectures for complex real-world environments, including in-vehicle networks, medical imaging, and high-resolution remote sensing. He has authored 39 scholarly documents, receiving 215 citations across 205 citing publications, and holds an h-index of 9, reflecting sustained research impact and influence. Recent publications include RL-IDS, a robust reinforcement learning–based intrusion detection system for in-vehicle networks (Journal of Information Security and Applications, 2026), IMFF, a dual-space optimization framework for remote sensing scene classification (Expert Systems with Applications, 2026), and MLFD, a multi-level feature disentanglement network for medical image recognition (Expert Systems with Applications, 2025). His work advances robust, interpretable, and application-driven artificial intelligence research.

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Featured Publications

Xiali Li | Computer Science | Excellence in Academic Research

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Prof. Xiali Li | Computer Science | Excellence in Academic Research Award

Minzu University of China | China

Prof. Xiali Li’s research centers on artificial intelligence, reinforcement learning, deep neural networks, and intelligent game systems, with a strong emphasis on low-resource and culturally specific board games. The scholarly record comprises 72 documents, accumulating 500 citations across 457 citing documents, with an h-index of 11, reflecting sustained academic impact. The work spans advanced reinforcement learning frameworks, transformer-based architectures, residual and LSTM-integrated models, and lightweight computer vision networks. A significant contribution lies in AI-driven modeling of Mahjong and Tibetan Jiu chess, including hierarchical decision-making, human-knowledge–enhanced reinforcement learning, and efficient data collection using improved YOLO models. Publications in high-impact journals such as Engineering Applications of Artificial Intelligence, Frontiers of Information Technology & Electronic Engineering, Tsinghua Science and Technology, and CAAI Transactions on Intelligence Technology demonstrate both methodological innovation and practical relevance, advancing intelligent systems for complex decision-making and game AI research.

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Featured Publications

Mert Gülüm | Network Properties and Measures | Research Excellence Award

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Assoc. Prof. Dr. Mert Gülüm | Network Properties and Measures | Research Excellence Award

Karadeniz Technical University | Turkey

Assoc. Prof. Dr. Mert Gülüm is a leading researcher in alternative fuels, combustion, and thermophysical properties of fluids, with 32 publications, 933 citations from 645 documents, and an h-index of 18. His research emphasizes diesel engines, bio-derived and hybrid fuels, nanofluids, and advanced modeling to enhance fuel performance, combustion efficiency, and emission control. His key contributions include hydrogen utilization in diesel engines, viscosity–temperature modeling of alternative fuel blends, thermophysical analysis of nanofluids, and optimized biodiesel production from waste cooking oil. Published in high-impact SCIE and ESCI journals, his work integrates experimental, numerical, and computational approaches, significantly advancing sustainable energy, fluid dynamics, and environmentally efficient engine technologies.

 

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Featured Publications

Chao Liu | Graph Neural Networks | Research Excellence Award

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Mr. Chao Liu | Graph Neural Networks | Research Excellence Award

China University of Geosciences | China

Mr. Chao Liu is an active researcher in computer science with a strong focus on knowledge graphs, natural language processing, and big data analysis, particularly in the context of graph neural networks (GNNs). He has authored 33 scholarly documents that have attracted 492 citations across 488 citing publications, reflecting a high level of visibility and influence within the research community. With an h-index of 11, his work demonstrates consistent academic impact, especially in large-scale graph learning and data-driven intelligence. His recent contributions explore fundamental challenges in sampling-based large-scale GNNs, examining the relative roles of sampling strategies versus iterative computation for improving scalability and performance. Notably, his 2026 journal article in Neurocomputing, titled “Which plays a key role in sampling-based large-scale GNNs, sampling or iteration?”, provides important theoretical and empirical insights into efficient GNN design. Overall, his research advances scalable machine learning methods for complex, data-intensive systems.

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Featured Publications

Changda Lei | Artificial intelligence | Best Academic Researcher Award

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Dr. Changda Lei | Artificial intelligence | Best Academic Researcher Award

Resident physician at First Affiliated Hospital of Soochow University, China

Professional Profile

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Orcid

Summary

Dr. Changda Lei is a medical doctor and early-career researcher specializing in gastrointestinal tumors, digestive endoscopy, and artificial intelligence (AI) in clinical diagnostics. He is currently affiliated with the Department of Gastroenterology at the First Affiliated Hospital of Soochow University, China. His interdisciplinary expertise bridges medical imaging, clinical gastroenterology, and AI-driven diagnostic systems, with a particular focus on enhancing early cancer detection.

Educational Details

Dr. Lei earned his Doctor of Medicine degree with a residency in Gastroenterology from the First Affiliated Hospital of Soochow University, China. His doctoral thesis, titled “Artificial intelligence for early gastric cancer boundary recognition in NBI and NF-NBI endoscopic images,” was supervised by Prof. Rui Li and represents a significant contribution to AI applications in endoscopic image analysis for early cancer detection.

Professional Experience

During his residency and doctoral training at Soochow University, Dr. Lei gained clinical and research experience in gastroenterological procedures and endoscopic imaging. He has collaborated closely with multidisciplinary teams, including radiologists, computer scientists, and oncologists, to develop and validate AI-assisted diagnostic tools. His co-authored works involve both methodological development and clinical validation, marking him as a key contributor to translational medicine in the field of digestive oncology.

Research Interests

Dr. Lei’s research interests lie at the intersection of gastrointestinal tumor diagnostics, digestive endoscopy, and artificial intelligence. He focuses on improving early detection of gastric cancer, particularly through boundary recognition in NBI and NF-NBI endoscopic images. His broader research also explores multi-task learning, semantic segmentation, and clinical integration of AI tools for real-time diagnostic support in endoscopy suites.

Author Metrics

Although early in his academic career, Dr. Lei has co-authored multiple peer-reviewed articles in reputable international journals. His publications in Annals of Medicine, Scandinavian Journal of Gastroenterology, and Expert Systems with Applications demonstrate a growing influence in both clinical and AI research communities. His ORCID ID is 0000-0001-7908-7011, and his citation metrics are expected to rise with his growing publication footprint in multidisciplinary fields.

Awards and Honors

While formal awards are not explicitly listed, Dr. Lei’s contributions to high-impact publications and participation in cutting-edge research—such as the application of task-specific prompting in AI models for endoscopy—indicate peer recognition and significant academic promise. His collaborative work with senior scientists like Prof. Rui Li and publication in top-tier journals positions him as a rising expert in the medical AI and gastroenterology research community.

Publication Top Notes

1. Artificial Intelligence-Assisted Diagnosis of Early Gastric Cancer: Present Practice and Future Prospects

  • Authors: Changda Lei, Wenqiang Sun, Kun Wang, Ruirong Weng, Xiuji Kan, Rui Li

  • Journal: Annals of Medicine

  • Volume/Issue: Volume 57, Issue 1

  • Article ID: 2461679

  • Publication Date: 2025

  • DOI: 10.1080/07853890.2025.2461679

  • Summary:
    This article reviews current applications and future directions for artificial intelligence (AI) in the diagnosis of early gastric cancer (EGC). It highlights advances in endoscopic imaging, especially NBI (Narrow Band Imaging) and AI-based pattern recognition, and discusses clinical integration, challenges, and prospects for real-time implementation.

2. Neonatal Lupus Erythematosus: An Acquired Autoimmune Disease to Be Taken Seriously

  • Authors: Wenqiang Sun, Changchang Fu, Xinyun Jin, Changda Lei, Xueping Zhu

  • Journal: Annals of Medicine

  • Publication Date: December 31, 2025

  • DOI: 10.1080/07853890.2025.2476049

  • Summary:
    This clinical review focuses on neonatal lupus erythematosus (NLE), a rare but significant autoimmune condition affecting newborns. The article emphasizes early diagnosis, maternal screening, and therapeutic strategies, highlighting the need for interdisciplinary vigilance and patient-specific care.

Conclusion

Dr. Changda Lei is an exceptionally promising early-career academic who has already made meaningful contributions to the convergence of AI and gastrointestinal oncology. His research is not only innovative and clinically relevant but also indicative of leadership in next-generation diagnostic solutions.

Abdullah Abonamah | Machine Learning | Best Researcher Award

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Prof. Abdullah Abonamah | Machine Learning | Best Researcher Award

Research Affiliate at George Washington University, United States

Prof. Abdullah A. Abonamah is a distinguished academic and technology leader with over 40 years of expertise in artificial intelligence (AI), machine learning, and higher education. He currently serves as a Professor of Computing and AI at George Washington University and Chairman of AI Learning Solutions in the UAE. Dr. Abonamah has held key leadership roles, including President of the Abu Dhabi School of Management and CEO of the UAE Academy. He holds a Ph.D. in Computer Science from the Illinois Institute of Technology and has contributed extensively to AI research, focusing on AI integration in business processes, healthcare, smart cities, and cybersecurity. With over 10 patents in AI-driven systems and numerous scholarly publications, his work is widely cited in both academia and industry. Dr. Abonamah has secured over $1 million in research funding and has received several prestigious awards, including the Government of Abu Dhabi Recognition Award. His innovative projects have influenced AI and digital transformation strategies globally, and he has represented the UAE in international delegations. Prof. Abonamah’s leadership, combined with his groundbreaking research, positions him as a deserving candidate for the Best Researcher Award.

Professional Profile
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Google Scholar

Summary

Dr. Abdullah A. Abonamah is a highly accomplished academic, technology strategist, and business leader with over four decades of experience in computing, artificial intelligence, and higher education leadership. He currently serves as Professor of Computing and AI at George Washington University’s Environmental and Energy Management Institute and is Chairman of the Board of AI Learning Solutions in the UAE. Dr. Abonamah has held numerous executive, academic, and advisory roles, including President and Provost of the Abu Dhabi School of Management, and CEO of the UAE Academy. His work bridges academia, innovation, and industry with a focus on AI adoption, data strategy, and digital transformation.

Educational Background

Dr. Abonamah holds a Ph.D. in Computer Science from the Illinois Institute of Technology, USA, and an M.S. in Computer Science and Engineering from Wright State University. He earned his B.S. in Computer Science from the University of Dayton and later obtained an Executive Management Certificate from Yale School of Management. His multidisciplinary academic foundation has empowered his leadership in both technical research and institutional development.

Professional Experience

Dr. Abonamah has served in numerous high-impact roles, including:

  • Professor of Computing at Abu Dhabi School of Management (2007–2024)

  • Professor of IT at Zayed University (2000–2007)

  • Director of the Institute for Technological Innovation (Dubai Internet City)

  • Chair, AI Management Institute at ADSM

  • Business leader and strategist in several startups and research institutes
    In his leadership positions, he led major organizational transformations, managed multimillion-dollar budgets, implemented ERP and AI systems, developed academic programs, and fostered public-private partnerships. He also served as Dean, Program Director, and Assistant Dean in various institutions, ensuring accreditation and global standards compliance.

Research Interests

Dr. Abonamah’s research spans artificial intelligence, machine learning, cybersecurity, fault-tolerant computing, and innovation ecosystems. His recent work focuses on the integration of AI into business processes, human-centered machine learning, and strategic data governance. He is also involved in applied AI projects in healthcare, smart cities, and education technology.

Author Metrics

Dr. Abonamah has authored and co-authored dozens of journal articles, book chapters, and conference papers, many of which are indexed in Scopus, IEEE, and Web of Science. He holds multiple patents and intellectual property certificates for AI-driven systems, ERP modules, academic tools, and mobile apps. His scholarship includes both foundational theory and practical implementations, making his work highly cited in both academic and industry domains.

Awards and Honors

Dr. Abdullah A. Abonamah has been the recipient of numerous prestigious awards and recognitions throughout his distinguished career. He was honored with the Government of Abu Dhabi Recognition Award in 2017 for his outstanding contributions to higher education and institutional development. Over the years, he has secured multiple competitive research grants totaling more than $1 million, including major funding for the development of AI and cybersecurity programs by the Federal Authority for Identity, Citizenship, Customs & Port Security and the Emirates Academy for Identity and Citizenship. His innovative projects have led to the creation of several intellectual property-certified digital systems, earning formal recognition from the UAE Ministry of Economy, with over 10 patented and certified software applications in AI, ERP systems, and academic tools. Dr. Abonamah was also awarded the US State Department MEPI Grant for the Emirati Women’s Organizational Leadership Program and received the Microsoft Instructional Lab Grant and other major institutional grants for research labs and technology initiatives. Recognized for his leadership, he has represented the UAE on international delegations, including a technological mission to Japan, and has consistently been acknowledged for his impactful work in promoting innovation, entrepreneurship, and digital transformation in education and governance.

Publication Top Notes

1. A Collaborative Adaptive Cybersecurity Algorithm for Cognitive Cities

  • Authors: A. Abonamah, F.N. Sibai

  • Published in: Journal of Computer Information Systems, 2025, pp. 1–16

  • Summary:
    This paper introduces a novel adaptive cybersecurity algorithm specifically designed for cognitive cities, which rely heavily on interconnected AI systems and IoT infrastructure. The algorithm leverages collaborative machine learning, enabling various smart subsystems to share threat intelligence and dynamically adjust defenses in real time. The model improves resilience, threat detection speed, and situational awareness, offering a scalable security solution for complex urban networks.

2. Managerial Insights for AI/ML Implementation: A Playbook for Successful Organizational Integration

  • Authors: A.A. Abonamah, N. Abdelhamid

  • Published in: Discover Artificial Intelligence, 2024, Vol. 4(1), Article 22

  • Summary:
    This publication acts as a strategic guide for executives and IT leaders aiming to deploy AI and machine learning within organizations. It outlines a structured playbook, highlighting critical success factors, common pitfalls, change management practices, and technology readiness considerations. The work is grounded in case studies and offers a framework for bridging technical solutions with organizational goals.

3. Discover Artificial Intelligence

  • Authors: A.A. Abonamah, N. Abdelhamid

  • Published in: Discover, 2024, Vol. 4, Article 22

  • Summary:
    This appears to be a companion piece or an editorialized version of the article above, with expanded commentary on AI governance, leadership roles, and ethical implementation frameworks. It emphasizes building institutional capability and fostering innovation culture for sustainable AI integration.

4. Wearable Sensor-Based Device for Predicting, Monitoring, and Controlling Epilepsy and Methods Thereof

  • Inventors: M.U. Tariq, A.A. Abonamah

  • Filing Number: US Patent App. 18/107,839

  • Filed in: 2023

  • Summary:
    This patent proposes a wearable biomedical device equipped with sensor arrays and AI algorithms for the real-time detection, prediction, and intervention of epileptic seizures. The system analyzes physiological data—such as ECG, EEG, and temperature signals—and uses machine learning to anticipate seizure events, offering alerts or therapeutic responses. It aims to enhance autonomous patient care and reduce medical emergencies, particularly in outpatient or home settings.

5. Artificial Intelligence Technologies and Platforms

  • Authors: M.U. Tariq, A. Abonamah, M. Poulin

  • Published in: Engineering Mathematics and Artificial Intelligence, 2023, pp. 211–226

  • Summary:
    This book chapter provides an in-depth analysis of leading AI platforms and ecosystems, such as TensorFlow, PyTorch, and Azure AI. It covers architecture, deployment strategies, and use cases across domains like healthcare, finance, and smart cities. The chapter emphasizes the selection criteria for AI tools, and how platform choices affect scalability, maintainability, and compliance in enterprise contexts.

Conclusion

Prof. Abdullah A. Abonamah is an outstanding and highly deserving candidate for the Best Researcher Award. His blend of academic scholarship, applied innovation, institutional leadership, and global impact positions him uniquely at the intersection of technology and societal advancement. His research addresses real-world challenges with AI-driven solutions, while his leadership roles have built enduring institutions and empowered future generations.

Given his contributions to AI research, higher education reform, cross-sectoral innovation, and IP development, Prof. Abonamah clearly meets and exceeds the criteria for this award. He is not only a prolific scholar but also a visionary leader and mentor, making him an ideal recipient of the Best Researcher Award.

Ahmad Hassanat | Machine Learning | Best Researcher Award

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Prof. Ahmad Hassanat | Machine Learning | Best Researcher Award

Professor at Mutah University, Jordan

Professional Profile

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Summary

Prof. Ahmad B. A. Hassanat is a Full Professor of Computer Science at Mutah University, Jordan, and a senior IEEE member. He is globally recognized for his extensive contributions to artificial intelligence, machine learning, biometrics, and image processing. With over two decades of academic and research experience, he has authored numerous impactful papers and books and is widely known for pioneering innovative techniques like the "Hassanat Distance" metric and deep learning-based biometric systems. He is also active in international collaborations, editorial work, and AI-driven healthcare research.

Educational Details

Prof. Hassanat earned his Ph.D. in Computer Science from the University of Buckingham, UK,, with a focus on automatic lip-reading. He holds an M.Sc. in Computer Science from Al al-Bayt University, Jordan, where he specialized in fast string matching algorithms. He completed his B.Sc. in Computer Science at Mutah University, Jordan. His academic foundation reflects a strong blend of theoretical depth and applied research skills in computing and AI.

Professional Experience

Prof. Hassanat has served in multiple academic roles across Jordan and Saudi Arabia, including as a Full Professor at Mutah University and the University of Tabuk. He was Head of the IT Department at Mutah University and a visiting researcher at the Sarajevo School of Science and Technology. Earlier in his career, he worked for the Jordanian Armed Forces as a programmer and systems analyst, where he developed over a dozen mission-critical ICT systems. He is also a founder or co-founder of academic programs, conferences, and novel biometric solutions.

Research Interests

His research spans machine learning, artificial intelligence, image processing, biometrics, pattern recognition, and evolutionary algorithms. He is known for practical innovations such as deep learning for veiled-face recognition, genetic algorithm optimization, voice-based Parkinson’s detection, and machine learning models for epidemiology, security, and finance. He also created the widely referenced Hassanat Distance, improving classifier performance in imbalanced data scenarios.

Author Metrics

Prof. Hassanat has published over 100 journal articles and conference papers, with an H-index of 33, i10-index of 56, and more than 4,000 citations. His work is featured in top journals such as IEEE Access, PLOS ONE, Sustainability, Applied Sciences, and Computers. His algorithmic contributions and models are highly cited in the fields of AI, healthcare informatics, and big data analytics.

Awards and Honors

Prof. Hassanat has been named among the world’s top 2% scientists by Stanford–Elsevier in 2021, 2022, and 2023. He has received the Best Scientist award at Mutah University for 2023 and 2024, and multiple competitive research grants from Jordan and Saudi Arabia. He was the recipient of Mutah University’s Distinguished Researcher Award (2018, 2019), and granted IEEE Senior Membership for his research excellence. His innovations, including terrorist identification from hand gestures and COVID-19 forecasting tools, have received global media attention.

Publication Top Notes

1. Deep learning computer vision system for estimating sheep age using teeth images

  • Authors: AB Hassanat, MA Al-Sarayreh, AS Tarawneh, MA Abbadi, et al.

  • Journal: Connection Science

  • Volume/Issue: 37 (1)

  • Article ID: 2506456

  • Year: 2025

  • Summary:
    This study presents a deep learning-based computer vision system designed to estimate the age of sheep by analyzing images of their teeth. The model likely leverages convolutional neural networks (CNNs) or similar architectures to accurately assess age-related dental features, offering a non-invasive and automated method for livestock age estimation that can assist farmers and veterinarians.

  • Citations: Not provided

  • Access: Details not provided

2. ICT: Iterative Clustering with Training: Preliminary Results

  • Authors: AB Hassanat, AS Tarawneh, AS Alhasanat, M Alghamdi, K Almohammadi, et al.

  • Conference: 2025 International Conference on New Trends in Computing Sciences (ICTCS)

  • Year: 2025

  • Summary:
    This paper introduces a novel method named Iterative Clustering with Training (ICT), presumably a machine learning or data clustering approach. Preliminary results demonstrate its effectiveness in improving clustering accuracy or training efficiency for datasets common in computing science. The approach likely combines clustering with supervised training iterations for better performance.

3. Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing

  • Authors: E Al-Khlifeh, AS Tarawneh, K Almohammadi, M Alrashidi, R Hassanat, et al.

  • Journal: Parasites & Vectors

  • Volume/Issue: 18 (1)

  • Article Number: 33

  • Year: 2025

  • Summary:
    This research applies decision tree-based machine learning techniques to mine laboratory data for efficient and accurate diagnosis of amebiasis. The study demonstrates how data mining on clinical data combined with decision trees can improve testing accuracy and streamline diagnostic procedures in parasitology.

4. Non-Invasive Cancer Detection Using Blood Test and Predictive Modeling Approach

  • Authors: AS Tarawneh, AK Al Omari, EM Al-Khlifeh, FS Tarawneh, M Alghamdi, et al.

  • Book/Series: Advances and Applications in Bioinformatics and Chemistry

  • Pages: 159-178

  • Year: 2024

  • Summary:
    This paper proposes a non-invasive method for cancer detection by combining blood test results with predictive modeling approaches, likely using machine learning algorithms. The approach aims to provide an early, cost-effective screening tool for cancer by analyzing biomarkers and patterns in blood test data.

5. Extended spectrum beta-lactamase bacteria and multidrug resistance in Jordan are predicted using a new machine-learning system

  • Authors: EM Al-Khlifeh, IS Alkhazi, MA Alrowaily, M Alghamdi, M Alrashidi, et al.

  • Journal: Infection and Drug Resistance

  • Pages: 3225-3240

  • Year: 2024

  • Summary:
    This study develops and applies a machine learning system to predict the occurrence of extended spectrum beta-lactamase (ESBL) producing bacteria and multidrug resistance patterns in Jordan. The predictive model aids in understanding and managing antibiotic resistance, supporting healthcare decision-making and antimicrobial stewardship.

Conclusion

Prof. Ahmad Hassanat embodies the qualities of a world-class researcher—his work is innovative, deeply applied, and globally relevant. From introducing original metrics and models in AI to developing life-saving diagnostic systems and biometric security applications, his impact is both academic and practical.

His dedication to research excellence, mentorship, and cross-disciplinary innovation makes him highly deserving of the Best Researcher Award in Machine Learning.

Shakila Rahman | Machine Learning | Best Researcher Award

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Ms. Shakila Rahman | Machine Learning | Best Researcher Award

Lecturer at American International University, Bangladesh

Author Profile

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Summary

Shakila Rahman is a dedicated academician currently serving as a Lecturer in the Department of Computer Science at the Faculty of Science and Technology, American International University-Bangladesh (AIUB). She holds a strong academic background in Artificial Intelligence and Computer Engineering, with her research focusing on emerging areas such as UAV networking, wireless sensor networks, optimization algorithms, and machine learning. Shakila is actively involved in mentoring students, guiding projects, and publishing impactful research in reputed platforms.

Educational Details

Shakila Rahman earned her M.Sc. in AI & Computer Engineering from the University of Ulsan, South Korea, in 2023 with an impressive CGPA of 4.00 out of 4.50. She completed her B.Sc. in Computer Science and Engineering from International Islamic University Chittagong (IIUC), Bangladesh, in 2019, securing a CGPA of 3.743 out of 4.00. Prior to her university education, she completed her Higher Secondary Certificate (HSC) from Cox’s Bazar Govt. College and Secondary School Certificate (SSC) from Cox’s Bazar Govt. Girls’ High School.

Professional Experience

Shakila is currently employed as a Lecturer in the Department of Computer Science and Engineering at AIUB, Dhaka, Bangladesh, where she has been working since January 2023. She previously served as a Graduate Research Assistant at the University of Ulsan, South Korea, from September 2020 to December 2022 under Professor Seokhoon Yoon. Additionally, she worked as an Undergraduate Teaching Assistant at IIUC in 2019. She has participated in technical boot camps and workshops and actively contributes to academic supervision, having guided several student projects and a machine learning-based thesis group.

Research Interests

Her research interests span a wide range of cutting-edge topics including UAV Networking, Wireless Sensor Networks, Network Systems, Optimization Algorithms, Machine Learning, Deep Learning, Image Processing, and AR/VR Applications in Artificial Intelligence. These multidisciplinary areas reflect her focus on building intelligent and adaptive systems for real-world applications.

Author Metrics

Shakila Rahman actively maintains a presence on prominent academic platforms. Her ResearchGate profile can be found at https://www.researchgate.net/profile/Shakila-Rahman-3, and her ORCID ID is 0000-0001-6375-4174. She is also available on LinkedIn at Shakila Rahman. Her published works and citation records are regularly updated on these platforms.

Awards and Honors

During her master's studies, Shakila was awarded the prestigious Brain Korea 21 (BK21) Scholarship and a fully funded AF1 scholarship at the University of Ulsan, valued at approximately USD 21,000. She also received funding from Korean Government-supported National Research Foundation (NRF) projects to support her graduate research publications. These accolades recognize her academic excellence and research contributions in the field of computer science and engineering.

Publication Top Noted

1. Bilingual Sign Language Recognition: A YOLOv11-Based Model for Bangla and English Alphabets

Authors: N. Navin, F.A. Farid, R.Z. Rakin, S.S. Tanzim, M. Rahman, S. Rahman, J. Uddin, ...
Journal: Journal of Imaging, Vol. 11, Issue 5, Article 134
Year: 2025
Citation: 1 (as of now)
Summary:
This study introduces a YOLOv11-based deep learning model designed to recognize both Bangla and English sign language alphabets in real-time. The model was trained on a custom bilingual sign dataset and achieved high accuracy and low latency. The contribution is notable in promoting inclusivity for hearing-impaired communities in multilingual regions like Bangladesh.

2. Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11

Authors: R.Z. Rakin, M. Rahman, K.F. Borsa, F.A. Farid, S. Rahman, J. Uddin, H.A. Karim
Journal: Future Internet, Vol. 17, Issue 5, Article 187
Year: 2025
Summary:
This paper proposes an AI model using YOLOv11 to identify infrastructure faults (e.g., road cracks, bridge damage) through image data. Designed with smart city integration in mind, the model is tested in urban environments and demonstrates high efficiency.

3. A Hybrid CNN Framework DLI-Net for Acne Detection with XAI

Authors: S. Sharmin, F.A. Farid, M. Jihad, S. Rahman, J. Uddin, R.K. Rafi, R. Hossan, ...
Journal: Journal of Imaging, Vol. 11, Issue 4, Article 115
Year: 2025
Summary:
This paper presents DLI-Net, a hybrid CNN framework for classifying and explaining acne severity. It incorporates Explainable AI (XAI) techniques to enhance trust and transparency in medical AI systems.

4. A Deep Q-Learning Based UAV Detouring Algorithm in a Constrained Wireless Sensor Network Environment

Authors: S. Rahman, S. Akter, S. Yoon
Journal: Electronics, Vol. 14, Issue 1, Article 1
Year: 2024
Citation: 2 (as of now)
Summary:
This study explores a reinforcement learning-based approach using Deep Q-Learning for UAV navigation in constrained wireless sensor networks. The algorithm optimizes path planning in real-time, even in environments with signal interference or node failures.

5. A Deep Learning Model for YOLOv9-based Human Abnormal Activity Detection: Violence and Non-Violence Classification

Authors: S. Salehin, S. Rahman, M. Nur, A. Asif, M. Bin Harun, J. Uddin
Journal: Iranian Journal of Electrical & Electronic Engineering, Vol. 20, Issue 4
Year: 2024
Citation: 2 (as of now)
Summary:
This paper proposes a YOLOv9-based model to detect abnormal human activity, particularly violent behavior, in real-time video surveillance. The system is trained on public datasets and achieves high detection accuracy.

Conclusion

Ms. Shakila Rahman is a promising and emerging researcher, with an impressive blend of academic excellence, funded research, and contributions to cutting-edge domains like machine learning and UAV networks. Her commitment to mentoring students and publishing research makes her a very strong candidate for the Best Researcher Award, particularly among early-career researchers or those in developing countries.

Xin Liu | Deep Learning | Best Researcher Award

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Dr. Xin Liu | Deep Learning | Best Researcher Award

Associate Professor at Wenzhou Business College, China📖

Dr. Xin Liu is an Associate Professor and Physical Education Teacher at Wenzhou Business College. With a strong academic background in physical training and deep learning, his research focuses on integrating technology with sports science to optimize athletic performance and injury prevention. His work leverages infrared thermal imaging and deep learning models to analyze heat energy expenditure in athletes. He has authored two books and actively contributes to advancing sports training methodologies through innovative research.

Profile

Orcid Profile

Education Background🎓

  • Ph.D. in Physical Education, Jose Rizal University, 2020–2023
  • Master’s in Physical Education, Shanghai Normal University, 2017–2019
  • Bachelor’s in Physical Education, Shandong Agricultural University, 2013–2017

Professional Experience🌱

  • Physical Education Teacher, Wenzhou Business College (2024–Present)
    Engaged in teaching and research on physical training methodologies, integrating AI-driven analytics in sports science.
  • Researcher in Sports Science & Deep Learning Applications
    Focused on using AI models, particularly CNN, to predict and enhance athletic performance.
Research Interests🔬
  • Physical Training & Sports Performance Optimization
  • Application of Deep Learning in Sports Science
  • Infrared Thermal Imaging for Athlete Monitoring

Author Metrics

Dr. Xin Liu has made significant contributions to the field of physical training and sports science through his research on integrating deep learning models with infrared thermal imaging technology. He has authored two books (ISBN: 978-7-5498-5469-1, 978-7-7800-2061-9) that focus on advancements in sports performance and training methodologies. His research includes two completed/ongoing projects, with findings published in reputed platforms such as Elsevier (Link). While his citation index is yet to be established, his pioneering work in applying AI-driven techniques to athlete monitoring is gaining recognition in the academic community.

Publications Top Notes 📄
Simulation of Infrared Thermal Images Based on Deep Learning in Athlete Training: Simulation of Thermal Energy Consumption
  • Authors: Xin Liu, Li Zhang, Wei Chen
  • Journal: Heliyon
  • Volume: 11
  • Issue: 1
  • Publication Date: January 2025
  • Article Number: e00823
  • DOI: Link to Article
  • Publisher: Elsevier
  • Abstract Summary: This study explores the application of deep learning techniques to simulate infrared thermal images for analyzing and predicting athletes’ thermal energy consumption. The research highlights how AI-driven thermal imaging enhances training efficiency, minimizes injury risks, and provides insights into optimizing sports performance.

Conclusion

Dr. Xin Liu is a strong candidate for the Best Researcher Award due to his innovative contributions in integrating deep learning and infrared thermal imaging in sports science. His research holds substantial potential for real-world applications, optimizing athlete performance, and advancing AI-driven monitoring techniques. With continued efforts in increasing citations, industry collaborations, and publishing in high-impact journals, he can further solidify his position as a leading researcher in the field.