Yulin Jing | Computer Science | Research Excellence Award

Dr. Yulin Jing | Computer Science | Research Excellence Award

University of Electronic Science and Technology of China | China

Dr. Yulin Jing is a researcher affiliated with the University of Electronic Science and Technology of China, specializing in computer science with a focus on artificial intelligence, adversarial machine learning, and video recognition systems. With 5 publications, 24 citations, and an h-index of 3, Jing has contributed to advancing robust and secure AI models, particularly in the area of black-box adversarial attack algorithms. Engaged in collaborative research with multiple co-authors, their work addresses critical challenges in AI reliability and cybersecurity. Jing’s research holds societal significance by enhancing the safety and trustworthiness of intelligent systems in real-world applications, including surveillance, automation, and digital technologies.

Citation Metrics (Scopus)

24
18
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24

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5

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3

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Featured Publication

Bin Liu | Computer Science | Research Excellence Award

Prof. Bin Liu | Computer Science | Research Excellence Award

Professor | Northwest A&F University | China

Prof. Bin Liu is a researcher at Northwest A&F University, Yangling, China, with expertise in artificial intelligence, computer vision, agricultural informatics, and large-scale model training. He has published 69 Scopus-indexed documents, receiving approximately 2,949 citations and achieving an h-index of 18, reflecting sustained academic impact. His recent work focuses on multi-source data fusion, multimodal learning, remote sensing change detection, and efficient parallel training pipelines for large models, with publications in reputable venues such as IEEE Transactions on Computers, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, and Applied Sciences. Liu has collaborated with over 140 co-authors, demonstrating strong interdisciplinary and international research engagement. His research contributes to societal needs by advancing intelligent agricultural disease diagnosis, improving crop monitoring, and enhancing the efficiency of large-scale AI systems, supporting sustainable agriculture and data-driven environmental management.

Citation Metrics (Scopus)

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2,949

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69

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18

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Featured Publications


MDS-Net: An image-text enhanced multimodal dual-branch Siamese network for remote sensing change detection


– IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025

PRT: An efficient pipeline reuse technology for large models training


– IEEE International Conference on Cluster Computing (CLUSTER), 2025

VMF-SSD: A novel V-space based multi-scale feature fusion SSD for apple leaf disease detection


– IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023

Aleeza Adeel | Computer Science | Research Excellence Award

Mrs. Aleeza Adeel | Computer Science | Research Excellence Award

The University of Waikato | New Zealand

Mrs. Aleeza Adeel is a Ph.D. student at the School of Computing and Mathematical Sciences, University of Waikato, New Zealand, specializing in digital twin frameworks, sustainable energy systems, and user-centered computing solutions. Her research focuses on developing interoperable and scalable digital twin technologies to optimize energy system management, enhance operational efficiency, and support sustainable resource utilization. She has contributed to peer-reviewed publications, including a recent article in Energies on an interoperable user-centered digital twin framework, demonstrating her commitment to integrating advanced computational models with real-world energy systems. Aleeza collaborates with interdisciplinary researchers, including experts in energy management and computational modeling, to ensure her work addresses both technical rigor and societal relevance. Her research contributes to sustainable energy transitions by providing data-driven, user-centric solutions that improve system performance, reduce environmental impact, and support informed decision-making in complex energy infrastructures.

Profile: View ORCID Profile 

Featured Publication


An Interoperable User‑Centred Digital Twin Framework for Sustainable Energy System Management

– Adeel, A., Apperley, M., & Walmsley, T. G., Energies, 2026, 19(2), Article 333

Miroslaw Kozielski | Computer Science | Best Researcher Award

Mr. Miroslaw Kozielski | Computer Science | Best Researcher Award

Kazimierz Wielki University | Poland

Mr. Mirosław Kozielski is a researcher at Kazimierz Wielki University in Bydgoszcz, Poland, specializing in computer science, with a strong focus on natural language processing (NLP), industrial informatics, and Industry 4.0/5.0 technologies. His research addresses the use of intelligent language-based systems for automated industrial documentation, knowledge representation, and digital transformation in modern manufacturing environments. He has authored 7 peer-reviewed publications, which have accumulated 35 citations, and holds an h-index of 3, reflecting a focused and emerging academic impact. Dr. Kozielski collaborates with interdisciplinary teams, contributing to the integration of artificial intelligence with industrial and organizational processes. His work supports the development of efficient, human-centric, and sustainable industrial systems, with societal impact through improved documentation quality, enhanced knowledge accessibility, and the practical adoption of advanced AI-driven solutions in contemporary industrial ecosystems.

Citation Metrics (Scopus)

35
25
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35

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7

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3

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Featured Publications

Zeba Shamsi | Computer Science | Research Excellence Award

Assoc. Prof. Dr. Zeba Shamsi | Computer Science | Research Excellence Award

Associate Professor | Lendi Institute of Engineering and Technology | India

Dr. Zeba Shamsi is a researcher at the National Institute of Technology Silchar, India, with expertise in computer science and engineering, particularly in cybersecurity, machine learning, and intelligent data-driven systems. Her research focuses on advanced threat detection, deep learning architectures, and generative models for secure and resilient computing. She has authored 7 peer-reviewed research publications, receiving 104 citations, with an h-index of 5, reflecting steady academic impact. Her recent work on zero-day attack detection using dynamic-weighted contractive autoencoders and GAN-based evaluation highlights her contribution to next-generation cyber defense mechanisms. Dr. Shamsi actively collaborates with national and international researchers, fostering interdisciplinary research and knowledge exchange. Her work contributes to improving digital security, protecting critical infrastructure, and supporting safer adoption of emerging technologies, demonstrating meaningful societal and technological impact at both academic and applied levels.

Citation Metrics (Scopus)

104
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104

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7

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5

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Featured Publications


An Encryption Scheme for Securing Multiple Medical Images


– Journal of Information Security and Applications, 2019

Visually Meaningful Cipher Data Concealment


– Digital Signal Processing, 2024

Visually Meaningful Cipher Data Concealment


– Digital Signal Processing, 2024

Securing Encrypted Image Information in Audio Data


– Multimedia Tools and Applications, 2023

Christian Schachtner | Computer Science | Research Excellence Award

Prof. Dr. Christian Schachtner | Computer Science | Research Excellence Award

Professor of Business Informatics | RheinMain University of Applied Sciences | Germany

Dr. Christian Schachtner is a researcher at Fachhochschule Wiesbaden, Germany, specializing in safety culture, social impact assessment, and sustainable development in technical and organizational systems. His work bridges corporate social responsibility, environmental management, and smart district development, emphasizing practical solutions to complex societal challenges. According to Scopus, he has authored 23 scholarly publications, received 9 citations, and holds an h-index of 2. His recent research includes open-access work on the determinants of social impact through safety culture in technical organizations and scholarly contributions to book chapters on smart regional and district development initiatives. Dr. Schachtner actively collaborates with international researchers, supporting interdisciplinary perspectives and knowledge exchange. His research contributes to improving organizational governance, enhancing safety performance, and promoting socially responsible and sustainable practices across technical and socio-economic domains.

Citation Metrics (Scopus)

23
15
10
5
0

Citations

9

Documents

23

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2

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Featured Publications

Zhaozhen Jiang | Computer Science | Best Research Article Award

Dr. Zhaozhen Jiang | Computer Science | Best Research Article Award

Assistant Researcher | Naval Submarine Academy | China

Dr. Zhaozhen Jiang is a distinguished researcher at the Navy Submarine Academy in Qingdao, China, specializing in intelligent systems, maritime navigation, and dynamic target search. His research focuses on the development of advanced path-planning algorithms and neural network–based optimization techniques for complex maritime environments. He has published extensively and collaborated widely with researchers across multiple disciplines, reflecting a strong commitment to interdisciplinary innovation. His recent work on GBNN-based maritime dynamic target search demonstrates a focus on enhancing operational decision-making and situational awareness in challenging naval contexts. Through his research, he aims to advance autonomous maritime systems and contribute to safer, more efficient naval operations, while fostering technological progress with meaningful societal impact.

Citation Metrics (Scopus)

40
30
20
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37

Documents

15

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4

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Featured Publications

Sarbajit Paul Bappy | Computer Science | Research Excellence Award

Mr. Sarbajit Paul Bappy | Computer Science | Research Excellence Award

Teaching Assistant | Daffodil International University | Bangladesh

Sarbajit Paul Bappy is an emerging researcher in computer science with a growing focus on applied machine learning, medical image analysis, and agricultural informatics. He is currently serving as a Teaching Assistant in the Department of Computer Science and Engineering at Daffodil International University, Bangladesh, where he has been contributing to academic instruction and research support since 2025. Alongside his professional role, he is pursuing his undergraduate degree in Computer Science and Engineering at the same institution, demonstrating a strong integration of academic excellence and early-career research productivity. His scholarly work includes peer-reviewed publications and openly accessible datasets that address critical challenges in healthcare diagnostics and smart agriculture. Notably, he co-authored SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images (MAKE, 2025), which combines deep learning with explainable AI techniques to enhance early disease detection. He also contributed significantly to the dataset Jackfruit AgroVision, a comprehensive benchmark for disease detection in jackfruit and its leaves, supporting advancements in precision agriculture and food-security research. His collaborations span multidisciplinary teams involving experts such as Amir Sohel, Rittik Chandra Das Turjy, Md Assaduzzaman, Ahmed Al Marouf, Jon George Rokne, and Reda Alhajj, illustrating his ability to contribute within diverse international research groups. Through his ongoing work in AI-driven health diagnostics, dataset development, and sustainable agricultural technology, Bappy aims to advance research that supports societal well-being, improves disease detection accuracy, and contributes to innovation within global machine learning communities.

Profiles: Google Scholar | ORCID | LinkedIn

Featured Publications

1. Sohel, A., Turjy, R. C. D., Bappy, S. P., Assaduzzaman, M., Marouf, A. A., Rokne, J. G., & Alhajj, R. (2025). SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images. Machine Learning and Knowledge Extraction, 7(4), 157. https://doi.org/10.3390/make7040157  MDPI+1

2. Sohel, A., Bijoy, M. H. I., Turjy, R. C. D., & Bappy, S. P. (2025). Jackfruit AgroVision: A Extensive Dataset for Jackfruit Disease and Leaf Disease Detection using Machine Learning. Mendeley Data. https://doi.org/10.17632/pt647jfn52.1

Mohammed Alenazi | Computer Engineering | Best Researcher Award

Mr. Mohammed Alenazi | Computer Engineering | Best Researcher Award

Assistant Professor | University of Tabuk | Saudi Arabia

Mr. Mohammed M. Alenazi is an accomplished academic and researcher with expertise in electrical and electronics engineering, computer engineering, and artificial intelligence applications in energy-efficient networks. He earned his Ph.D. in Electrical and Electronics Engineering from the University of Leeds, UK (2018–2022), focusing on energy efficiency in AI-powered communication systems. Prior to this, he completed his M.Eng. in Computer Engineering at Florida Institute of Technology, USA (2016–2017), and a B.Eng. in Computer Engineering from University Sultan Bin Fahad (2007–2011), along with an Associate’s degree in Electrical/Electronics Equipment Installation and Repair from Tabuk College of Technology (2002–2004). Professionally, Mr. Alenazi began his career as a Senior Engineer at Saudi Telecom Company (2006–2011), where he gained practical experience in optical fiber networks, before transitioning to academia as a Teaching Assistant at Northern Border University (2012–2013) and later at the University of Tabuk, where he continues to serve since 2013, eventually advancing into an assistant professorship. His research interests include machine learning, IoT networks, energy optimization, and intelligent systems, with key contributions in developing models for energy-efficient ML-based service placement, neural network embedding in IoT, and intelligent sterilization systems, reflected in several IEEE and Scopus-indexed publications. In addition to publications, he has contributed innovative patents, such as systems for vehicle communication during accidents. His research skills encompass advanced AI modeling, simulation of communication networks, and interdisciplinary problem-solving in sustainable technologies. Mr. Alenazi is an active member of IEEE, AAAI (USA), AISB (UK), PMI, and the Saudi Council of Engineers, and he holds prestigious certifications including CCNA, CompTIA Security+ CE, and PMP. He has consistently demonstrated leadership in academia and professional communities, bridging industry and research while mentoring students. With a growing academic profile of 28 citations, 7 documents, and an h-index of 3, he is well-positioned for continued impact and recognition in his field.

Profiles: Google Scholar | Scopus | ORCID  | ResearchGate

Featured Publications

  1. Alenazi, M. M., Yosuf, B. A., El-Gorashi, T., & Elmirghani, J. M. H. (2020). Energy efficient neural network embedding in IoT over passive optical networks. 2020 22nd International Conference on Transparent Optical Networks (ICTON), 1–6. Cited by: 13

  2. Yosuf, B. A., Mohamed, S. H., Alenazi, M. M., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient AI over a virtualized cloud fog network. Proceedings of the Twelfth ACM International Conference on Future Energy Systems. Cited by: 11

  3. Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient distributed machine learning in cloud fog networks. 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), 935–941. Cited by: 9

  4. Banga, A. S., Alenazi, M. M., Innab, N., Alohali, M., Alhomayani, F. M., Algarni, M. H., & others. (2024). Remote cardiac system monitoring using 6G-IoT communication and deep learning. Wireless Personal Communications, 136(1), 123–142. Cited by: 4

  5. Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2022). Energy efficient placement of ML-based services in IoT networks. 2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom). Cited by: 4

CHENGZU DONG | Computer Science | Best Researcher Award

Prof . CHENGZU DONG | Computer Science | Best Researcher Award

Assistant Professor at Lingnan University , Hong Kong

Dr. Chengzu Dong is a highly accomplished early-career researcher specializing in cybersecurity, AI, blockchain, IoT, UAVs, and edge computing. Currently an Assistant Professor at Lingnan University, he has published over 30 peer-reviewed papers in prestigious Q1 journals and Core A/A* conferences, showcasing his strong research productivity and interdisciplinary expertise. His collaborations with CSIRO and industry partners reflect a robust blend of academic rigor and applied impact. Dr. Dong has received multiple accolades, including best paper awards, hackathon honors, and scholarships, highlighting his innovation and leadership potential. In addition to research, he has made significant contributions to teaching, mentoring, and curriculum development across several international institutions. While he could further enhance his profile through principal investigator roles and broader international visibility, his achievements and contributions make him a strong candidate for the Best Researcher Award, particularly in emerging areas of intelligent systems, secure computing, and next-generation network technologies.

Professional Profile 

Education🎓

Dr. Chengzu Dong has a strong and diverse educational background in computer science and information technology. He earned his Ph.D. from Deakin University, Australia, specializing in blockchain, AI, UAVs, edge computing, IoT, Web3, and the Metaverse. During his Ph.D. studies (2021–2024), he conducted extensive research in next-generation technologies, contributing significantly to academic and applied fields. Prior to that, he completed a Bachelor of Computer Science (Honours) at Swinburne University of Technology in 2020, where he deepened his expertise in software development and systems engineering. He also holds a Bachelor of Information Technology from Deakin University (2016–2019), which laid the foundation for his interests in cybersecurity and emerging technologies. His education reflects a consistent focus on interdisciplinary innovation and a strong grounding in both theoretical knowledge and practical applications. Dr. Dong’s academic journey across top Australian universities has prepared him well for a career in high-impact, technology-driven research and teaching.

Professional Experience📝

Dr. Chengzu Dong brings a rich and diverse professional background in academia, research, and industry. He is currently serving as an Assistant Professor at Lingnan University, Hong Kong, where he teaches and develops courses in blockchain, data mining, machine learning, and cybersecurity. Prior to this, he held multiple academic roles at Deakin University, including seminar lecturer, academic tutor, course developer, and capstone mentor, contributing significantly to curriculum innovation and student mentorship. His research experience includes collaborations with CSIRO, Australia’s leading scientific agency, where he worked on blockchain and AI projects. He also held positions as a research assistant at Swinburne University and Deakin University, and worked in software development roles at Artchain Global, Creative Geelong, and FPT Software. These roles highlight his strong technical skills and ability to bridge academia and industry. Dr. Dong’s professional journey reflects a well-rounded portfolio of teaching, research, and applied innovation in emerging technologies.

Research Interest🔎

Dr. Chengzu Dong’s research interests lie at the intersection of emerging technologies and intelligent systems, with a strong focus on cybersecurity, blockchain, artificial intelligence (AI), Internet of Things (IoT), unmanned aerial vehicles (UAVs), edge computing, Web3, and the Metaverse. His work aims to address critical challenges in data privacy, secure communication, and decentralized systems through the integration of blockchain and federated learning frameworks. He is particularly passionate about developing secure and efficient architectures for UAV delivery systems and smart edge networks, making his research highly relevant to real-world applications. Dr. Dong’s interdisciplinary approach combines theoretical advancements with practical implementations, as demonstrated by his collaborations with CSIRO and numerous industry partners. His contributions not only advance academic knowledge but also provide innovative solutions to pressing technological issues in digital security and autonomous systems. This diverse and forward-looking research portfolio positions him as a thought leader in next-generation computing and intelligent infrastructure.

Award and Honor🏆

Dr. Chengzu Dong has received numerous awards and honors in recognition of his academic excellence, research impact, and innovative contributions to emerging technologies. He was the recipient of the prestigious Deakin University Postgraduate Research Scholarship and a CSIRO Top-up Scholarship, supporting his advanced research in blockchain and AI. His work has earned best paper awards at international conferences such as IEEE IAS GLOBCONHT 2023, and he has achieved first runner-up prizes in blockchain hackathons in both Thailand and Australia. Dr. Dong is a certified member of the Australian Computer Society and holds various professional certifications, including Cisco CCNET and Certificate IV in Training and Assessment. His academic excellence was further recognized with the Golden Key Top 15% Student Award. He has also received recognition as a journal and conference reviewer, including a free ACM membership. These accolades collectively highlight his leadership, innovation, and dedication to research excellence and professional development.

Research Skill🔬

Dr. Chengzu Dong possesses a comprehensive set of research skills that span theoretical development, applied experimentation, and interdisciplinary collaboration. He is highly proficient in blockchain technology, artificial intelligence, federated learning, and cybersecurity frameworks, with a particular focus on secure systems for UAVs and edge computing environments. Dr. Dong demonstrates strong technical expertise in programming languages such as Python, Node.js, and React, along with experience in data analytics, machine learning model development, and system architecture design. His ability to design privacy-preserving frameworks and implement decentralized solutions reflects his strength in combining research theory with practical outcomes. Additionally, his experience working with organizations like CSIRO showcases his capability to collaborate on large-scale, real-world projects. Dr. Dong is also skilled in academic writing and publishing, with over 30 high-quality publications in top-tier journals and conferences. His strong analytical mindset, problem-solving ability, and innovation make him a highly capable and impactful researcher in advanced computing domains.

Conclusion💡

Dr. Chengzu Dong exemplifies the qualities of an outstanding researcher and academic, making him a highly suitable candidate for the Best Researcher Award. His extensive contributions to cutting-edge areas such as blockchain, AI, cybersecurity, and UAV systems reflect both depth and breadth in research expertise. With over 30 high-impact publications, multiple international awards, and active collaborations with renowned institutions like CSIRO, Dr. Dong has demonstrated consistent research excellence and innovation. His ability to translate theoretical knowledge into practical solutions for real-world challenges, especially in emerging technologies, underscores his relevance and leadership in the field. Additionally, his dedication to teaching and mentoring at multiple universities enhances his influence in shaping future researchers and professionals. Dr. Dong’s interdisciplinary skills, academic achievements, and forward-thinking research agenda not only position him as a leader in his domain but also affirm his deserving candidacy for this prestigious recognition.

Publications Top Noted✍

  • Title: BBM: A Blockchain-Based Model for Open Banking via Self-Sovereign Identity
    Authors: C. Dong, Z. Wang, S. Chen, Y. Xiang
    Year: 2020
    Citations: 32

  • Title: A Novel Security Framework for Edge Computing Based UAV Delivery System
    Authors: A. Yao, F. Jiang, X. Li, C. Dong, Y.X. Jia, X.L. Gang Li
    Year: 2021
    Citations: 28

  • Title: Enhancing Quality of Service Through Federated Learning in Edge-Cloud Architecture
    Authors: J. Zhou, S. Pal, C. Dong, K. Wang
    Year: 2024
    Citations: 22

  • Title: A Blockchain-Aided Self-Sovereign Identity Framework for Edge-Based UAV Delivery System
    Authors: C. Dong, F. Jiang, X. Li, A. Yao, G. Li, X. Liu
    Year: 2021
    Citations: 20

  • Title: Optimizing Performance in Federated Person Re-Identification Through Benchmark Evaluation for Blockchain-Integrated Smart UAV Delivery Systems
    Authors: C. Dong, J. Zhou, Q. An, F. Jiang, S. Chen, L. Pan, X. Liu
    Year: 2023
    Citations: 15

  • Title: Continuous Authentication for UAV Delivery Systems Under Zero-Trust Security Framework
    Authors: C. Dong, F. Jiang, S. Chen, X. Liu
    Year: 2022
    Citations: 15

  • Title: A Privacy-Preserving Location Data Collection Framework for Intelligent Systems in Edge Computing
    Authors: A. Yao, S. Pal, X. Li, Z. Zhang, C. Dong, F. Jiang, X. Liu
    Year: 2024
    Citations: 11

  • Title: A Framework for User Biometric Privacy Protection in UAV Delivery Systems with Edge Computing
    Authors: A. Yao, S. Pal, C. Dong, X. Li, X. Liu
    Year: 2024
    Citations: 11