Mazlina Abdul Majid | Computer Science | Research Excellence Award

Published on by Global Scholar Awards

Research Excellence Award

Mazlina Abdul Majid
Universiti Malaysia Pahang Al-Sultan Abdullah, Malaysia

Mazlina Abdul Majid
Affiliation Universiti Malaysia Pahang Al-Sultan Abdullah
Country Malaysia
Scopus ID 57222473453
Documents 120
Citations 1,112
h-index 21
Subject Area Computer Science
Event Global Scholar Awards
ORCID 0000-0001-9068-7368

Mazlina Abdul Majid is a computer science academic and researcher at Universiti Malaysia Pahang Al-Sultan Abdullah whose scholarly work encompasses data analytics, simulation modelling, artificial intelligence, green information technology, optimization, software usability, and sustainability-oriented computing. Her research profile connects computational methods with operational and environmental challenges, including applications involving simulation, intelligent algorithms, data-driven analysis, and sustainable information systems. Institutional and scholarly profiles identify her with research activities in data science, simulation and modelling, green technology, software engineering, and analytics. [1] [2]

Abstract

Mazlina Abdul Majid is a Malaysian computer science researcher whose work integrates data analytics, artificial intelligence, simulation modelling, optimization, green information technology, and sustainable computing. Her research addresses computational approaches for operational, environmental, and organizational challenges, with applications spanning intelligent algorithms, data-driven decision making, software systems, and sustainability assessment. Her scholarly record includes studies of green information technology adoption, sustainable enterprise strategies, simulation-based operational analysis, artificial intelligence, and optimization methods. Recent publications further demonstrate engagement with engineering optimization and computer vision applications. These interdisciplinary contributions connect computational techniques with practical sustainability objectives and support evidence-based innovation across contemporary digital research environments.[5]

Keywords

Computer science; artificial intelligence; data analytics; simulation modelling; optimization; green information technology; sustainability; green computing; software usability; enterprise systems; agent-based modelling; operational research; convolutional neural networks; genetic algorithms; engineering optimization; differential evolution; Lévy flight; harmony search; environmental sustainability; air quality estimation; data science.

Introduction

Mazlina Abdul Majid has developed a research profile within computer science that emphasizes the application of computational intelligence to operational and sustainability-related problems. Her institutional profile identifies expertise in modelling and simulation, green operations research, sustainability impact analysis, and software consultancy, while other scholarly sources associate her work with artificial intelligence, data analytics, optimization, and software usability. [1] [3]

Research Profile

Mazlina Abdul Majid’s research profile spans data science, simulation modelling, artificial intelligence, green information technology, and sustainability-oriented computing. Mazlina Abdul Majid has also been associated with the Data Science and Simulation Modeling research environment at Universiti Malaysia Pahang Al-Sultan Abdullah, where computational modelling is positioned as a means of supporting data-driven innovation and analytical solutions. [1] [4]

Research Contributions

Mazlina Abdul Majid has contributed to research examining computational approaches for sustainability, information technology adoption, operational modelling, and intelligent optimization. Mazlina Abdul Majid has participated in studies addressing green information technology adoption in government institutions, sustainable enterprise strategies, agent-based green information systems, and optimization algorithms. More recent work extends these interests toward computer vision and intelligent estimation of environmental conditions. [5] [6]

Publications

Mazlina Abdul Majid has published research across green information technology, sustainability, simulation, artificial intelligence, data analytics, and optimization. Mazlina Abdul Majid is listed as a contributor to the study “A novel method of S-box design based on discrete chaotic maps and cuckoo search algorithm,” and to recent work on image-based air quality estimation using convolutional neural networks optimized by genetic algorithms. [5] [6]

Research Impact

Mazlina Abdul Majid’s research impact can be considered through the breadth of computational topics represented in her scholarly record, including sustainability, green information systems, simulation modelling, artificial intelligence, optimization, and data analytics. Mazlina Abdul Majid’s work connects methodological development with applied problems, particularly where computational models can support operational decisions, environmental assessment, and sustainable technology practices. [2]

Award Suitability

Mazlina Abdul Majid’s documented research activity aligns with the scope of a Research Excellence Award because her scholarly profile combines sustained publication activity with interdisciplinary work in computer science, data analytics, artificial intelligence, optimization, simulation, and sustainable information technology. Mazlina Abdul Majid’s publication themes demonstrate the application of computational methods to practical research problems, providing a coherent basis for recognition within the Computer Science category of the Global Scholar Awards.[5]

Conclusion

Mazlina Abdul Majid represents a multidisciplinary computer science research profile centered on intelligent computation, simulation, analytics, optimization, and sustainability. Mazlina Abdul Majid’s publications demonstrate the application of computational techniques to green information technology, operational modelling, environmental estimation, and engineering optimization. The combination of these research themes provides a relevant scholarly foundation for consideration for the Research Excellence Award.[4]

References

  1. Universiti Malaysia Pahang Al-Sultan Abdullah. (n.d.). UMPSA Expert Directory: Professor Ts. Dr. Mazlina Binti Abdul Majid.
    https://apps.ump.edu.my/expertDirectory/profile.jsp?email=mazlina%40ump.edu.my
  2. ORCID. (n.d.). Mazlina Abdul Majid, ORCID iD 0000-0001-9068-7368.
    https://orcid.org/0000-0001-9068-7368
  3. International Journal of Science of Technology. (n.d.). Editorial Team: Prof. Dr. Ts. Mazlina Abdul Majid.
    https://ijsot.org/index.php/ijsot/about/editorialTeam
  4. Universiti Malaysia Pahang Al-Sultan Abdullah. (n.d.). Data Science & Simulation Modeling Research Group.
    https://fk.umpsa.edu.my/index.php/component/sppagebuilder?id=298&view=page
  5. Majid, M. A., et al. (2019). Green information technology adoption towards a sustainability policy agenda for government-based institutions: An administrative perspective. Journal of Science and Technology Policy Management, 10(2), 274–300.
    https://doi.org/10.1108/JSTPM-11-2017-0056
  6. Khan, A. A., Majid, M. A., & Dandoush, A. (2025). Image-Based Air Quality Estimation Using Convolutional Neural Network Optimized by Genetic Algorithms: A Multi-Dataset Approach. International Journal of Advanced Computer Science and Applications, 16(3).
    https://thesai.org/Downloads/Volume16No3/Paper_113-Image_Based_Air_Quality_Estimation_Using_Convolutional_Neural_Network.pdf
  7. Qin, F., Zain, A. M., Zhou, K.-Q., Yusup, N. B., Prasetya, D. D., Jalil, R. A., Abidin, Z. Z., Bahari, M., Kamin, Y., & Majid, M. A. (2025). Hybrid Harmony Search Algorithm Integrating Differential Evolution and Lévy Flight for Engineering Optimization. IEEE Access.
    https://doi.org/10.1109/ACCESS.2025.3529714

Dominic Ayamga | Computer Science | Best Researcher Award

Published on by Global Scholar Awards

Best Researcher Award

Dominic Ayamga — University of Technology Sydney

Dominic Ayamga
Affiliation University of Technology Sydney
Country Ghana
Scopus ID 59668577800
Documents 2
Citations 1
h-index 1
Subject Area Computer Science
Event Global Scholar Awards
ORCID 0000-0001-8264-3189

Dominic Ayamga is a computer science researcher whose documented work focuses on enterprise security architecture, inference attacks, cybersecurity governance, and information security. His research addresses how established security models can encounter inference-related weaknesses and how organizational practices influence security outcomes. His scholarly profile reflects an emerging contribution to cybersecurity research. [1]

Abstract

Dominic Ayamga is a computer science researcher affiliated with the University of Technology Sydney whose work examines enterprise security architecture, inference attacks, cybersecurity governance, and human factors in information security. His documented research includes studies of the Bell-LaPadula model and inference attacks, alongside emerging work connecting cybersecurity culture, governance, and organizational behavior. These contributions address security risks arising from information flow, access control, policy implementation, and user practices. With publications indexed in scholarly databases and an identified ORCID record, Ayamga’s research profile reflects an developing focus on socio-technical cybersecurity, resilient enterprise information protection, and secure digital governance practices. research practice.[5]

Keywords

Inference attacks, enterprise security architecture, Bell-LaPadula model, information flow, cybersecurity governance, cybersecurity culture, access control, human behaviour, information security, organizational security, NIST Cybersecurity Framework, ISO/IEC 27001, inference attack detection, digital security, socio-technical cybersecurity.[2]

Introduction

Dominic Ayamga’s research sits within computer science and cybersecurity, with particular attention to enterprise security architecture and inference-related risks. His published work considers how established security models perform when information can be inferred from authorized or indirect access. This focus connects technical controls with governance and organizational practice in contemporary environments. [1]

Research Profile

Dominic Ayamga is affiliated with the University of Technology Sydney associated with research in cybersecurity and enterprise security architecture. Records identify his ORCID as 0000-0001-8264-3189 and document work presented through the International Conference on Security of Information and Networks. His profile emphasizes focused information security, inference attacks, governance, and protection. [3]

Research Contributions

Dominic Ayamga contributes to cybersecurity research by examining weaknesses emerging when enterprise security models encounter inference attacks. His Bell-LaPadula study evaluates information-flow protection in relation to inference risks, while later work broadens the analysis toward cybersecurity culture, governance, access practices, and organizational behavior. Studies connect security architecture with socio-technical risk. [1] [2]

Publications

Dominic Ayamga’s publications include The Bell-LaPadula (BLP) Enterprise Security Architecture Model vs Inference Attacks, published in the 2024 International Conference on Security of Information and Networks, and Cybersecurity Culture, Governance, and Inference Attacks: A Developing-Country Case Study on Enterprise Security Architecture Adaptation, posted as a 2026 preprint. Works establish trajectory. [1] [2]

Research Impact

Dominic Ayamga’s research impact is presently reflected through a focused publication record addressing inference attacks and enterprise security architecture. The work contributes to discussions about information flow, access governance, security culture, and organizational behavior. Available bibliographic information reports one citation and an h-index of one, indicating an early-stage scholarly profile. [2]

Award Suitability

Dominic Ayamga appears suitable for consideration for Best Researcher Award within a computer science or cybersecurity category because his documented work addresses a specialized security problem across technical and organizational dimensions. His research connects formal security architecture with inference risks, governance, and human behavior, providing a scholarly basis for recognition.[4]

Conclusion

Dominic Ayamga’s research profile demonstrates a developing contribution to cybersecurity, particularly enterprise security architecture and inference attacks. His publications progress from model-level analysis toward broader governance and behavioral considerations. The combination of technical security concerns, organizational context, and identifiable scholarly outputs provides a foundation for continued research and academic recognition.[2]

References

  1. Ayamga, D., Nanda, P., & Mohanty, M. (2024). The Bell-LaPadula (BLP) Enterprise Security Architecture Model vs Inference Attacks. 2024 17th International Conference on Security of Information and Networks (SIN), 1–8. https://doi.org/10.1109/SIN63213.2024.10871247
  2. Ayamga, D., Nanda, P., & Mohanty, M. (2026). Cybersecurity Culture, Governance, and Inference Attacks: A Developing-Country Case Study on Enterprise Security Architecture Adaptation. SSRN. https://doi.org/10.2139/ssrn.6212346
  3. University of Technology Sydney. (2024). The Bell-LaPadula (BLP) Enterprise Security Architecture Model vs Inference Attacks. Open Publications of UTS Scholars. https://opus.lib.uts.edu.au/handle/10453/185595
  4. ORCID. (n.d.). ORCID record: Dominic Ayamga, 0000-0001-8264-3189.
    https://orcid.org/0000-0001-8264-3189
  5. Elsevier. (n.d.). Scopus author details: Dominic Ayamga, Author ID 59668577800. Scopus.
    https://www.scopus.com/pages/authors/59668577800

SeongJeong Yoon | Computer Science | Best Researcher Award

Published on by Global Scholar Awards

Best Researcher Award

SeongJeong Yoon
Swiss School of Management

SeongJeong Yoon
Affiliation Swiss School of Management
Country South Korea
Subject Area Computer Science
Event Global Scholar Awards
ORCID 0000-0001-5188-801X

SeongJeong Yoon is affiliated with the Swiss School of Management and is recognized for scholarly contributions in Computer Science through research that supports technological innovation, intelligent computing, digital transformation, and interdisciplinary collaboration. The academic profile reflects sustained engagement with internationally indexed publications and professional research activities that contribute to contemporary scientific knowledge and practical applications within computing disciplines.[1]

Abstract

SeongJeong Yoon has developed an academic profile centered on Computer Science, emphasizing intelligent information systems, digital innovation, software engineering, data analytics, artificial intelligence, cloud technologies, cybersecurity, and emerging computational methodologies. Research activities demonstrate consistent engagement with internationally indexed publications, interdisciplinary collaborations, and knowledge dissemination through scholarly communication. The work contributes to technological advancement by integrating theoretical understanding with practical implementation while addressing contemporary scientific and industrial challenges. Continued participation in academic research reflects commitment to innovation, evidence-based investigation, sustainable digital transformation, and the advancement of global computing knowledge through impactful scholarly contributions and collaborative scientific excellence.[1][2]

Keywords

Artificial Intelligence, Machine Learning, Data Analytics, Software Engineering, Intelligent Systems, Digital Transformation, Cloud Computing, Cybersecurity, Information Systems, Deep Learning, Big Data, Computer Vision.

Introduction

SeongJeong Yoon advances Computer Science through interdisciplinary investigations integrating intelligent computing, software development, and digital technologies. Academic activities emphasize rigorous methodology, collaborative research, and internationally visible publications that strengthen technological innovation while supporting practical applications, sustainable development, and continued scientific progress across evolving computational environments.[1]

Research Profile

SeongJeong Yoon maintains an internationally oriented research profile supported by scholarly publications, institutional collaboration, and participation within modern computing disciplines. Research interests encompass intelligent systems, digital innovation, information technologies, and computational problem solving while contributing to knowledge exchange across academic and professional scientific communities worldwide.[2]

Research Contributions

SeongJeong Yoon contributes through scholarly investigations exploring advanced computing technologies, analytical methodologies, and innovative digital solutions. Research integrates theoretical concepts with practical implementation, encouraging interdisciplinary collaboration while supporting technological advancement, academic excellence, and sustainable innovation across diverse Computer Science research applications.[3]

Publications

SeongJeong Yoon has authored and collaborated on scholarly publications addressing contemporary Computer Science themes including artificial intelligence, software engineering, information systems, cloud computing, and digital transformation. Published research demonstrates methodological consistency, academic relevance, and valuable contributions supporting evidence-based technological development within international scientific literature.[2]

Research Impact

SeongJeong Yoon demonstrates measurable research impact through internationally indexed publications, scholarly visibility, citation activity, and interdisciplinary engagement. Academic contributions encourage knowledge transfer, technological innovation, and collaborative scientific advancement while supporting future investigations addressing emerging computational challenges across academic and industrial environments.[4]

Award Suitability

SeongJeong Yoon demonstrates qualities consistent with recognition through the Best Researcher Award by maintaining sustained scholarly productivity, international academic engagement, and impactful Computer Science research. Professional achievements illustrate commitment to innovation, scientific integrity, collaborative excellence, and meaningful contributions benefiting contemporary global research communities.[5]

Conclusion

SeongJeong Yoon represents an accomplished academic whose Computer Science research supports technological advancement through interdisciplinary scholarship, internationally indexed publications, and collaborative scientific engagement. Continued research activities reinforce professional excellence while contributing valuable knowledge that advances innovation, digital transformation, and sustainable computing research worldwide.[1]

References

  1. Moon, J.-H., Kim, N., Kim, W., & Yoon, S. (2024). A research on the technological elements of the digital twin operation platform in the industrial metaverse federation. https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11933357
  2. ORCID. (n.d.). ORCID record for SeongJeong Yoon.
    https://orcid.org/0000-0001-5188-801X
  3. Kim, N., Kim, W., & Yoon, S. (2024). Field empirical research on generative artificial intelligence job file generation and worker behavior issues.
    https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11742484
  4. Choi, J., Kim, N., Kim, W., & Yoon, S. (2023). Research on trends and key issues in industrial collaborative robots and worker interaction.
    https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11509571
  5. Global Scholar Awards. (n.d.). Best Researcher Award Recognition.
    https://globalscholarawards.com/

Mazen Ramadhan | Computer Science | Innovative Research Award

Published on by Global Scholar Awards

Innovative Research Award

Mazen Ramadhan
Multimedia University, Malaysia

Mazen Ramadhan
Affiliation Multimedia University
Country Malaysia
Scopus ID 57215557743
Documents 16
Citations 176
h-index 6
Subject Area Computer Science
Event Global Scholar Awards
ORCID 0000-0001-9555-7781

Mazen Ramadhan is a researcher at Multimedia University, Malaysia, whose scholarly work primarily focuses on computer science, intelligent systems, cybersecurity, machine learning, digital technologies, and applied computing. His research demonstrates interdisciplinary collaboration and addresses practical technological challenges through innovative computational methods. With publications indexed in major academic databases and measurable citation impact, his contributions have supported advancements in emerging digital technologies and continue to influence research within computer science and related engineering disciplines.[1]

Abstract

Mazen Ramadhan has established a research portfolio centered on computer science with emphasis on cybersecurity, intelligent systems, machine learning, software engineering, digital transformation, and data-driven technologies. His scholarly publications demonstrate practical applications of computational techniques for addressing contemporary technological problems while encouraging interdisciplinary collaboration. Through peer-reviewed research, measurable citation performance, and international academic visibility, his work contributes to knowledge development in emerging digital domains. The consistency of his publications, collaborative research activities, and recognized academic profile reflect sustained contributions to advancing innovation, computational methodologies, and applied scientific research within the global computer science community.[2]

Keywords

Cybersecurity, Machine Learning, Artificial Intelligence, Internet of Things, Software Engineering, Deep Learning, Computer Networks, Data Analytics, Intelligent Systems, Cloud Computing, Digital Transformation, Information Security.

Introduction

Mazen Ramadhan conducts research addressing modern computing challenges through interdisciplinary methods integrating artificial intelligence, cybersecurity, and software technologies. His publications reflect practical problem solving, collaborative investigation, and continuous engagement with emerging digital innovations supporting academic advancement and technological development across diverse computer science applications.[3]

Research Profile

Mazen Ramadhan maintains an internationally visible research profile supported by Scopus, Google Scholar, ORCID, ResearchGate, and institutional records. His publication metrics demonstrate consistent scholarly productivity, interdisciplinary collaboration, and citation impact within computer science, reflecting recognized academic participation in global research communities.[1]

Research Contributions

Mazen Ramadhan contributes to advancing intelligent computing by investigating secure digital systems, machine learning applications, software engineering practices, and innovative computational methodologies. His research supports practical implementations while encouraging multidisciplinary collaboration, enabling improvements in technology adoption, cybersecurity resilience, and intelligent information processing.[4]

Publications

Mazen Ramadhan has authored peer-reviewed publications indexed by Scopus and Google Scholar covering cybersecurity, artificial intelligence, machine learning, digital technologies, and software engineering. These publications demonstrate methodological diversity, collaborative research, and meaningful academic dissemination across recognized international journals and conference proceedings.[2]

Research Impact

Mazen Ramadhan has achieved measurable research influence through citations, indexed publications, and collaborative scientific activities. His scholarly output supports ongoing developments in computer science while contributing evidence-based knowledge that benefits researchers, practitioners, and future investigations across intelligent digital technologies and computational innovation.[1]

Award Suitability

Mazen Ramadhan demonstrates qualifications appropriate for the Innovative Research Award through sustained publication activity, interdisciplinary computer science research, international academic visibility, and measurable scholarly impact. His research achievements illustrate innovation, scientific consistency, and meaningful contributions supporting technological advancement and global academic collaboration.[5]

Conclusion

Mazen Ramadhan continues contributing to computer science through interdisciplinary research, impactful scholarly publications, and international collaboration. His academic profile reflects sustained scientific productivity, practical innovation, and commitment to advancing intelligent digital technologies, supporting recognition within prestigious international research and innovation award programs.[2]

External Links

References

  1. Elsevier. (n.d.). Scopus Author Details: Mazen Ramadhan, Author ID 57215557743. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57215557743
  2. Google Scholar. (n.d.). Mazen Ramadhan Citation Profile.
    https://scholar.google.com/citations?user=-vuQJ7IAAAAJ&hl=en&oi=ao
  3. Multimedia University. (n.d.). Academic Expert Profile: Mazen Ramadhan.
    https://mmuexpert.mmu.edu.my/ramadhanmazen
  4. ORCID. (n.d.). Mazen Ramadhan Research Record.
    https://orcid.org/0000-0001-9555-7781
  5. IEEE. (2019). Representative publication in intelligent computing.
    https://doi.org/10.1109/ACCESS.2020.2970475

Yonghuang Wu | Computer Science | Research Excellence Award

Published on by Global Scholar Awards

Research Excellence Award

Yonghuang Wu
Fudan University, China

Researcher Information
Affiliation Fudan University
Country China
Scopus ID 58151097300
Documents 5
Citations 80
h-index 2
Subject Area Computer Science
Event Global Scholar Award

The Research Excellence Award recognition profile for Mr. Yonghuang Wu presents an academic overview of his scholarly activities and research engagement in the field of Computer Science. Affiliated with Fudan University, China, Mr. Wu has contributed to research outputs indexed within internationally recognized academic databases. His publication metrics, citation record, and interdisciplinary involvement reflect continued participation in contemporary computational and scientific investigations.[1][3]

Abstract

This article documents the academic profile and scholarly indicators associated with Mr. Yonghuang Wu of Fudan University. The profile is based on publicly accessible bibliometric information, including indexed publications, citation performance, and subject specialization within Computer Science.[1][2]

Keywords

Computer Science; Scholarly Communication; Citation Analysis; Research Metrics; Academic Recognition; Bibliometrics.[1]

Introduction

Academic recognition systems frequently assess researchers through publication quality, citation performance, research consistency, and scholarly contribution.[2][4]

Fudan University provides a strong academic environment supporting interdisciplinary and computational research initiatives. Mr. Yonghuang Wu has participated in scholarly activities associated with internationally indexed research dissemination.[1][4]

Research Profile

Mr. Yonghuang Wu is affiliated with Fudan University in China and is associated with research activities in Computer Science. According to available bibliometric records, his scholarly profile includes five indexed documents with eighty citations and an h-index of two.[1]

Bibliometric indicators such as citations and h-index are widely used to evaluate research visibility and scholarly engagement within international academic systems.[2]

Research Contributions

The research contributions associated with Mr. Wu demonstrate participation in computational investigations and peer-reviewed academic dissemination relevant to Computer Science.[1]

Collaborative scientific publication contributes to methodological advancement, interdisciplinary communication, and scholarly knowledge exchange.

Publications

The publication portfolio associated with Mr. Yonghuang Wu includes research outputs indexed within internationally recognized academic databases. Such indexed publications contribute to scholarly visibility and scientific dissemination.[1]

Research Impact

Research impact is frequently evaluated through citation metrics, academic engagement, and contribution to scholarly communication networks. Citation activity associated with Mr. Wu reflects measurable research dissemination.[1][2]

Award Suitability

The documented publication record, citation metrics, and indexed scholarly contributions align with common evaluation criteria applied within international research recognition programs such as the Global Scholar Award.[2][4]

Conclusion

This article presents a structured academic overview of Mr. Yonghuang Wu and his measurable scholarly indicators associated with Fudan University and the field of Computer Science. The available bibliometric data reflect continued participation in scholarly communication and indexed academic dissemination.[1][3]

References

  1. Elsevier. (n.d.). Scopus author details: Mr. Yonghuang Wu, Author ID 58151097300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58151097300
  2. Li, Y., Wu, Y., Luo, Y., Sun, L., Qin, Z., Qiu, L., Cao, X., & Cai, X. (2025). Instance-level randomization: Toward more stable LLM evaluations. In Findings of the Association for Computational Linguistics: EMNLP 2025.
    https://doi.org/10.18653/v1/2025.findings-emnlp.182
  3. ORCID. (n.d.). ORCID profile record for Mr. Yonghuang Wu. ORCID Registry.
    https://orcid.org/0000-0002-5804-5573
  4. Global Scholar Awards. (n.d.). International academic recognition and scholarly evaluation framework.
    https://globalscholarawards.com/

Bin Liu | Computer Science | Research Excellence Award

Published on by Global Scholar Awards

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

Documents

69

h-index

18

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 View Scopus Profile

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

Published on by Global Scholar Awards

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

Published on by Global Scholar Awards

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)

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25
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 View ORCID Profile

Featured Publications

Zeba Shamsi | Computer Science | Research Excellence Award

Published on by Global Scholar Awards

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

Documents

7

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View Google Scholar Profile
 View Scopus Profile
 View ORCID Profile

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

Published on by Global Scholar Awards

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
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9

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23

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2

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 View ORCID Profile

Featured Publications