Riad Hossain | Computer Science | Best Scholar Award

Published on by Global Scholar Awards

Best Scholar Award

Riad Hossain
East Delta University, Bangladesh

Riad Hossain
Affiliation East Delta University
Country Bangladesh
Subject Area Computer Science
Event Global Scholar Awards
ORCID 0009-0005-6722-7140

Riad Hossain is a computer science researcher affiliated with East Delta University, Bangladesh, with published work spanning machine learning, natural language processing, cybersecurity, multimodal artificial intelligence, and data resources. His recent research includes Bengali speech analysis for Parkinson’s disease detection, transformer-based aggression detection, adversarial evaluation of large vision-language models, and annotated resume datasets. These publications provide a documented basis for describing his current research profile. [1] [2]

Abstract

Riad Hossain is a computer science researcher affiliated with East Delta University, Bangladesh, whose published work spans machine learning, natural language processing, cybersecurity, multimodal artificial intelligence, and data resources. His research includes Bengali speech analysis for Parkinson’s disease detection, transformer-based aggression detection, adversarial evaluation of large vision-language models, and annotated resume datasets. These studies address applied computational problems through machine learning and deep learning methodologies, with attention to evaluation, explainability, robustness, and under-resourced language resources. His publication record reflects collaborative research involving academic institutions and interdisciplinary computational applications, supporting recognition within the Global Scholar Awards context and practice today research.

Keywords

Machine learning, deep learning, artificial intelligence, natural language processing, Bengali language technology, Parkinson’s disease detection, conversational speech, transformer models, social-media aggression detection, cybersecurity, adversarial machine learning, large vision-language models, LLaVA, projected gradient descent, explainable AI, resume datasets, data annotation, and computer vision methods.

Introduction

Riad Hossain works in computer science with interests reflected in publications on artificial intelligence, machine learning, natural language processing, computer vision, cybersecurity, and data-centric methods. Records associate him with East Delta University and research with Chittagong University of Engineering and Technology, including articles and conference papers from 2025 and 2026. [1] [3]

Research Profile

Riad Hossain’s research profile is characterized by applied artificial intelligence and machine learning studies addressing language, health, security, and multimodal systems. His work includes Bengali conversational speech analysis, social-media aggression classification, visual jailbreak evaluation, resume annotation, and image restoration, demonstrating a portfolio connecting algorithmic development with datasets and evaluation frameworks. [1] [2] [3]

Research Contributions

Riad Hossain has contributed to studies developing or evaluating computational methods for challenging datasets and emerging AI risks. Reported contributions include machine-learning approaches for Bengali Parkinson’s disease screening, transformer-based detection of religion-oriented aggression, projected-gradient-descent analysis of multimodal safety, and resume datasets. These projects emphasize reproducible evaluation and applied problem formulation. [1] [2] [4]

Publications

Riad Hossain has co-authored CareerCorpus: A comprehensive dataset of annotated resumes, BenSParX: A robust explainable machine learning framework for Parkinson’s disease detection from Bengali conversational speech, and Quantifying modality imbalance and visual jailbreak robustness in LLaVA via projected gradient descent. These publications span journals and international academic collaborative research venues. [1] [2] [3]

Research Impact

Riad Hossain’s documented research impact can be described through the scope of recent publications rather than unverified citation metrics. His work contributes datasets, machine-learning evaluations, explainable approaches, and security analyses across healthcare, language technology, multimodal AI, and social-media analysis. Publication records provide evidence of ongoing collaborative research activity across domains. [1] [3]

Award Suitability

Riad Hossain’s publication themes align with an award profile centered on scholarly activity in computer science and artificial intelligence. His research addresses contemporary topics including machine learning, natural language processing, multimodal model security, healthcare speech analysis, and data resources. Assessment should consider Global Scholar Awards criteria alongside verifiable academic records. [1] [3]

Conclusion

Riad Hossain’s scholarly record presents a computer science profile focused on artificial intelligence, machine learning, language technologies, multimodal robustness, and data resources. His publications demonstrate collaborative engagement with computational problems and multiple application domains. Further assessment can use verified bibliographic records, researcher identifiers, publication quality, and published award criteria consistently. [1] [2]

References

  1. Chowdhury, M. S., Chowdhury, A. F., Banu, A., & Hossain, R. (2026). CareerCorpus: A comprehensive dataset of annotated resumes. Data in Brief, 65, 112567. https://doi.org/10.1016/j.dib.2026.112567
  2. Hossain, R., Kabir, M. A., Mowla, A. I. G., Roy, A. C., & Ghosh, R. (2026). BenSParX: A robust explainable machine learning framework for Parkinson’s disease detection from Bengali conversational speech. Artificial Intelligence in Medicine, 182, 103538.  https://doi.org/10.1016/j.artmed.2026.103538
  3. Abdullah, S., Hossain, R., & Chowdhury, M. (2026). Quantifying modality imbalance and visual jailbreak robustness in LLaVA via projected gradient descent. Discover Applied Sciences.  https://doi.org/10.1007/s42452-026-08793-w
  4. Abdullah, S., Hossain, R., & Chowdhury, M. (2026). Quantifying the efficacy of scenario based jailbreaks on Llama using HarmBench and LLM-as-a-judge. Discover Artificial Intelligence. https://doi.org/10.1007/s44163-026-01816-w
  5. Hossain, R., Fariya, K. E., Afrin, S., Banu, A., & Talukder, M. S. (2025). BRAC: A Transformer-Based Model for Detecting Religion-Oriented Aggression in Bengali Social Media Comments. 28th International Conference on Computer and Information Technology. https://doi.org/10.1109/ICCIT68739.2025.11491627

 

Hemraj Singh | Computer Science | Young Scientist Award

Published on by Global Scholar Awards

Young Scientist Award

Hemraj Singh, Yonsei University, South Korea

Hemraj Singh
Affiliation Yonsei University
Country South Korea
Scopus ID 58072710400
Documents 9
Citations 78
h-index 5
Subject Area Computer Science
Event Global Scholar Awards
ORCID 0000-0002-7110-5147

Hemraj Singh is a computer science researcher whose published work centers on computer vision, video understanding, deep learning, and efficient visual recognition. His research addresses video salient object detection, video shadow detection, facial expression recognition, lightweight neural architectures, and resource-aware visual computing. His publication record includes studies of separable convolution, deformable fusion, geometric multi-scale learning, and edge-preserving network design. These themes place his research within contemporary efforts to develop accurate and computationally efficient vision systems for complex video environments and Internet of Things applications.[5]

Abstract

Hemraj Singh is a computer science researcher working in computer vision, video analysis, deep learning, and efficient visual recognition. His research includes video salient object detection, shadow detection, facial expression recognition, and lightweight neural networks for resource-constrained applications. His publications investigate separable convolution, deformable fusion, geometric multi-scale representation, contrastive learning, and edge-preserving feature extraction. These studies address challenges involving motion, occlusion, clutter, computational cost, and visual complexity in video data. His research profile demonstrates sustained engagement with practical artificial intelligence methods for intelligent visual systems, particularly where accuracy, efficiency, and deployment considerations must be balanced in real-world environments and applications. [5]

Keywords

Hemraj Singh’s publication-oriented keywords include video salient object detection, video shadow detection, facial expression recognition, computer vision, deep learning, lightweight neural networks, separable convolution, dilated convolution, deformable fusion, geometric multi-scale learning, pixel-level contrastive learning, spatiotemporal features, edge-preserving networks, Internet of Things, visual recognition, image processing, and resource-efficient artificial intelligence. [3]

Introduction

Hemraj Singh’s research addresses computer vision problems requiring reliable interpretation of dynamic visual information. His publications focus particularly on video salient object detection, where models must identify visually important regions while handling motion, appearance variation, occlusion, blur, and complex backgrounds. His work also considers computational efficiency, reflecting the needs of practical intelligent systems and constrained deployment environments. [2]

Research Profile

Hemraj Singh’s research profile is situated within artificial intelligence and computer vision, with emphasis on video processing and deep neural network design. His documented work includes lightweight architectures for salient object detection, video shadow analysis, and facial expression recognition. The research combines spatial, temporal, geometric, and semantic representations to improve visual understanding while maintaining computational practicality. [1]

Research Contributions

Hemraj Singh’s contributions include efficient separable convolution networks for video salient object detection, deformable separable fusion for visual feature integration, and geometric multi-scale pixel-level contrastive learning for video analysis. His work on HSNet further addresses video shadow detection through hierarchical separable processing and edge-preserving representations. Together, these studies emphasize efficient feature extraction, robust spatial-temporal modeling, and practical computer vision. [2] [3] [4]

Publications

Hemraj Singh has published research including Novel Dilated Separable Convolution Networks for Efficient Video Salient Object Detection in the Wild, DSFNet: Video Salient Object Detection Using a Novel Lightweight Deformable Separable Fusion Network, DMFNet: geometric multi-scale pixel-level contrastive learning for video salient object detection, HSNet: A Novel Edge-Preserving Hierarchical Separable Network for Video Shadow Detection, DSNet: Efficient Lightweight Model for Video Salient Object Detection for IoT and WoT Applications, and DMSNet: A Lightweight and Efficient Facial Expression Recognition Model for IoT and WoT Applications. [1] [3] [4]

Research Impact

Hemraj Singh’s research contributes to computer vision by addressing the practical balance between recognition quality and computational efficiency. His studies investigate lightweight architectures applicable to video processing and Internet of Things environments, while other work targets challenging visual conditions such as shadows, clutter, motion, and scale variation. The combination of these themes supports broader development of deployable artificial intelligence systems. [2] [4]

Award Suitability

Hemraj Singh’s publication activity and research themes provide a relevant basis for consideration for a Young Scientist Award in computer science. His work demonstrates engagement with contemporary artificial intelligence challenges, particularly efficient deep learning, video understanding, salient object detection, shadow detection, and visual recognition. The documented publication record also reflects continued research development across related computer vision problems. [1] [5]

Conclusion

Hemraj Singh represents an emerging research profile in computer science focused on computer vision, video processing, and efficient deep learning. His publications address salient object detection, shadow detection, facial expression recognition, and lightweight model development. The consistent emphasis on efficient visual representations and practical deployment establishes a coherent research direction within contemporary artificial intelligence and intelligent video analysis. [1] [4]

References

  1. DBLP. (n.d.). Hemraj Singh: Bibliographic profile and publications. DBLP Computer Science Bibliography.
    https://dblp.org/pid/338/0146.html
  2. Singh, H., Verma, M., & Cheruku, R. (2025). DMFNet: Geometric multi-scale pixel-level contrastive learning for video salient object detection. International Journal of Multimedia Information Retrieval, 14, 12.
    https://doi.org/10.1007/s13735-025-00361-z
  3. Singh, H., Verma, M., & Cheruku, R. (2023). Novel Dilated Separable Convolution Networks for Efficient Video Salient Object Detection in the Wild. IEEE Transactions on Instrumentation and Measurement, 72.
    https://doi.org/10.1109/TIM.2023.3302911
  4. Singh, H., Verma, M., & Cheruku, R. (2025). HSNet: A Novel Edge-Preserving Hierarchical Separable Network for Video Shadow Detection. Circuits, Systems, and Signal Processing, 44, 3983–4012.
    https://doi.org/10.1007/s00034-024-02983-w
  5. Singh, H., Baithi, P., & Bashir, Z. (2025). DMSNet: A Lightweight and Efficient Facial Expression Recognition Model for IoT and WoT Applications. WWW Companion Volume, 2531–2535.
    https://doi.org/10.1145/3701716.3717576

Danhuai Guo | Computer Science | Best Researcher Award

Published on by Global Scholar Awards

Best Researcher Award

Danhuai Guo — Beijing University of Chemical Technology, China

Danhuai Guo
Affiliation Beijing University of Chemical Technology
Country China
Scopus ID 32667731300
Documents 89
Citations 1,282
h-index 19
Subject Area Computer Science
Event Global Scholar Awards
ORCID 0000-0002-9131-6363

Danhuai Guo is a researcher affiliated with Beijing University of Chemical Technology whose scholarly profile encompasses artificial intelligence, spatial computing, machine learning, geospatial analysis, and data intelligence. His research record includes studies of spatial scene representation, similarity evaluation, trajectory processing, prediction, and emerging computational approaches. His indexed work demonstrates interdisciplinary engagement with contemporary computer science and intelligent information processing. [3]

Abstract

Danhuai Guo is a researcher at Beijing University of Chemical Technology whose scholarly work connects artificial intelligence, spatial computing, machine learning, geospatial analysis, and data intelligence. His publications address spatial scene understanding, similarity assessment, trajectory processing, anomaly detection, and emerging applications of generative models. His record demonstrates interdisciplinary research across computational methods and practical problems involving complex spatial and temporal information. The Best Researcher Award profile recognizes this trajectory through bibliographic indicators, documented publications, citation activity, and broader relevance to contemporary computer science. His work illustrates how modern data driven techniques can support intelligent analysis, modeling, prediction, and decision making. [3]

Keywords

Spatial scene similarity; spatial scene representation; self-supervised learning; deep learning; geospatial intelligence; spatial computing; artificial intelligence; machine learning; trajectory data; spatio-temporal prediction; neural point processes; spatial cognition; scene generation; anomaly detection; large language models; geographic information systems; data mining; pattern recognition.

Introduction

Danhuai Guo is affiliated with the College of Information Science and Technology at Beijing University of Chemical Technology in China. His research spans data mining, artificial intelligence, spatial analysis, machine learning, pattern recognition, and geospatial science. Records show continuing activity in spatial intelligence, computational modeling, and information processing across domains. [1]

Research Profile

Danhuai Guo is represented in scholarly records as a computer science researcher working on computational methods for spatial and temporal information. ScienceDirect lists Beijing University of Chemical Technology as his current affiliation and identifies interests including hashing algorithms, query processing, language modeling, and data intensive topics. These areas indicate research. [1]

Research Contributions

Danhuai Guo has contributed to methods for spatial scene similarity, scene reconstruction, trajectory data processing, and prediction. His publications include convolutional approaches, self supervised representation learning, constrained generation, and emergency response frameworks. Collectively, these studies demonstrate an emphasis on transforming spatial data into representations suitable for analysis, prediction, and decision support. [2]

Publications

Danhuai Guo has published research in journals and conference venues covering computer science, geospatial information, safety science, and computational chemistry. Representative works include DeepSSN, SpatialScene2Vec, spatial scene reconstruction, SceneDiffusion, EventFormer, and research on large language models. His publication record reflects collaboration across disciplines while maintaining a consistent focus on methods. [2] [3]

Research Impact

Danhuai Guo’s research impact can be considered through indexed publications, citation activity, collaborations, and relevance. His work connects machine learning with spatial intelligence and applications such as transportation, emergency response, environmental analysis, and public safety. Bibliographic profiles also show a coauthor network, indicating that his research participates in computational community. [4]

Award Suitability

Danhuai Guo is suitable for consideration for a Best Researcher Award because his research combines sustained publication activity with methodological development. His record includes peer reviewed studies addressing challenges in spatial computing, artificial intelligence, and data intelligence. Award evaluation should consider bibliographic records, research quality, originality, citation context, and relevance. [1]

Conclusion

Danhuai Guo presents a research profile centered on intelligent analysis of complex spatial, temporal, and computational information. His publications demonstrate methodological development across machine learning, spatial representation, prediction, and artificial intelligence applications. The scholarly record supports recognition of a contribution to computer science, while award assessment should remain evidence based. [1]

References

  1. Elsevier. (n.d.). Scopus author details: Danhuai Guo, Author ID 32667731300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=32667731300
  2. Guo, D., Ge, S., Zhang, S., Gao, S., Tao, R., & Wang, Y. (2022). DeepSSN: A deep convolutional neural network to assess spatial scene similarity. Transactions in GIS, 26(4), 1914–1938. https://doi.org/10.1111/tgis.12915
  3. Guo, D., Yu, Y., Ge, S., Gao, S., Mai, G., & Chen, H. (2024). SpatialScene2Vec: A self-supervised contrastive representation learning method for spatial scene similarity evaluation. International Journal of Applied Earth Observation and Geoinformation, 128, 103743. https://doi.org/10.1016/j.jag.2024.103743
  4. Yu, S., Zhu, J., Li, J., Li, X., Wang, K., Tu, J., & Guo, D. (2025). SceneDiffusion: Scene generation model embedded with spatial constraints. ISPRS International Journal of Geo-Information, 14(7), 250. https://doi.org/10.3390/ijgi14070250
  5. Yu, S., Guo, D., Fu, Y., & Jin, W. (2025). EventFormer: A hierarchical neural point process framework for spatio-temporal clustering events prediction. Journal of Big Data, 12, 162. https://doi.org/10.1186/s40537-025-01232-4

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

Shahid Latif | Computer Science | Innovative Research Award

Published on by Global Scholar Awards

Innovative Research Award

Shahid Latif
University of the West of England, United Kingdom

Shahid Latif
Researcher Shahid Latif
Affiliation University of the West of England
Country United Kingdom
Scopus ID 57216963065
Documents 42
Citations 1,494
h-index 14
Subject Area Computer Science
Event Global Scholar Awards
ORCID 0000-0002-6368-2729

The Innovative Research Award recognizes the scholarly achievements and research contributions of Shahid Latif from the University of the West of England, United Kingdom. The recognition highlights academic engagement within the field of Computer Science and acknowledges contributions to interdisciplinary technological research, scientific dissemination, and internationally indexed scholarly communication.[1] The award reflects measurable academic visibility through citation performance, publication activity, and collaborative research participation within global scientific communities.[2]

Abstract

Shahid Latif maintains an internationally visible academic research profile associated with Computer Science and interdisciplinary technological innovation. The researcher’s scholarly activities include peer-reviewed publication, citation-based impact, and international research dissemination through recognized academic platforms.[1] Research visibility and scholarly engagement are further supported through academic identifiers and professional research networking systems.[2]

Keywords

  • Computer Science
  • Artificial Intelligence
  • Research Innovation
  • Scholarly Publications
  • Academic Impact
  • Scopus Indexed Research
  • Interdisciplinary Technology

Introduction

Academic recognition frameworks are designed to acknowledge scholarly excellence, publication visibility, research dissemination, and interdisciplinary scientific contribution. The Innovative Research Award associated with the Global Scholar Awards recognizes researchers demonstrating measurable academic impact and engagement within internationally indexed research environments.[1] Shahid Latif’s academic profile reflects sustained participation in Computer Science research and collaborative technological inquiry. Citation indicators, indexed publications, and scholarly networking profiles collectively demonstrate academic engagement and scientific communication within contemporary research systems.[3]

Research Profile

Shahid Latif is affiliated with the University of the West of England in the United Kingdom and maintains an internationally indexed Scopus author profile documenting research output, citations, and scholarly visibility.[1] The profile currently reflects forty-two indexed documents, 1,494 citations, and an h-index value of fourteen, indicating active scholarly participation and measurable academic impact. Research activities associated with the scholar are connected to Computer Science, intelligent systems, computational innovation, and interdisciplinary technological applications. Scholarly communication through international academic databases supports discoverability and global research accessibility.

Research Contributions

The scholarly contributions associated with Shahid Latif include participation in peer-reviewed research publication, citation-based dissemination, and interdisciplinary computational investigation. Indexed publications contribute to international scientific accessibility and broader academic communication within technological research communities.[1]

  • Contribution to peer-reviewed scientific publication and interdisciplinary research dissemination.
  • Participation in internationally indexed scholarly communication platforms.
  • Research engagement in intelligent computational systems and technological innovation.
  • Support for collaborative scientific inquiry and citation-based academic impact.

Publications

The Scopus author profile associated with Shahid Latif documents scholarly outputs indexed through internationally recognized academic databases.[1] Publication activity contributes to citation-based evaluation, academic visibility, and interdisciplinary scientific communication.[4]  Professional research identifiers including ORCID, Google Scholar, and ResearchGate profiles support discoverability, verification, and accessibility of scholarly contributions within international academic systems.[2][3]

  1. Research articles related to Computer Science and intelligent computational systems.
  2. Peer-reviewed journal and conference publications indexed within scholarly databases.
  3. Academic outputs contributing to citation-based dissemination and scientific accessibility.

Research Impact

Research impact is commonly evaluated through publication visibility, citation activity, scholarly accessibility, and interdisciplinary relevance. Citation metrics associated with Shahid Latif demonstrate measurable engagement within the international research community and broader scientific communication networks.[1] Academic recognition through international award frameworks may support collaboration, institutional engagement, and continued participation in global research initiatives associated with Computer Science and technological innovation.

Award Suitability

The academic profile of Shahid Latif reflects characteristics commonly associated with scholarly recognition programs, including indexed publication activity, measurable citation performance, interdisciplinary engagement, and participation in internationally visible research dissemination systems.[1] Research visibility through professional academic identifiers and scholarly networking systems further supports recognition within contemporary scientific communication environments.[2][4]

Conclusion

Shahid Latif’s scholarly profile demonstrates sustained participation in academic publication, interdisciplinary technological inquiry, and international research dissemination associated with Computer Science and innovation-oriented scientific investigation. Indexed publications, citation indicators, and professional academic profiles collectively support visibility within global scholarly systems.[1] The Innovative Research Award represents recognition of ongoing scholarly engagement, academic communication, and participation in internationally recognized research environments supported through citation databases and professional research identification systems.[2]

References

  1. Elsevier. (n.d.). Scopus author details: Shahid Latif, Author ID 57216963065. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57216963065
  2. ORCID. (n.d.). ORCID researcher profile for Shahid Latif.
    https://orcid.org/0000-0002-6368-2729
  3. Google Scholar. (n.d.). Scholar profile of Shahid Latif.
    https://scholar.google.com/citations?user=CThRoJQAAAAJ&hl=en
  4. ResearchGate. (n.d.). ResearchGate profile of Shahid Latif.
    https://www.researchgate.net/profile/Shahid-Latif-11

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/

Sarbajit Paul Bappy | Computer Science | Research Excellence Award

Published on by Global Scholar Awards

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