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

Bin Liu | Computer Science | Research Excellence Award

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Prof. Bin Liu | Computer Science | Research Excellence Award

Professor | Northwest A&F University | China

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

Citation Metrics (Scopus)

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2,949

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View Scopus Profile
 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

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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

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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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 View ORCID Profile

Featured Publications

Boris Goldengorin | Computer Science | Best Researcher Award

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Prof. Boris Goldengorin | Computer Science | Best Researcher Award

Optimal Management of Tools in Computer Science at Ohio University, United States

Prof. Boris Goldengorin is a globally recognized expert in combinatorial optimization, applied mathematics, and operations research, with a career spanning over five decades. Holding multiple PhDs and a Doctor of Science, he pioneered groundbreaking data correcting algorithms that revolutionized the solving of complex optimization problems such as the Quadratic Cost Partition, Max-Cut, and Traveling Salesman Problems. With over 100 publications in leading international journals and numerous books and monographs, his research has significantly advanced quantitative logistics, supply chain management, and industrial engineering. His algorithms have consistently outperformed global benchmarks, holding world records in solving large-scale combinatorial problems. Prof. Goldengorin has also served as an associate editor for several prestigious journals and has mentored generations of top-performing researchers and students. Honored internationally for his scientific contributions, he continues to influence both theoretical research and practical applications across disciplines, making him a leading figure in modern combinatorial optimization and applied mathematics.

Professional Profile

Education

Prof. Boris Goldengorin possesses an extensive and diverse educational background, reflecting his deep expertise across engineering, applied mathematics, and optimization. He earned his first MSc in Electrical Engineering from Ryazan Radio-Engineering Institute, Russia, in 1967, followed by a second MSc in Applied Mathematics from the Moscow Institute of Electronics and Mathematics in 1973. He completed his PhD in Engineering Sciences at the prestigious VNIINMASH, part of the USSR Ministry of Standardization, in 1975. Further demonstrating his commitment to advanced research, he earned a Doctor of Science (ScD) in Engineering Sciences from the Institute for System Analysis at the USSR Academy of Sciences in 1989. His academic journey continued internationally, obtaining a PhD in Combinatorial Optimization from the University of Groningen, The Netherlands, in 200

Professional Experience

Prof. Boris Goldengorin has built a distinguished career as a researcher, professor, and global leader in combinatorial optimization and operations research. He has held prominent academic and research positions at top institutions, including the University of Groningen (Netherlands), Ohio University (USA), and Khmelnitsky National University (Ukraine), contributing extensively to the fields of mathematical programming, quantitative logistics, and industrial engineering. His pioneering work on data correcting algorithms has shaped modern approaches to solving large-scale optimization problems. Prof. Goldengorin also serves as an associate editor for leading journals such as the Journal of Global Optimization, Journal of Combinatorial Optimization, and Journal of Computational and Applied Mathematics, showcasing his influence in global scientific discourse. Alongside his research, he has mentored generations of students, many of whom have become world-class researchers. His career reflects a rare blend of theoretical innovation, practical application, and global academic leadership, making him a pivotal figure in applied mathematics and operations research.

Research Interest

Prof. Boris Goldengorin’s research interests lie at the intersection of combinatorial optimization, operations research, applied mathematics, and quantitative logistics, where he has made pioneering contributions for over five decades. His primary focus is on developing data correcting algorithms (DCA) and tolerance-based approaches, which have significantly advanced the efficient solving of large-scale optimization problems. His work spans supply chain management, industrial engineering, network analysis, and scheduling problems, with a particular emphasis on benchmark instances such as the Quadratic Cost Partition Problem, Max-Cut Problem, Traveling Salesman Problem, and Simple Plant Location Problem. Beyond classical optimization, Prof. Goldengorin explores the mathematical foundations of algorithmic efficiency and robustness, contributing to big data analysis, game theory, and image processing. His research combines theoretical rigor with computational innovation, enabling faster and more accurate solutions to some of the most computationally challenging problems across disciplines, ensuring long-term impact on both academia and industry applications.

Awards and Honors

Prof. Boris Goldengorin has received numerous awards and honors throughout his illustrious career, recognizing his extraordinary contributions to combinatorial optimization, applied mathematics, and operations research. In 2015, he was named C. Paul Stocker Honorary Professor in Industrial and Systems Engineering at Ohio University, USA. In 2013, the United States Citizenship and Immigration Services (USCIS) granted him Honorable Recognition as an Alien with Extraordinary Ability in Science, Technology, and Education. In 2008, he was recognized as the Best Scientist in Applied Mathematics and Informatics by the Municipality of Khmelnitsky Region, Ukraine. His contributions were further acknowledged in 2005 when Khmelnitsky National University awarded him an Honorary Doctorate in Applied Mathematics and Computer Technologies. Earlier, in 2003, he was named a Fellow in Quantitative Logistics by the Royal Netherlands Academy of Arts and Sciences. These prestigious honors reflect Prof. Goldengorin’s global impact and pioneering role in advancing applied mathematics and optimization research.

Research Skills

Prof. Boris Goldengorin possesses exceptional research skills that span theoretical development, algorithm design, computational experimentation, and interdisciplinary application. His ability to formulate complex combinatorial optimization problems, develop innovative algorithms such as Data Correcting Algorithms (DCA), and rigorously validate their performance through extensive computational benchmarking sets him apart as a world-class researcher. His expertise includes algorithmic design for large-scale optimization problems, quantitative logistics modeling, and supply chain optimization, showcasing his ability to translate mathematical theory into practical solutions. Prof. Goldengorin also excels in analyzing computational complexity, ensuring his algorithms not only produce optimal solutions but do so with unmatched speed and efficiency, often outperforming the leading methods globally. His collaborative research style, combining mentorship, teamwork, and interdisciplinary thinking, has produced high-impact publications across applied mathematics, operations research, game theory, and industrial engineering, making him a highly versatile and innovative researcher with profound analytical and computational skills.

Conclusion

Dr. Boris Goldengorin is highly suitable for the Best Researcher Award.

His exceptional track record in combinatorial optimization, algorithmic innovations, world-record computational achievements, and long-term research leadership position him as a top contender for such a prestigious award.

His global impact, cross-disciplinary contributions, and ability to outperform top research teams in algorithmic efficiency make him a standout figure in applied mathematics, optimization, and industrial engineering.

Publications Top Noted

  • Proceedings of the 11th International Conference on Integer Programming and Combinatorial Optimization
    M. Jünger, V. Kaibel
    Springer-Verlag
    2005 — 233 citations

  • Branch and peg algorithms for the simple plant location problem
    B. Goldengorin, D. Ghosh, G. Sierksma
    Computers & Operations Research 30 (7), 967-981
    2003 — 112 citations

  • The data-correcting algorithm for the minimization of supermodular functions
    B. Goldengorin, G. Sierksma, G.A. Tijssen, M. Tso
    Management Science 45 (11), 1539-1551
    1999 — 76 citations

  • Improvements to MCS algorithm for the maximum clique problem
    M. Batsyn, B. Goldengorin, E. Maslov, P.M. Pardalos
    Journal of Combinatorial Optimization 27, 397-416
    2014 — 65 citations

  • Network approach for the Russian stock market
    A. Vizgunov, B. Goldengorin, V. Kalyagin, A. Koldanov, P. Koldanov, etc.
    Computational Management Science 11, 45-55
    2014 — 65 citations

  • A hybrid method of 2-TSP and novel learning-based GA for job sequencing and tool switching problem
    E. Ahmadi, B. Goldengorin, G.A. Süer, H. Mosadegh
    Applied Soft Computing 65, 214-229
    2018 — 60 citations

  • Tolerance-based branch and bound algorithms for the ATSP
    M. Turkensteen, D. Ghosh, B. Goldengorin, G. Sierksma
    European Journal of Operational Research 189 (3), 775-788
    2008 — 54 citations

  • Lower tolerance-based branch and bound algorithms for the ATSP
    R. Germs, B. Goldengorin, M. Turkensteen
    Computers & Operations Research 39 (2), 291-298
    2012 — 47 citations

  • Tolerances applied in combinatorial optimization
    B. Goldengorin, G. Jäger, P. Molitor
    Journal of Computational Science 2 (9), 716-734
    2006 — 47 citations

  • Cell formation in industrial engineering: Theory, Algorithms and Experiments
    B. Goldengorin, D. Krushinsky, P.M. Pardalos
    Springer
    2013 — 45 citations

  • Solving the simple plant location problem using a data correcting approach
    B. Goldengorin, G.A. Tijssen, D. Ghosh, G. Sierksma
    Journal of Global Optimization 25, 377-406
    2003 — 38 citations

  • Requirements of standards: optimization models and algorithms
    B. Goldengorin
    (No specific journal listed)
    1995 — 35 citations

  • Worst case analysis of max-regret, greedy, and other heuristics for multidimensional assignment and traveling salesman problems
    G. Gutin, B. Goldengorin, H.J.
    Journal of Heuristics, 169-181
    2008 — 34 citations

  • Complexity evaluation of benchmark instances for the p-median problem
    B. Goldengorin, D. Krushinsky
    Mathematical and Computer Modelling 53 (9-10), 1719-1736
    2011 — 32 citations

  • Flexible PMP approach for large-size cell formation
    B. Goldengorin, D. Krushinsky, J. Slomp
    Operations Research 60 (5), 1157-1166
    2012 — 31 citations

Fengyu Liu | Computer Science | Best Researcher Award

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Dr. Fengyu Liu | Computer Science | Best Researcher Award

PhD candidate at Southeast University, China

Fengyu Liu is a dedicated researcher specializing in deep learning, integrated navigation, intelligent unmanned systems, multi-sensor fusion, and SLAM (Simultaneous Localization and Mapping). He has authored 10 academic papers, including 5 SCI-indexed Q1 journal articles, and has contributed significantly to the fields of robotics and sensor technology. With 5 domestic invention patents and 1 PCT patent, his work demonstrates a strong focus on innovation. He has received numerous awards, including the National Scholarship and the Southeast University ‘Zhishan’ Scholarship, and has won four national and provincial-level first prizes in student competitions. He actively participates in academic conferences and serves as a reviewer for IEEE TIM, IEEE Sensor Journal, and MST journals. His research contributions and leadership in the academic community make him a promising figure in the field of intelligent navigation and robotics.

Professional Profile

Education

Fengyu Liu earned his B.S. degree in Electronic Science and Technology from the School of Instrument and Electronics, North University of China, in 2020. Currently, he is pursuing a Ph.D. in Instrument Science and Technology at the School of Instrument Science and Engineering, Southeast University, Nanjing, China. His doctoral research focuses on deep learning-driven navigation, SLAM, and multi-sensor fusion for intelligent unmanned systems. Throughout his academic journey, he has been recognized for his outstanding performance, receiving prestigious scholarships and awards for academic excellence and research contributions.

Professional Experience

During his undergraduate studies, Fengyu Liu served as the Chair of the Embedded Laboratory at the Innovation Elite Research Institute, where he led multiple student research projects. He has been actively involved in presenting at international conferences, including the 2023 International Conference on Robotics, Control, and Vision Engineering (Tokyo) and the China-Russia “Navigation and Motion Control” Youth Forum (2024, Nanjing). His research findings have been published in top-tier journals, and he has contributed as a reviewer for leading IEEE journals. His expertise in SLAM, sensor fusion, and AI-driven navigation technologies has led to patents and real-world applications, making him a key contributor to the advancement of autonomous systems and intelligent robotics.

Research Interests

Fengyu Liu’s research focuses on deep learning, integrated navigation, intelligent unmanned systems, multi-sensor fusion, and simultaneous localization and mapping (SLAM). His work explores advanced sensor fusion techniques, including the integration of LiDAR, cameras, inertial measurement units (IMUs), and deep learning models to enhance navigation accuracy and autonomy in complex environments. He is particularly interested in developing robust localization algorithms for dynamic and unstructured environments, with applications in robotics, autonomous vehicles, and aerospace navigation. His contributions to AI-driven SLAM and vision-based perception systems aim to improve real-time mapping, object recognition, and motion estimation for next-generation autonomous systems.

Awards and Honors

Fengyu Liu has received multiple prestigious awards, including the National Scholarship and the Southeast University ‘Zhishan’ Scholarship, recognizing his academic excellence. He has won four first prizes at national and provincial-level university student competitions, demonstrating his problem-solving skills and technical expertise. His research has also been recognized at academic conferences, earning him the Outstanding Paper Award at the 2022 Science and Technology Workers Seminar of the Chinese Society of Inertial Technology. His participation in international research forums, such as the China-Russia “Navigation and Motion Control” Youth Forum (2024, Nanjing), further highlights his growing impact in the field.

Research Skills

Fengyu Liu possesses a diverse skill set in deep learning, computer vision, and multi-sensor data fusion, particularly for robotics and autonomous navigation. He is proficient in developing AI-based SLAM algorithms, sensor calibration techniques, and real-time embedded system implementations. His expertise extends to software tools and programming languages, including Python, MATLAB, C++, TensorFlow, and PyTorch, which he utilizes for machine learning and signal processing applications. He has hands-on experience with robotic perception systems, LiDAR-based mapping, and inertial navigation technologies, contributing to multiple high-impact research projects. Additionally, his role as a peer reviewer for IEEE TIM, IEEE Sensor Journal, and MST journals reflects his strong analytical and critical evaluation skills in cutting-edge research.

Conclusion

Fengyu Liu is a highly promising young researcher with strong academic contributions, patents, and international recognition. His research in SLAM, deep learning, and multi-sensor fusion aligns with cutting-edge advancements in robotics and AI. His leadership roles, awards, and editorial responsibilities further strengthen his profile.

For the Best Researcher Award, he is a strong candidate, but additional international collaborations, funded research projects, and industry partnerships could further enhance his competitiveness for top-tier global research awards.

Publications Top Noted

  • Confidence Factor Based Robust Localization Algorithm with Visual-Inertial-LiDAR Fusion in Underground Space

  • LiDAR-Aided Visual-Inertial Odometry Using Line and Plane Features for Ground Vehicles

    • Authors: Jianfeng Wu, Xianghong Cheng, Fengyu Liu, Xingbang Tang, Wengdong Gu
    • Year: 2025
    • DOI: 10.1109/TVT.2025.3527472

  • Spatial Feature Recognition and Layout Method Based on Improved CenterNet and LSTM Frameworks

  • Transformer-Based Local-to-Global LiDAR-Camera Targetless Calibration With Multiple Constraints

  • Spacecraft-DS: A Spacecraft Dataset for Key Components Detection and Segmentation via Hardware-in-the-Loop Capture

  • A Visual SLAM Method Assisted by IMU and Deep Learning in Indoor Dynamic Blurred Scenes

  • A Spatial Layout Method Based on Feature Encoding and GA-BiLSTM

  • Combination of Iterated Cubature Kalman Filter and Neural Networks for GPS/INS During GPS Outages

    • Authors: Fengyu Liu, Xiaohong Sun, Yufeng Xiong, Huang Haoqian, Xiao-ting Guo, Yu Zhang, Chong Shen
    • Year: 2019
    • DOI: 10.1063/1.5094559

Dengtian Yang | Computer Science | Best Researcher Award

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Mr. Dengtian Yang | Computer Science | Best Researcher Award

Student at Institute of Microelectronics of the Chinese Academy of Sciences, China

Yang Dengtian is a promising researcher in the field of Circuit and System, currently pursuing his Ph.D. at the Institute of Microelectronics of the Chinese Academy of Sciences. His research interests focus on hardware-software co-optimization, object detection, and hardware acceleration, with key contributions in developing post-processing accelerators for object detection and improving micro-architecture design for GPGPU. Yang’s project experience spans from UAV object detection to the design of System on Chip (SoC) and the deployment of deep learning models on specialized hardware like NVDLA IP. His dedication to advancing technology is reflected in his published works in renowned journals. Yang is a proactive learner, often sharing his findings on blogs, contributing to the academic community’s growth. His work is poised to have a significant impact in fields such as artificial intelligence, hardware design, and computer vision.

Professional Profile 

Education

Yang Dengtian began his academic journey at Xi’an Jiaotong University, where he earned his Bachelor’s degree in Electronic Science and Technology in 2020. His strong foundational knowledge in electronics laid the groundwork for his current research. In 2020, he began his Ph.D. at the Institute of Microelectronics of the Chinese Academy of Sciences, specializing in Circuit and System. His doctoral research has primarily focused on hardware-software co-optimization and advanced object detection systems, areas that combine his deep understanding of both electronics and cutting-edge computing techniques. Yang’s education has been integral in shaping his research pursuits, allowing him to contribute valuable insights into hardware acceleration and the optimization of machine learning systems. His academic journey is ongoing, with an expected completion of his Ph.D. in 2025.

Professional Experience

Yang has worked on several innovative projects throughout his academic career. His recent project, “Learn and Improve Vortex GPGPU,” focuses on understanding GPGPU micro-architecture design and developing improvements for performance optimization. Another notable project was the “Post-Processing Accelerator for Object Detection,” where he investigated hardware-software co-optimization methods, contributing to the development of a unified accelerator system for object detection. In 2023, Yang worked on the “SoC Building and Yolox-Nano Network Deployment Based on NVDLA IP,” where he built an SoC with NVDLA IP and deployed a Yolox-Nano model on a specialized hardware platform. Yang has also worked on solutions to reduce off-chip memory accesses for CNN inference and deployed deep learning models using Vitis-AI. These experiences, along with his publications in renowned journals, highlight his advanced technical expertise and problem-solving abilities in cutting-edge electronics and AI research.

Research Interest

Yang Dengtian’s primary research interest lies in the intersection of Circuit and System design, hardware-software co-optimization, and artificial intelligence (AI). His work focuses on developing hardware accelerators for deep learning applications, particularly in object detection and micro-architecture optimization. He is passionate about creating more efficient systems for processing large-scale data, especially in environments that require real-time processing, such as unmanned aerial vehicles (UAVs) and embedded systems. Yang’s research includes developing GPGPU micro-architectures, improving System on Chip (SoC) designs, and enhancing the deployment of deep learning models on specialized hardware, such as NVDLA IP. His research aims to bridge the gap between hardware capabilities and software needs, making AI applications more accessible and efficient. He is particularly interested in creating unified frameworks for hardware-software co-design, which could significantly advance machine learning and computer vision technologies.

Awards and Honors

Yang Dengtian’s outstanding contributions to research have been recognized through various accolades. His publication in reputable journals, such as Information and IEICE Transactions on Information and Systems, demonstrates the impact of his work in the field of hardware and software co-optimization. While still early in his career, Yang’s commitment to research excellence has already led to numerous recognitions in his academic community. He has also been acknowledged for his innovative projects in hardware acceleration for AI applications, particularly in the development of post-processing accelerators for object detection. Yang’s work is a testament to his technical expertise and his potential for future awards as his research continues to make significant strides in the fields of electronics, AI, and machine learning. Given his promising trajectory, Yang is likely to receive further honors as his doctoral studies progress and his body of work grows.

Conclusion

Yang Dengtian is undoubtedly a strong contender for the Best Researcher Award due to his innovative approach to research, technical expertise, and significant contributions to the field of hardware-software co-design and optimization. His passion for learning, combined with his publications and project experience, highlights his potential to make substantial advancements in his area of study. However, expanding his collaborations and enhancing the practical impact of his research could further solidify his status as a leading researcher in the field.

Recommendation: Yang Dengtian is highly deserving of the Best Researcher Award, with his strengths outweighing areas for improvement. His future contributions are expected to have a lasting impact in the fields of object detection, hardware acceleration, and micro-architecture design.

Publications Top Noted

  • Title: Nano-carriers of combination tumor physical stimuli-responsive therapies
    Authors: W Jin, C Dong, D Yang, R Zhang, T Jiang, D Wu
    Journal: Current Drug Delivery
    Volume & Issue: 17 (7), 577-587
    Year: 2020
    Cited by: 7
  • Title: Object Detection Post Processing Accelerator Based on Co-Design of Hardware and Software
    Authors: D Yang, L Chen, X Hao, Y Zhang
    Journal: Information
    Volume & Issue: 16 (1), 63
    Year: 2025
    Cited by: Not yet cited (as of 2025)

 

Siliang Ma | Computer Science | Best Researcher Award

Published on by Global Scholar Awards

Dr. Siliang Ma | Computer Science | Best Researcher Award

Senior Algorithm Engineer at School of Computer Science and Engineering, South China University of Technology, China

Dr. Siliang Ma, a Ph.D. candidate at South China University of Technology, is an accomplished researcher specializing in computer science with a focus on image processing and machine learning. With an excellent academic record, including a bachelor’s degree from South China Agricultural University (GPA: 3.99/5), Dr. Ma has made significant contributions to cutting-edge research. His works, published in esteemed journals such as Acta Automatica Sinica and Image and Vision Computing, address topics like calligraphy character recognition, multilingual scene text spotting, and efficient bounding box regression through novel loss functions like MPDIoU and FPDIoU. A skilled programmer proficient in Python, Java, and C#, he has developed robust image processing algorithms and software applications. Dr. Ma also contributes as a reviewer for leading conferences like ICRA and ICASSP, reflecting his commitment to advancing the research community. His innovative and impactful work positions him as a rising talent in computational science.

Professional Profile 

Education

Dr. Siliang Ma has a strong educational background in computer science and engineering. He is currently pursuing a Ph.D. at the South China University of Technology, where he has maintained an excellent GPA of 86.33/100. His doctoral research focuses on cutting-edge topics in image processing, machine learning, and computational algorithms, demonstrating both theoretical depth and practical relevance. Prior to this, Dr. Ma earned his bachelor’s degree from South China Agricultural University, graduating with a remarkable GPA of 3.99/5. His undergraduate studies in mathematics and informatics laid a solid foundation for his advanced research pursuits, equipping him with the analytical and technical skills essential for solving complex computational problems. Through rigorous academic training and dedication, Dr. Ma has excelled in his education, which is further reflected in his extensive publications in high-impact journals and his active engagement in academic conferences and peer reviews.

Professional Experience

Dr. Siliang Ma has gained valuable professional experience through diverse roles in research and industry, complementing his academic achievements. He interned as a Data Analyst at the China Construction Bank Guangdong Branch Technology Center, where he conducted financial data analysis using PostgreSQL, mastering database operations and complex linked table queries. As a Quality Engineer at the China Mobile Guangdong Branch Business Support Center, he developed a JavaWeb-based minimum feature set for user registration, login, and management, and implemented automated quality testing workflows using Jenkins. These roles allowed Dr. Ma to hone his skills in software development, data analysis, and quality assurance, showcasing his ability to translate theoretical knowledge into practical applications. Additionally, his expertise in programming and image processing has led to impactful contributions in academia, particularly in algorithm development. This blend of industrial and research experience positions Dr. Ma as a versatile professional in computer science and engineering.

Research Interest

Dr. Siliang Ma’s research interests lie at the intersection of computer vision, machine learning, and image processing. He is particularly focused on developing innovative algorithms and techniques for efficient and accurate object detection, scene text recognition, and character recognition. His work explores advanced loss functions, such as MPDIoU and FPDIoU, to optimize bounding box regression for both traditional and rotated object detection. Additionally, Dr. Ma has a keen interest in multilingual scene text spotting, where he leverages character-level features and benchmarks to improve the accuracy of text recognition across diverse languages. His research extends to robust graph learning and hypergraph-enhanced self-supervised models for social recommendation systems, showcasing his ability to address complex, real-world challenges. Through his work, Dr. Ma aims to bridge theoretical advancements with practical applications, contributing to the broader fields of artificial intelligence, data analysis, and computational optimization.

Award and Honor

Dr. Siliang Ma has been recognized for his academic and research excellence through various accolades and contributions. As a Ph.D. candidate at South China University of Technology, his consistent high performance, reflected in his impressive GPA, underscores his dedication to academic rigor. Although specific awards or honors are not explicitly listed in his profile, his role as a reviewer for prestigious conferences such as ICRA and ICASSP highlights his esteemed position within the research community. Dr. Ma’s impactful publications in top-tier journals and conferences, including Acta Automatica Sinica and Image and Vision Computing, further demonstrate the high regard in which his work is held. His innovative contributions to image processing and machine learning have earned him recognition as a rising talent in his field. These achievements reflect Dr. Ma’s commitment to advancing computational science and his growing influence in academic and professional circles.

Conclusion

Siliang Ma is a strong candidate for the Best Researcher Award due to his impressive academic record, significant publications, and technical expertise. His contributions to advanced image processing algorithms and innovative loss functions for object detection demonstrate technical ingenuity and research excellence. To further strengthen his profile, he could expand his research impact through interdisciplinary work, mentorship roles, and greater industry engagement.

Publications Top Noted

  • Title: FPDIoU Loss: A loss function for efficient bounding box regression of rotated object detection
    Authors: Siliang Ma, Yong Xu
    Year: 2024
    Citation: Ma, S., & Xu, Y. (2024). FPDIoU Loss: A loss function for efficient bounding box regression of rotated object detection. Image and Vision Computing. https://doi.org/10.1016/j.imavis.2024.105381
  • Title: Rethinking Multilingual Scene Text Spotting: A Novel Benchmark and a Character-Level Feature Based Approach
    Authors: Siliang Ma, Yong Xu
    Year: 2024
    Citation: Ma, S., & Xu, Y. (2024). Rethinking Multilingual Scene Text Spotting: A Novel Benchmark and a Character-Level Feature Based Approach. American Journal of Computer Science and Technology. https://doi.org/10.11648/j.ajcst.20240703.12

Nunzio Alberto Borghese | Computer Science | Best Researcher Award

Published on by Global Scholar Awards

Prof. Nunzio Alberto Borghese | Computer Science | Best Researcher Award

Full professor at Università degli Studi di MIlano, Italy

Professor N. Alberto Borghese is a renowned researcher in computational intelligence and its application to real-world problems. He graduated magna cum laude in Electrical Engineering from Politecnico di Milan and has held significant academic positions, including Full Professor at the University of Milan. His research focuses on innovative methods such as multi-scale hierarchical neural networks, adaptive clustering, and statistical data processing, with particular emphasis on limited processing time. He has made notable contributions to e-Health and robotics, integrating AI, service robots, virtual communities, and smart objects to improve healthcare and welfare systems. With over 90 journal papers, 140+ conference papers, and 16 international patents, he has a strong academic and industrial impact. He has led several high-profile projects funded by the European Commission and Italian government, including REWIRE, MOVECARE, and AIRCA. His work continues to advance the intersection of AI, robotics, and healthcare, addressing critical societal needs.

Professional Profile 

Education

Professor N. Alberto Borghese received his education in Electrical Engineering, graduating magna cum laude in 1986 from Politecnico di Milan, one of Italy’s leading institutions. This strong academic foundation laid the groundwork for his extensive research career. His academic journey furthered through his role as a tenured researcher at the National Research Council (CNR) from 1987 to 2000, where he began developing his expertise in computational intelligence. This led to his appointment as an Associate and later Full Professor at the Department of Computer Science, University of Milan (UNIMI). At UNIMI, he also directs the Laboratory of Applied Intelligent Systems, where he has mentored students and led cutting-edge research projects. Professor Borghese’s education and professional development have been marked by continuous innovation, research leadership, and a commitment to applying his knowledge to real-world challenges, particularly in e-Health, robotics, and AI.

Professional Experience

Professor N. Alberto Borghese has had a distinguished professional career, beginning as a tenured researcher at the National Research Council (CNR) from 1987 to 2000. During this time, he built a strong foundation in computational intelligence. He then transitioned to the University of Milan (UNIMI), where he became an Associate Professor and later a Full Professor in the Department of Computer Science. At UNIMI, he also directs the Laboratory of Applied Intelligent Systems, where he leads innovative research projects focused on AI, robotics, and e-Health. Throughout his career, he has contributed to over 90 journal papers, more than 140 conference papers, and holds 16 international patents. Professor Borghese has led several major research projects funded by the European Commission, including REWIRE, MOVECARE, and FITREHAB, and has been involved in multiple Italian government-funded initiatives. His work bridges academia and industry, addressing pressing societal needs in healthcare and welfare through technological advancements.

Research Interest

Professor N. Alberto Borghese’s research interests lie primarily in the field of computational intelligence, focusing on the development and application of advanced algorithms to solve real-world problems. He specializes in multi-scale hierarchical neural networks, adaptive clustering, and statistical data processing, with an emphasis on optimizing solutions for limited processing time. His work extends to the integration of Artificial Intelligence (AI) and robotics, particularly in the domains of e-Health and e-Welfare. Professor Borghese has pioneered the use of service robots, virtual communities, and smart objects, creating innovative platforms that enhance healthcare and welfare systems. His research also explores the intersection of AI with healthcare technologies such as exer-games, aiming to improve accessibility and promote well-being. Additionally, he has a strong focus on interdisciplinary collaboration, leading several European and Italian research projects that combine AI, robotics, and human-centered design to address societal challenges in health, aging, and rehabilitation.

Award and Honor

Professor N. Alberto Borghese has received numerous awards and honors throughout his distinguished academic and research career. His recognition stems from his innovative contributions to computational intelligence, AI, and robotics, particularly in the fields of e-Health and e-Welfare. With over 90 journal papers and 140+ conference papers, his research has garnered widespread acclaim, reflected in his h-index of 42. He has also been honored for his extensive intellectual property contributions, holding 16 international patents. His leadership in research has been recognized through his involvement in high-profile projects funded by the European Commission and Italian government, such as REWIRE (FP7), MOVECARE (H2020), and AIRCA (2023-2025). These honors not only underline his academic excellence but also highlight his impact on advancing technology in healthcare and welfare systems. His continued success in securing major funding and his role in shaping interdisciplinary research make him a highly respected figure in his field.

Conclusion

Based on his exceptional academic qualifications, pioneering research in computational intelligence and e-Health, leadership in high-profile projects, and impressive publication and patent record, N. Alberto Borghese is a highly suitable candidate for the Best Researcher Award. Addressing minor improvements in public engagement and cross-disciplinary impact could further strengthen his candidacy. Nonetheless, his proven expertise and contributions make him a deserving nominee.

Publications Top Noted

  • Kinematic determinants of human locomotion
    • Authors: N. Alberto Borghese, L. Bianchi, F. Lacquaniti
    • Year: 1996
    • Citations: 553
  • Different brain correlates for watching real and virtual hand actions
    • Authors: D. Perani, F. Fazio, N. A. Borghese, M. Tettamanti, S. Ferrari, J. Decety, …
    • Year: 2001
    • Citations: 402
  • Autocalibration of MEMS accelerometers
    • Authors: I. Frosio, F. Pedersini, N. A. Borghese
    • Year: 2008
    • Citations: 261
  • Time-varying mechanical behavior of multijointed arm in man
    • Authors: F. Lacquaniti, M. Carrozzo, N. A. Borghese
    • Year: 1993
    • Citations: 202
  • Internal models of limb geometry in the control of hand compliance
    • Authors: F. Lacquaniti, N. A. Borghese, M. Carrozzo
    • Year: 1992
    • Citations: 197
  • Reading the reading brain: a new meta-analysis of functional imaging data on reading
    • Authors: I. Cattinelli, N. A. Borghese, M. Gallucci, E. Paulesu
    • Year: 2013
    • Citations: 188
  • A functional-anatomical model for lipreading
    • Authors: E. Paulesu, D. Perani, V. Blasi, G. Silani, N. A. Borghese, U. De Giovanni, …
    • Year: 2003
    • Citations: 163
  • The role of vision in tuning anticipatory motor responses of the limbs
    • Authors: F. Lacquaniti
    • Year: 1993
    • Citations: 151
  • Exergaming and rehabilitation: A methodology for the design of effective and safe therapeutic exergames
    • Authors: M. Pirovano, E. Surer, R. Mainetti, P. L. Lanzi, N. A. Borghese
    • Year: 2016
    • Citations: 148
  • Self-adaptive games for rehabilitation at home
    • Authors: M. Pirovano, R. Mainetti, G. Baud-Bovy, P. L. Lanzi, N. A. Borghese
    • Year: 2012
    • Citations: 146
  • Transient reversal of the stretch reflex in human arm muscles
    • Authors: F. Lacquaniti, N. A. Borghese, M. Carrozzo
    • Year: 1991
    • Citations: 144
  • Computational intelligence and game design for effective at-home stroke rehabilitation
    • Authors: N. A. Borghese, M. Pirovano, P. L. Lanzi, S. Wüest, E. D. de Bruin
    • Year: 2013
    • Citations: 139
  • Automatic detection of powdery mildew on grapevine leaves by image analysis: Optimal view-angle range to increase the sensitivity
    • Authors: R. Oberti, M. Marchi, P. Tirelli, A. Calcante, M. Iriti, A. N. Borghese
    • Year: 2014
    • Citations: 128
  • Usability and effects of an exergame-based balance training program
    • Authors: S. Wüest, N. A. Borghese, M. Pirovano, R. Mainetti, R. van de Langenberg, …
    • Year: 2014
    • Citations: 121
  • Pattern recognition in 3D automatic human motion analysis
    • Authors: G. Ferrigno, N. A. Borghese, A. Pedotti
    • Year: 1990
    • Citations: 121