Tag: International Computer Science Award

Tamim Mahmud | Computer Science | Best Researcher Award

Published on by World Science Awards

 

Best Researcher Award

Researcher Information
Affiliation Daffodil International University
Country Bangladesh
Scopus ID THiIDGIAAAAJ
Documents 3
Subject Area Artificial Intelligence, Medical Imaging, Computer Science
Event Young Research Excellence Award
ORCID 0009-0006-6164-1620

Tamim Mahmud is a final-year Computer Science and Engineering student at Daffodil International University, Bangladesh. His academic work focuses on artificial intelligence, machine learning, deep learning, computer vision, and medical image analysis. Through multiple research appointments and collaborative projects, he has contributed to AI-assisted healthcare technologies, explainable artificial intelligence, uncertainty-aware deep learning, and intelligent diagnostic systems.[1]

Abstract

Tamim Mahmud has established an emerging research profile in artificial intelligence for healthcare through work in medical imaging, disease prediction, explainable AI, and uncertainty-aware deep learning. His publications and ongoing research projects emphasize clinically relevant computer vision models, diagnostic decision support, and robust machine learning methodologies. His contributions demonstrate interdisciplinary integration between computer science and medical research.[2]

Keywords

Artificial Intelligence, Computer Vision, Deep Learning, Medical Imaging, Machine Learning, Explainable AI, Healthcare Informatics, Disease Prediction, MRI Analysis, CT Imaging.

Introduction

Mahmud’s research centers on developing intelligent healthcare systems capable of improving disease diagnosis through advanced machine learning algorithms. His academic activities combine theoretical AI research with practical healthcare applications, including lung cancer detection, brain tumor classification, cardiovascular disease prediction, maternal health risk assessment, and gastrointestinal image segmentation.[3]

Research Profile

  • Research Assistant, Health Informatics Research Lab (HIRL)
  • Research Fellow, Bangladesh Medical Research Council (BMRC)
  • Research Assistant, ELITE Research Lab LLC, USA
  • Research interests include AI, Medical Imaging, NLP, Computer Vision and Healthcare Analytics.

Research Contributions

His contributions include explainable deep learning models for lung cancer detection, uncertainty-aware brain tumor diagnosis, lightweight transformer-based polyp segmentation, cardiovascular disease prediction, maternal health analytics, Bangla handwriting recognition, and deployment of AI-enabled healthcare systems. His work integrates CNNs, Transformers, ensemble learning, Bayesian uncertainty estimation, and clinical feature engineering.[2]

Publications

  • HALI-Net: Explainable Hybrid Deep Learning Model for Lung Cancer Detection (Intelligence-Based Medicine, 2026).
  • Multiple journal submissions in Intelligence-Based Medicine, Array, and Engineering Reports.
  • Conference papers published or accepted in IEEE, Springer Nature, and Atlantis Press proceedings.
  • Creator of Bangla Handwritten Character and Word Recognition Dataset published on Zenodo.

Research Impact

Although at an early stage of his academic career, Mahmud has demonstrated substantial research productivity through first-author publications, corresponding authorship, competitive research fellowships, interdisciplinary collaborations, and practical AI applications addressing healthcare challenges. His work has potential relevance for clinical decision support, explainable diagnostics, and AI-assisted medical imaging.[4]

Award Suitability

Based on available academic evidence, Tamim Mahmud demonstrates characteristics commonly associated with emerging researcher recognition. These include strong first-author publication activity, participation in nationally funded research, leadership in AI-based healthcare projects, dataset development, and interdisciplinary collaboration. His research aligns well with awards recognizing innovation in artificial intelligence, medical informatics, and early-career scientific achievement.[4]

Conclusion

Tamim Mahmud represents a promising early-career researcher whose work integrates computer science with healthcare innovation. His growing publication record, involvement in funded research, and focus on explainable and clinically applicable AI demonstrate a commitment to advancing intelligent diagnostic technologies and medical decision-support systems.

External Links

References

  1. Curriculum Vitae of Tamim Mahmud (2026).
  2. Mahmud, T. (2026). HALI-Net: An Explainable Hybrid Deep Learning Model with Attention and Texture Fusion for Lung Cancer Detection in CT Images.
    https://doi.org/10.1016/j.ibmed.2026.100419
  3. Mahmud, T. et al. Conference Proceedings (2025–2026), IEEE, Springer Nature, Atlantis Press.
  4. Google Scholar Profile.
    https://scholar.google.com/citations?hl=en&user=THiIDGIAAAAJ

 

Jerry (Zeyu) Gao | Computer Science | Innovative Research Award

Published on by World Science Awards

Innovative Research Award

Jerry Zeyu Gao
Professor, Department of Computer Engineering, San Jose State University, United States

Jerry Zeyu Gao
Affiliation San Jose State University
Country United States
Scopus ID 7404475003
Documents 248
Citations 4,460
h-index 35
Subject Area Computer Science, Software Engineering, Artificial Intelligence
Event World Science Awards

Jerry Zeyu Gao is a distinguished computer scientist, software engineering researcher, educator, and innovator recognized for his extensive contributions to software testing, quality assurance, mobile computing, cloud engineering, artificial intelligence testing, and smart city technologies. As a Professor at San Jose State University, he has developed an influential body of scholarly work spanning software validation, cloud services, mobile application engineering, machine learning applications, intelligent systems, and data-driven urban technologies. His research achievements have contributed substantially to both academic advancement and industrial practice, making him a strong candidate for recognition through an Outstanding Researcher Award.[1]

Abstract

Professor Jerry Zeyu Gao has established an internationally recognized research profile through decades of contributions to software engineering, software testing, cloud computing, artificial intelligence systems, mobile applications, and smart city technologies. His research has produced influential methodologies, testing frameworks, cloud-based architectures, AI validation models, and intelligent urban service solutions. With more than 240 indexed publications, thousands of citations, and an h-index of 35, his scholarly output demonstrates both academic significance and practical relevance. His work continues to influence research communities across software engineering, AI quality assurance, and data-driven computing.[1]

Keywords

Software Engineering, Software Testing, Artificial Intelligence, AI Testing, Cloud Computing, Mobile Computing, Smart Cities, Machine Learning, Quality Assurance, Big Data Analytics, Intelligent Systems, Test Automation.

Introduction

The increasing complexity of software-intensive systems requires advanced methodologies for validation, quality assurance, automation, and intelligent decision support. Professor Jerry Zeyu Gao has dedicated his academic career to addressing these challenges through pioneering research in software testing, mobile systems, cloud services, and artificial intelligence. His work bridges theoretical foundations with industrial implementation, contributing significantly to modern software engineering practices and emerging AI-enabled technologies.[2]

Research Profile

Professor Gao serves in the Department of Computer Engineering at San Jose State University. His academic career encompasses software engineering, cloud systems, mobile applications, AI testing, smart city infrastructure, autonomous systems, environmental analytics, agricultural intelligence, and machine learning applications. He has supervised numerous research projects and collaborated extensively with academic institutions and industry partners worldwide. His interdisciplinary approach has enabled the development of innovative solutions across multiple technological domains.[3]

Research Contributions

Professor Gao’s research contributions span several influential areas of computer science and engineering. His work on object-oriented software testing helped establish foundational methodologies for quality assurance in modern software systems. He later expanded his research into component-based software testing, cloud testing services, mobile application validation, AI software testing, and intelligent automation. More recently, his research has focused on smart cities, renewable energy intelligence, environmental monitoring, agricultural AI platforms, and computer vision-based analytics.[4]

  • Software testing and quality assurance methodologies.
  • Cloud-based testing infrastructure and Testing-as-a-Service (TaaS).
  • Artificial intelligence testing frameworks.
  • Smart city analytics and intelligent transportation systems.
  • Machine learning applications in agriculture and sustainability.
  • Autonomous systems and computer vision validation.

Publications

Professor Gao has authored and co-authored more than 240 scholarly publications, including journal articles, conference papers, book chapters, edited proceedings, and technical books. His publications appear in respected venues such as IEEE Access, IEEE Computer, IEEE Software, World Wide Web, Smart Cities, Agriculture, Remote Sensing, Energies, Sustainability, and numerous IEEE international conferences.[5]

  • Object-Oriented Software Testing (IEEE Computer Society Press).
  • Testing and Quality Assurance for Component-Based Software.
  • Engineering Wireless-Based Software Systems.
  • Mobile Application Testing – A Tutorial (IEEE Computer).
  • AI Testing for Intelligent Chatbots – A Case Study.
  • Integration of UAV and Remote Sensing Data for Early Diagnosis of Crop Diseases.

Research Impact

The impact of Professor Gao’s work is reflected through extensive citation activity, international collaborations, editorial contributions, and practical adoption of research outcomes. His publications have accumulated more than four thousand citations, while several of his studies have become highly referenced resources within software engineering and cloud computing communities. His contributions have influenced academic curricula, industrial testing frameworks, AI validation approaches, and smart infrastructure development projects worldwide.[4]

Award Suitability

Professor Jerry Zeyu Gao demonstrates the qualities expected of an Outstanding Researcher Award recipient through sustained scholarly excellence, innovation, interdisciplinary leadership, and measurable scientific impact. His long-standing commitment to advancing software engineering, cloud services, AI quality assurance, and smart city technologies has produced a substantial body of influential research. The breadth of his contributions, combined with his educational leadership and international collaborations, highlights his significance within the global research community.[5]

Conclusion

Jerry Zeyu Gao has made substantial and sustained contributions to software engineering, testing methodologies, cloud computing, artificial intelligence systems, and intelligent urban technologies. His extensive publication record, strong citation performance, educational leadership, and innovative research portfolio collectively support recognition through an Outstanding Researcher Award. His work continues to shape emerging technological disciplines while providing practical solutions to complex real-world challenges.

References

  1. Elsevier. (n.d.). Scopus Author Details: Jerry Zeyu Gao, Author ID 7404475003. https://www.scopus.com/authid/detail.uri?authorId=7404475003
  2. Gao, J. Z., Tsai, W. T., Paul, R., & Uehara, T. Mobile Testing as a Service (MTaaS).
  3. San Jose State University Faculty Profile and Academic Biography.
  4. Gao, J. Z. Research Publications in Software Engineering, AI Testing, and Smart Cities.
  5. Gao, J. Z. Object-Oriented Software Testing, IEEE Computer Society Press.

Muath Alrammal | Computer Science | Innovative Research Award

Published on by World Science Awards

Innovative Research Award

Muath Alrammal
Affiliation University of Wollongong Dubai
Country United Arab Emirates
Scopus ID 35108740800
Documents 25
Citations 207
h-index 6
Subject Area Computer Science, Big Data, Blockchain, Artificial Intelligence
Event World Science Awards
ORCID 0000-0002-3240-6262

Muath Alrammal is a computer scientist, researcher, and academic specializing in big data systems, XML stream processing, machine learning, blockchain technologies, artificial intelligence, and distributed data architectures. He currently serves as Assistant Professor at the University of Wollongong Dubai and has developed a multidisciplinary research portfolio spanning data-intensive computing, cybersecurity, blockchain-enabled sustainability solutions, malware detection, and intelligent information systems. His scholarly contributions encompass journal articles, conference publications, book chapters, funded research projects, and industry-focused innovation initiatives.[1]

With academic training in France and extensive experience across higher education institutions in the United Arab Emirates, Alrammal has contributed to advancing research in scalable data processing, performance modeling, reinforcement learning, blockchain applications, and artificial intelligence-driven software engineering. His work reflects a combination of theoretical research and practical implementation directed toward digital transformation and emerging technologies.[2]

Abstract

This article presents an academic overview of Muath Alrammal, highlighting his educational background, research specialization, scholarly contributions, and impact within the fields of computer science, artificial intelligence, blockchain technologies, cybersecurity, and large-scale data processing. His work combines foundational research in XML stream processing and scalable information systems with contemporary investigations in machine learning, blockchain-enabled applications, malware analysis, and software engineering. Through academic publications, funded projects, industrial certifications, and collaborative research activities, Alrammal has contributed to the advancement of data-driven technologies and digital transformation initiatives across academia and industry.[1]

Keywords

Big Data, XML Stream Processing, Blockchain, Artificial Intelligence, Machine Learning, Cybersecurity, Data Analytics, Distributed Systems, Software Engineering, Web3 Technologies.

Introduction

The evolution of digital ecosystems has increased the demand for scalable computing systems capable of processing massive volumes of structured and unstructured information. Researchers working at the intersection of data science, distributed computing, and intelligent systems play a critical role in addressing these challenges. Muath Alrammal has established a research profile focused on large-scale data processing, stream-based information retrieval, machine learning applications, and blockchain integration. His academic journey includes doctoral research in France, postdoctoral appointments, leadership positions in higher education, and ongoing involvement in emerging technologies and innovation-driven research initiatives.[1]

Research Profile

Alrammal earned a Ph.D. in Computer Science from Université Paris-Est, France, where his doctoral research focused on algorithms for XML stream processing, external memory management, and scalable performance optimization. His graduate studies were preceded by a Master of Science in Information Technology from Télécom SudParis. Following the completion of his doctorate, he undertook postdoctoral research projects involving high-performance computing, artificial intelligence applications in finance, and secure large-scale document processing systems.[3]

His academic appointments include positions at the University of Wollongong Dubai, Higher Colleges of Technology, and Al-Khawarizmi International College. Across these institutions, he has contributed to teaching, curriculum development, research supervision, and academic governance while maintaining an active publication record in computer science and information technology disciplines.[1]

Research Contributions

Alrammal’s contributions span several research domains, including XML stream processing, performance prediction models, XPath selectivity estimation, malware detection frameworks, reinforcement learning systems, blockchain-enabled resource management, and AI-assisted software engineering. His early work contributed methodologies for scalable querying and processing of large XML datasets, while his more recent investigations have explored cybersecurity analytics, blockchain-based sustainability applications, and intelligent decision-support systems.[4]

  • XML stream processing and scalable query optimization.
  • Big data analytics and performance modeling.
  • Blockchain and decentralized information systems.
  • Machine learning and reinforcement learning applications.
  • Cybersecurity and anti-malware intelligence frameworks.
  • AI-driven software requirements engineering.

Publications

Selected scholarly outputs include journal articles, conference proceedings, and book chapters covering machine learning, blockchain technologies, cybersecurity, XML processing, and intelligent computing systems. Representative publications include contributions to sustainable management systems using blockchain, malware detection methodologies, Industry 4.0 frameworks, reinforcement learning models, and scalable XML query processing techniques.[5]

  • Machine Learning with Python (CRC Press, 2022).
  • Blockchain Technology for Sustainable Management of Electricity and Water Consumption (2023).
  • A Blockchain Solution for Water and Electricity Management (2022).
  • A Two-Layered Machine Learning Approach for Anti-Malware Sustainability (2022).
  • Forward XPath Stream Processing: End-to-End Confidentiality and Scalability (2014).
  • Performance Prediction Model for Forward XPath Processing (2012).

Research Impact

The research activities of Alrammal demonstrate an emphasis on practical impact and technology transfer. His funded projects have addressed malware clustering systems, XML document processing, and intelligent computing frameworks. Ongoing projects involving AI-driven software requirements classification and decentralized credit scoring systems illustrate the application of advanced computational methods to real-world challenges. These efforts contribute to digital transformation, cybersecurity enhancement, sustainable infrastructure management, and intelligent automation initiatives.[2]

Award Suitability

Muath Alrammal’s academic record aligns with the objectives commonly associated with Emerging Research Excellence Award programs. His multidisciplinary research portfolio demonstrates sustained scholarly productivity, innovation in data-intensive computing, contributions to blockchain and artificial intelligence applications, and engagement with industry-oriented research initiatives. The combination of publications, research leadership, funded projects, international collaborations, and technology-focused educational contributions supports recognition within emerging and applied research categories.[1]

Conclusion

Muath Alrammal has developed a diverse and evolving research profile spanning big data systems, blockchain technologies, artificial intelligence, cybersecurity, and distributed computing. Through scholarly publications, academic leadership, interdisciplinary collaborations, and industry-focused innovation projects, he has contributed to the advancement of computational research and digital transformation initiatives. His work reflects an ongoing commitment to bridging theoretical developments with practical technological applications across multiple domains of computer science.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Muath Alrammal, Author ID 35108740800. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=35108740800
  2. Alrammal, M. Research projects and academic profile documentation relating to artificial intelligence, blockchain, and software engineering initiatives.
  3. Alrammal, M. Doctoral thesis: Algorithms for XML Stream Processing: Massive Data, External Memory and Scalable Performance. Université Paris-Est, France.
  4. Alrammal, M., & Hains, G. Research contributions in XML stream processing, selectivity estimation, and scalable information systems.
  5. Alrammal, M., Xanthidou, O. K., & Naveed, M. (2022). Machine Learning with Python. Chapman & Hall/CRC.
    https://doi.org/10.1201/9781003139010
  6. Alrammal, M., Abu-Amara, F., Ismail, Z., & Nadeem, M. (2023). Blockchain Technology for Sustainable Management of Electricity and Water Consumption.
    https://doi.org/10.3390/engproc2023059223

Zahraa Ghabriess | Computer Science | Research Excellence Award

Published on by World Science Awards

Research Excellence Award

Zahraa Ghabriess
ENSTA Bretagne, Lebanon

Zahraa Ghabriess
Affiliation ENSTA Bretagne
Country Lebanon
Google Scholar rkoyQ8oAAAAJ&hl
Subject Area Cybersecurity, Artificial Intelligence, Federated Learning, IoT Security
Event World Science Awards
ORCID 0009-0003-5746-9504

Zahraa Ghabriess is a cybersecurity researcher and doctoral candidate whose academic and professional activities focus on intelligent threat detection, access control systems, federated learning architectures, and security solutions for IoT-enabled 5G and beyond networks. Her work integrates artificial intelligence, machine learning, edge computing, and cybersecurity engineering to address contemporary challenges in digital infrastructure protection. Through research internships, software engineering practice, and doctoral investigations, she has contributed to emerging approaches for intrusion detection and access control automation in distributed environments.[1]

Abstract

Zahraa Ghabriess has developed an interdisciplinary research profile that combines cybersecurity, artificial intelligence, machine learning, and distributed computing systems. Her ongoing doctoral research at ENSTA Bretagne investigates federated edge architectures for intrusion detection in IoT-enabled 5G and beyond communication networks. Through conference publications, submitted journal manuscripts, and collaborative research projects, she contributes to the development of scalable, privacy-preserving, and intelligent security frameworks designed for modern networked environments. Her work addresses emerging cybersecurity challenges associated with large-scale connected systems while promoting advanced detection mechanisms based on federated learning and artificial intelligence methodologies.[2]

Keywords

Cybersecurity, Federated Learning, Intrusion Detection Systems, Internet of Things, 5G Networks, Edge Computing, Artificial Intelligence, Machine Learning, Access Control, Process Mining, Threat Detection, Secure Computing.

Introduction

The rapid expansion of interconnected digital systems has increased the need for innovative cybersecurity solutions capable of protecting large-scale networks from sophisticated threats. Researchers working at the intersection of artificial intelligence and cybersecurity play a crucial role in developing adaptive security mechanisms for future communication infrastructures. Zahraa Ghabriess represents a new generation of cybersecurity researchers whose work focuses on integrating machine learning, federated learning, and edge intelligence into practical security frameworks for IoT-enabled environments. Her academic trajectory demonstrates a commitment to addressing complex security challenges through rigorous research and technological innovation.[3]

Research Profile

Following the completion of a Bachelor of Computer Science and a Master of Science in Cybersecurity from the Lebanese University – Faculty of Sciences, Zahraa Ghabriess expanded her expertise through software engineering practice, research internships, and doctoral studies. Her technical competencies encompass cybersecurity, ethical hacking, artificial intelligence, machine learning frameworks, process mining, data mining, secure programming, web development, mobile application development, and database management systems.[1]

Her current doctoral research at ENSTA Bretagne focuses on intelligent intrusion detection frameworks designed for IoT-enabled 5G and beyond networks. The research combines federated learning methodologies with edge computing architectures to enhance detection accuracy while preserving privacy and reducing centralized processing constraints. These investigations contribute to the broader development of resilient and scalable cybersecurity infrastructures.[2]

Research Contributions

  • Development of the FEDGE framework, a federated edge architecture for attack detection in IoT-enabled 5G and beyond networks.
  • Research on semi-decentralized federated learning models aimed at improving intrusion detection performance and scalability.
  • Investigation of automated extraction of Attribute-Based Access Control (ABAC) rules from Object-Centric Event Logs (OCEL).
  • Application of machine learning techniques for detecting unauthorized access attempts through HTTP request and response analysis.
  • Comparative evaluation of emerging technologies for attack detection in advanced wireless communication networks.

Publications

The publication record of Zahraa Ghabriess reflects active engagement in cybersecurity research, particularly in intelligent attack detection and federated learning applications. Her notable conference publication examines the integration of advanced technologies for attack detection in IoT-enabled 5G and beyond networks and was presented at the International Wireless Communications and Mobile Computing Conference (IWCMC 2025). Additional submitted manuscripts address forward-looking security visions for future networks and introduce novel federated edge frameworks for intrusion detection.[2]

  • Ghabriess, Z., Harb, H., Mansour, A., Yao, K. C., & Osswald, C. (2025). Attacks Detection in IoT-enabled 5G and Beyond Networks: Performance Evaluation of Integrating Cutting-Edge Technologies. IEEE IWCMC 2025.
  • IoT-Enabled 5G and Beyond Networks: A Forward Security Vision (Submitted Survey Paper).
  • FEDGE: A Federated Edge Framework for Attack Detection in IoT-Enabled 5G and Beyond Networks (Submitted Journal Paper).
  • SD-FEDGE: A Semi-Decentralized Federated Edge Framework for Attack Detection in IoT-Enabled 5G Networks (Ongoing Journal Paper).

Research Impact

The significance of Zahraa Ghabriess’s research lies in its practical relevance to next-generation communication networks and critical digital infrastructures. Her investigations into federated edge learning seek to overcome limitations associated with centralized security systems while supporting privacy preservation, scalability, and real-time threat detection. Such contributions are increasingly important as IoT deployments continue to expand across industrial, commercial, and public sectors. Her work also demonstrates the growing convergence of artificial intelligence and cybersecurity as complementary disciplines for addressing emerging security risks.[3]

Award Suitability

Zahraa Ghabriess demonstrates strong suitability for recognition within emerging researcher and cybersecurity innovation award categories. Her academic achievements include advanced graduate education, active doctoral research, conference dissemination, interdisciplinary collaboration, and the development of novel cybersecurity frameworks addressing contemporary technological challenges. The combination of theoretical contributions and practical implementation experience positions her research within areas of growing international importance, particularly those involving intelligent security systems, federated learning architectures, and future communication networks.[2]

Conclusion

Zahraa Ghabriess has established a promising academic profile characterized by research excellence in cybersecurity, artificial intelligence, and federated learning systems. Through doctoral investigations, collaborative research initiatives, and scientific dissemination activities, she contributes to the advancement of secure and intelligent digital infrastructures. Her work reflects contemporary priorities in cybersecurity research and demonstrates the potential for meaningful impact on the protection of future IoT-enabled communication environments.[1]

References

  1. Professional curriculum vitae and academic profile of Zahraa Ghabriess, including educational background, professional experience, technical competencies, certifications, and doctoral research activities.
  2. Ghabriess, Z., Harb, H., Mansour, A., Yao, K. C., & Osswald, C. (2025). Attacks Detection in IoT-enabled 5G and Beyond Networks: Performance Evaluation of Integrating Cutting-Edge Technologies. Proceedings of the International Wireless Communications and Mobile Computing Conference (IWCMC 2025).
    https://doi.org/10.1109/IWCMC62903.2025
  3. ENSTA Bretagne and Lab-STICC Research Activities. Doctoral research information relating to federated learning, intrusion detection systems, and cybersecurity applications for IoT-enabled communication infrastructures.
    https://www.ensta-bretagne.fr

Narayan Jee | Computer Science | Research Excellence Award

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Mr. Narayan Jee | Computer Science | Research Excellence Award

Haridwar University, Roorkee | India

Mr. Narayan Jee is an accomplished academician and researcher in the field of Computer Science and Engineering, currently serving as Assistant Professor and Deputy Head of Department at Haridwar University, Roorkee. With over 15 years of teaching experience and more than 6 years in academic administration, he specializes in Artificial Intelligence and Deep Learning. His research focuses on intelligent healthcare systems, particularly heart disease prediction using optimized swarm intelligence and ensemble learning techniques. He has authored 14 research publications, including SCI and Scopus-indexed journals and IEEE conferences, along with three published patents, demonstrating his strong research contributions. He has guided numerous postgraduate students and actively collaborates on interdisciplinary innovations. His work contributes to advancing AI-driven healthcare solutions, reflecting a commitment to societal impact, academic excellence, and the development of future-ready technological education.

Citation Metrics (Scopus)

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Featured Publications

S Kumar, KK Gola, N Jee, BM Singh (2024).
Optimized feature fusion-based modified cascaded kernel extreme learning machine for heart disease prediction in E-healthcare
Computer Methods in Biomechanics and Biomedical Engineering | Journal Article · 2024 · 📊 Citations: 8

B Gupta, KK Gola, N Jee, P Dimri (2022).
Energy-efficient routing protocol for congestion control in wireless sensor network
International Conference on Wireless Communications Signal Processing | Conference Paper · 2022 · 📊 Citations: 4

N Jee, S Kumar, RR Patel, R Mandal, RK Singh, H Vardhan (2024).
Advancements in Voice Assistants: A Study of Speech Recognition and Emotional Intelligence
International Conference on System Modeling & Advancement | Conference Paper · 2024 · 📊 Citations: 3

D Kamboj, KK Gola, S Ahmad, A Singh, N Jee (2023).
A Comparative Study of Time Series Models for Bitcoin Price Prediction
International Conference on Computing Communication and Networking Technologies | Conference Paper · 2023 · 📊 Citations: 3

KK Gola, S Kumar, T Jain, N Jee, S Kushwaha, N Jain (2023).
Odd even: A hybrid search technique based on bi-linear and jump search
AIP Conference Proceedings | Conference Paper · 2023 · 📊 Citations: 2

Ferdib Al Islam | Computer Science | Research Excellence Award

Published on by World Science Awards

Mr. Ferdib Al Islam | Computer Science | Research Excellence Award

Northern University of Business and Technology Khulna | Bangladesh

Ferdib-Al-Islam is an Assistant Professor of Computer Science and Engineering at Northern University of Business and Technology Khulna, Bangladesh. He holds an M.Sc. and B.Sc. in CSE and has extensive academic and industrial experience spanning software engineering, IoT, and applied artificial intelligence. His research expertise centers on machine learning, deep learning, explainable AI, large language models, computer vision, and multimodal fusion, with a strong emphasis on trustworthy and interpretable AI for healthcare, agriculture, and smart systems. He has authored 30+ peer-reviewed journal and conference publications, including articles in Springer, IEEE, ACM, and Scopus-indexed journals, and has received multiple best paper and gold awards. An active international collaborator and reviewer for leading journals, he contributes to societal impact through AI-driven healthcare diagnostics, smart farming, and assistive technologies.

Citation Metrics (Scopus)

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Featured Publications

Islam, Md. Rabiul; Godder, T. K.; Ul-Ambia, A.; Ferdib Al-Islam et al. (2025).
Ensemble model-based arrhythmia classification with local interpretable model-agnostic explanations. IAES International Journal of Artificial Intelligence (IJ-AI), Vol. 14, No. 3 • DOI: 10.11591/ijai.v14.i3.pp2012-2025

Akter, L.; Ferdib Al-Islam; Islam, Md. M.; Al-Rakhami, M. S.; Haque, Md. R. (2021).
Prediction of Cervical Cancer from Behavior Risk Using Machine Learning Techniques. SN Computer Science, Vol. 2, No. 3 • DOI: 10.1007/s42979-021-00551-6

Saha, P.; Sadi, M. S.; Aranya, O. F. M. R. R.; Jahan, S.; Al-Islam, F. (2021).
COV-VGX: An automated COVID-19 detection system using X-ray images and transfer learning. Informatics in Medicine Unlocked, Vol. 26 • DOI: 10.1016/j.imu.2021.100741

Rana, Md. M. R.; Adnan, Md. N.; Siddique, Md. M.; Rahman, Md. T.; Ferdib Al-Islam (2024).
Predicting Education Level of the Farmers’ Children of a Developing Country during COVID-19 Using Machine Learning. International Journal of Modern Education and Computer Science (IJMECS), Vol. 16, No. 6 • DOI: 10.5815/ijmecs.2024.06.07

Hossain, S. S.; F. Al-Islam; Islam, Md. R.; Rahman, S.; Parvej, Md. S. (2025).
Autism Spectrum Disorder Identification from Facial Images Using Fine-Tuned Pre-trained Deep Learning Models and Explainable AI Techniques. Semarak International Journal of Applied Psychology, Vol. 5, No. 1, pp. 29–53

Massudi Mahmuddin | Computer Science | Research Excellence Award

Published on by World Science Awards

Assoc. Prof. Dr. Massudi Mahmuddin | Computer Science | Research Excellence Award

Universiti Utara Malaysia | Malaysia 

Associate Professor Dr. Massudi Mahmuddin is a highly experienced academic and technology leader at Universiti Utara Malaysia (UUM), bringing more than two decades of contributions in teaching, research, administration, and student development. As an Associate Professor at the School of Computing, he has played a pivotal role in shaping academic programmes, strengthening industry collaborations, and nurturing future-ready graduates through innovative, student-centred learning approaches. His extensive leadership portfolio includes serving as Dean of Student Affairs, Director of Student Affairs, and Dean of Student Development and Alumni, where he led major initiatives in holistic student development, strategic planning, and university–community engagement. With a strong background in organizational management, project management, and computing systems, Dr. Massudi’s research focuses on intelligent computer systems, smart networking, artificial intelligence, blockchain technologies, big data analytics, and computer security. His scholarly impact is reflected through numerous indexed publications, international conference contributions, and interdisciplinary research collaborations. He is also actively engaged in academic quality enhancement, supervising postgraduate research, and developing technology-driven solutions aimed at social and educational transformation. Driven by a vision to improve human well-being through technology, Dr. Massudi continues to explore emerging digital innovations that enhance decision-making, cybersecurity, mental health monitoring, and community empowerment. His career reflects a balanced blend of academic excellence, administrative leadership, and a deep commitment to student success, making him a valuable contributor to Malaysia’s growing digital and educational ecosystem.

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View Scopus Profile

Featured Publications

Intellectual Property Blockchain
Conference Paper • Citations: 3

Pattern reconfigurable dielectric resonator antenna using capacitor loading for Internet of Things applications
International Journal of Electrical and Computer Engineering, 2023 • Citations: 4

 

S M Nahian Al Sunny | Computer Science | Editorial Board Member

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Dr. S M Nahian Al Sunny | Computer Science | Editorial Board Member

Walmart Global Tech | United States

Dr. S. M. Nahian Al Sunny is an accomplished researcher and software engineering professional with extensive experience in data-driven cloud applications, next-generation cyber-physical systems, and large-scale data engineering. With over two years of industry experience and five years of research-focused development, he combines academic rigor with practical innovation to address complex, real-world problems through scalable and intelligent technological solutions. He holds a Ph.D. in Computer Engineering from the University of Arkansas, USA, and a Bachelor’s degree in Electrical and Electronics Engineering from the Bangladesh University of Engineering and Technology. Dr. Sunny’s expertise spans Python, Java, R, advanced data engineering, distributed computing, big data analytics, machine learning pipelines, and cloud platforms including Google Cloud Platform, AWS, and Snowflake. His professional career at Walmart Global Tech involves architecting high-performance Spark/PySpark applications, forecasting systems, anomaly detection frameworks, and ETL pipelines that support decision-making at national scale. His contributions include developing predictive models with accuracies exceeding 90%, engineering multi-variate machine learning pipelines for more than 20,000 retail items, and designing cost-optimization strategies that produced substantial yearly savings in cloud resource utilization. In academia, Dr. Sunny pioneered research in cloud-based cyber-physical manufacturing systems, contributing to the development of MTComm—an Internet-scale communication method for remote machine tool interoperability. His work on IoT-integrated grocery delivery systems, smart edge hubs, and latency-optimized communication architectures demonstrates a strong commitment to advancing Industry 4.0 technologies. Dr. Sunny has published 12 peer-reviewed documents, including 3 journal articles and 9 conference papers, accumulating 487+ citations and an h-index of 8, reflecting the impact and visibility of his research. He has collaborated with interdisciplinary teams across cloud computing, manufacturing automation, robotics, and embedded systems, contributing to systems that hold both industrial and societal relevance. His ongoing work continues to bridge intelligent automation, data engineering, and cloud ecosystems to create future-ready technological solutions.

Profiles: Scopus | Google Scholar

Featured Publications

Hu, L., Nguyen, N. T., Tao, W., Leu, M. C., Liu, X. F., Shahriar, M. R., & Al Sunny, S. M. N. (2018). Modeling of cloud-based digital twins for smart manufacturing with MT Connect. Procedia Manufacturing, 26, 1193–1203.

Liu, X. F., Shahriar, M. R., Al Sunny, S. M. N., Leu, M. C., & Hu, L. (2017). Cyber-physical manufacturing cloud: Architecture, virtualization, communication, and testbed. Journal of Manufacturing Systems, 43, 352–364.

Shahriar, M. R., Al Sunny, S. M. N., Liu, X., Leu, M. C., Hu, L., & Nguyen, N. T. (2018). MTComm-based virtualization and integration of physical machine operations with digital twins in cyber-physical manufacturing cloud. In 2018 5th IEEE International Conference on Cyber Security and Cloud Computing.

Sunny, S. M. N. A., Liu, X. F., & Shahriar, M. R. (2018). Communication method for manufacturing services in a cyber–physical manufacturing cloud. International Journal of Computer Integrated Manufacturing, 31(7), 636–652.

Liu, X. F., Sunny, S. M. N. A., Shahriar, M. R., Leu, M. C., Cheng, M., & Hu, L. (2016). Implementation of MTConnect for open-source 3D printers in cyber physical manufacturing cloud. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference.

S M Nahian Al Sunny | Computer Science | Editorial Board Member

Published on by World Science Awards

Dr. S M Nahian Al Sunny | Computer Science | Editorial Board Member

Walmart Global Tech | United States

Dr. S. M. Nahian Al Sunny is an accomplished computer engineer and software professional whose work bridges advanced data-driven engineering, large-scale cloud systems, and next-generation cyber-physical infrastructures. He holds a Ph.D. in Computer Engineering from the University of Arkansas, USA, complemented by a Bachelor of Science in Electrical and Electronics Engineering from the Bangladesh University of Engineering and Technology (BUET). With over two years of industry expertise in software engineering and five years of research-focused development, Dr. Sunny has established himself as a leading contributor in scalable cloud application design, data engineering, and intelligent system optimization. Currently serving as a Software Engineer III at Walmart Global Tech, Dr. Sunny specializes in designing, developing, and optimizing Spark/PySpark applications for forecasting and anomaly detection across diverse, large-scale retail datasets. His contributions include building ETL pipelines in Google Cloud Platform (GCP), designing statistical and machine learning–based forecasting models, and architecting cost-optimization strategies that achieved significant yearly savings. He has also been instrumental in modernizing data workflows by migrating legacy systems to cloud-native architectures, thereby enhancing operational efficiency and scalability. During his doctoral research, Dr. Sunny pioneered the development of MTComm, an Internet-scale communication method for cyber-physical manufacturing. His research portfolio spans IoT-enabled smart systems, edge-based data optimization techniques, autonomous robotic delivery mechanisms, and FPGA-based smart edge hubs. Collectively, his innovations demonstrate measurable improvements in latency, data volume reduction, and remote system operability—significantly advancing the field of cloud-integrated cyber-physical systems. Dr. Sunny has authored 12 peer-reviewed publications, including three journal articles and nine conference papers, with over 500 citations, reflecting the impact and relevance of his contributions. He has collaborated with interdisciplinary research teams and industry partners, consistently translating complex technical concepts into practical, societally beneficial solutions. His work continues to influence the domains of cloud computing, data engineering, and intelligent manufacturing ecosystems on a global scale.

Profiles: Scopus | Google Scholar 

Featured Publications

 

Prof. Zhanwei Liu | Computer Science | Excellence in Innovation Award

Published on by World Science Awards

Prof. Zhanwei Liu | Computer Science | Excellence in Innovation Award

School of Information Science and Technology, China

Professor Zhanwei Liu is a highly accomplished scholar and master’s supervisor at Shijiazhuang Tiedao University, China, recognized for his pioneering work in intelligent optimization algorithms, computer vision, and cross-disciplinary engineering applications. He holds extensive academic and research experience in algorithmic design, system modeling, and real-world engineering integration. His educational and professional background reflects a deep commitment to advancing the convergence of artificial intelligence and complex systems, with a focus on improving computational efficiency, convergence precision, and robustness in metaheuristic algorithms. As a Professor at the School of Computer and Information Technology, he has played a pivotal role in developing and leading first-class undergraduate programs, mentoring graduate students, and fostering innovation-driven research. His research interests encompass swarm intelligence optimization, multi-UAV path planning, deep learning-based image enhancement, and intelligent system modeling for digital twin and smart infrastructure applications. With a strong command of algorithm development, AI-based modeling, data-driven optimization, and visual computing, Professor Liu has successfully contributed to several national and provincial-level projects, including digital twin platforms and structural health monitoring systems for major high-speed railway networks in China. His research excellence has been recognized through numerous awards and honors, including the Hebei Youth Science and Technology Innovation Award, First and Second Prizes for Scientific and Technological Progress, and Industry-University-Research Collaboration Innovation Award. He also holds more than 20 invention and utility model patents and has received 10 provincial-level industry awards, highlighting his strong innovation and practical problem-solving skills. In conclusion, Professor Zhanwei Liu exemplifies a dynamic blend of academic rigor, engineering innovation, and leadership, driving transformative advances in intelligent systems and digital technologies that contribute meaningfully to global scientific and industrial progress.

Profile: Scopus

Featured Publication

  1. Study of course system adjustment mechanism based on the employment needs. Conference Name.

Professor Zhanwei Liu’s work advances intelligent optimization algorithms and AI-driven engineering solutions, enabling more efficient, precise, and robust system designs. His contributions in multi-UAV path planning, computer vision, and digital twin platforms promote innovation in infrastructure, transportation, and industrial automation, benefiting science, industry, and society globally.