Tag: Future Computing Award

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

Nadeem Khanday | Computer Science | Best Researcher Award

Published on by World Science Awards

Assist. Prof. Dr. Nadeem Khanday | Computer Science | Best Researcher Award

Assistant Professor from National Institute of Technology Srinagar, India

Dr. Nadeem Yousuf Khanday is an accomplished academic and researcher in Computer Science & Engineering, currently serving as an Assistant Professor at the School of Computer Science, UPES, Dehradun, India. With a strong academic foundation and a passion for advanced computing technologies, he has contributed extensively to the fields of artificial intelligence, machine learning, and deep visual learning. His research outputs include high-impact journal publications, international conference presentations, patents, and book chapters with globally recognized publishers. Dr. Khanday is deeply involved in exploring innovative AI techniques that address real-world challenges, including healthcare diagnostics, crop disease detection, cloud computing, and smart environments. He is also a certified GATE, UGC-NET, and JK-SET qualifier, emphasizing his academic excellence. Throughout his career, he has taught a variety of technical subjects and mentored students in core areas of computer science. He brings a balanced combination of research, teaching, and applied innovation to the academic domain. With a growing body of interdisciplinary work, Dr. Khanday continues to build his reputation as a future-oriented researcher contributing to both academia and industry. His deep commitment to scholarly excellence and emerging technologies positions him as a deserving candidate for recognition in prestigious research awards.

Professional Profile

Education

Dr. Nadeem Yousuf Khanday has pursued a rigorous academic trajectory in Computer Science & Engineering. He earned his Doctor of Philosophy (Ph.D.) from the prestigious National Institute of Technology (NIT), Srinagar, focusing on advanced computing technologies and artificial intelligence. Prior to his doctorate, he completed his Master of Technology (M.Tech) from Vivekananda Global University, Jaipur, where he achieved an outstanding CGPA of 9.69 in Computer Science & Engineering, demonstrating his academic strength and subject mastery. His undergraduate studies were conducted at Visvesvaraya Technological University (VTU), Belgaum, where he obtained a Bachelor of Engineering (B.E.) degree in Computer Science & Engineering with a commendable academic record. Dr. Khanday has also qualified national-level competitive exams including the Graduate Aptitude Test in Engineering (GATE) and University Grants Commission National Eligibility Test (UGC-NET), as well as JK-SET, qualifying him for Assistant Professorship roles in Indian universities. These qualifications reflect his high-level proficiency in the domain and commitment to continued academic growth. His academic background provides a strong foundation for his research endeavors, enabling him to tackle complex computing problems and advance the frontier of knowledge in artificial intelligence, machine learning, and computer vision.

Professional Experience

Dr. Nadeem Yousuf Khanday possesses diverse and dynamic professional experience across some of India’s reputed institutions. He is currently employed as a Regular Assistant Professor at the School of Computer Science (SoCS), UPES Dehradun since June 2023. Before this, he served as a Lecturer at the University of Kashmir, J&K, where he taught undergraduate and postgraduate computer science courses from March to June 2023. His earlier appointments include his tenure as an Assistant Professor (Contract) at NIT Srinagar from April 2017 to July 2018, and later as a Teaching Assistant (Research Scholar) from July 2018 to February 2023 at the same institute. These roles have helped him accumulate extensive experience in teaching core computer science courses such as Artificial Intelligence, Operating Systems, Data Structures, and Computer Architecture. Throughout his career, Dr. Khanday has skillfully blended teaching with hands-on research, working on projects related to visual learning, deep learning, and intelligent systems. His progressive journey from contract roles to full-time professorship demonstrates his steady academic development and increasing responsibilities. With significant academic leadership and research roles, he is well-positioned to lead innovative educational and research initiatives in AI and computing.

Research Interests

Dr. Nadeem Yousuf Khanday’s research interests lie at the intersection of Artificial Intelligence, Machine Learning, and Computer Vision, with a particular focus on deep visual learning and few-shot learning models. He explores innovative solutions to computational challenges involving limited data samples, aiming to improve learning accuracy and cross-domain generalization. His research extends into practical domains such as healthcare diagnostics, agricultural disease prevention, cloud computing optimization, and smart IoT-based systems. Dr. Khanday has investigated topics including convolutional neural networks for COVID-19 prognosis, metric learning models for classification, and AI-driven smart farming using 5G networks. His recent work has integrated Large Language Models (LLMs) and Generative AI to enhance decision-making systems in medical and industrial contexts. His interdisciplinary approach combines theoretical models with real-world applications, contributing to sustainable development through intelligent computing. Dr. Khanday’s research aims not only to push academic boundaries but also to provide practical, scalable solutions for modern societal challenges. His continuous engagement with cutting-edge technologies and publication in top-tier journals solidify his status as a thought leader in visual intelligence and machine learning systems.

Research Skills

Dr. Nadeem Yousuf Khanday possesses a strong portfolio of research skills that span multiple domains in computing. He is proficient in developing machine learning algorithms, deep learning architectures, and advanced image processing models for varied applications. His expertise includes designing few-shot learning frameworks, enhancing cross-domain classification performance, and deploying convolutional neural networks for medical image analysis and smart diagnostics. He has hands-on experience with AI-based anomaly detection, visual segmentation systems, and cloud environment optimization using hybrid fuzzy and swarm intelligence methods. Dr. Khanday is also skilled in patent writing, having developed innovative systems for crop disease detection and motorcycle safety. His publication record reflects his ability to effectively communicate complex methodologies, backed by data-driven validation and practical implementation. Additionally, his collaboration in multi-author projects and book chapters indicates strong academic teamwork and interdisciplinary engagement. His teaching and research experiences across different institutions have also honed his ability to mentor students and lead academic discussions. Equipped with technical, analytical, and conceptual research skills, Dr. Khanday continues to contribute impactful and scalable innovations across emerging fields like generative AI, IoT systems, and smart computing.

Awards and Honors

Dr. Nadeem Yousuf Khanday has received various forms of recognition for his scholarly achievements and research excellence. Notably, he has qualified multiple national-level eligibility exams, such as GATE, UGC-NET, and JK-SET, highlighting his academic distinction and competency to teach at the university level. In 2023, he was awarded recognition for his impactful contributions to AI-driven visual understanding and applications, as reflected in his high-impact publications and patents. His patent work, including an apparatus for auto-detection of crop diseases and motorcycle safety systems, has been acknowledged for its potential technological and societal value. Dr. Khanday’s research has also gained visibility through SCOPUS- and SCI-indexed publications with top journals like Computer Science Review and Neural Computing and Applications. His invited book chapters published by Taylor and Francis, Springer Nature, and Cambridge University Press underline his reputation among international academic publishers. Furthermore, he has presented at international conferences in Europe and Asia, receiving acclaim for his work on machine vision, fuzzy systems, and cloud intelligence. These accolades reflect both his individual excellence and collaborative impact within the research community.

Conclusion

Dr. Nadeem Yousuf Khanday exemplifies the profile of a high-caliber academician and innovative researcher with notable achievements in the fields of artificial intelligence, deep learning, and computer vision. Through a strong foundation in computer science education and a wealth of research experience, he has consistently contributed to advancing both theory and practice. His multidisciplinary research in healthcare, smart agriculture, and intelligent systems, along with a growing list of high-impact publications, patents, and book contributions, sets him apart as a forward-thinking scholar. His teaching experience across reputed Indian institutions and his ability to combine pedagogy with practical applications further enhance his value to academia. Dr. Khanday’s commitment to solving real-world problems using machine learning and AI tools not only enhances academic discourse but also promotes sustainable innovation. His emerging collaborations, international conference participation, and national recognitions affirm his credibility and future potential. In light of his qualifications, scholarly output, and research relevance, he stands as a highly deserving candidate for the Best Researcher Award, with the capacity to influence the global research community and contribute significantly to technological advancement

  1. Covariance-based Metric Model for Cross-domain Few-shot Classification and Learning-to-generalization
    📘 Journal: Applied Intelligence, 2023
    👥 Authors: Khanday, N.Y.; Sofi, S.A.

  2. Learned Gaussian ProtoNet for Improved Cross-domain Few-shot Classification and Generalization
    📘 Journal: Neural Computing and Applications, 2023
    👥 Authors: Khanday, N.Y.; Sofi, S.A.

  3. Deep Insight: Convolutional Neural Network and Its Applications for COVID-19 Prognosis
    📘 Journal: Biomedical Signal Processing and Control, 2021
    👥 Authors: Khanday, N.Y.; Sofi, S.A.

  4. Taxonomy, State-of-the-art, Challenges and Applications of Visual Understanding: A Review
    📘 Journal: Computer Science Review, 2021
    👥 Authors: Khanday, N.Y.; Sofi, S.A.

Zhengming Jiang | Computer Science | Best Academic Researcher Award

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Dr. Zhengming Jiang | Computer Science | Best Academic Researcher Award

Teacher from Guangdong Polytechnic Normal University, China

Dr. Zhengming Jiang is a dedicated academic and researcher in the field of information and communication engineering, currently serving as a lecturer at the Industrial Training Center, Guangdong Polytechnic Normal University. With over a decade of experience in academia and research, he has established himself as a promising expert in dual-function radar-communication systems, intelligent reflecting surfaces, and physical layer security. Dr. Jiang obtained his Ph.D. and Master’s degrees from Shenzhen University, which shaped his foundational expertise in advanced communication technologies. In recent years, he has gained further prominence through his postdoctoral work at the Shenzhen International Graduate School of Tsinghua University, in collaboration with Guangdong Kekaida Intelligent Robotics Co., Ltd. His scholarly output includes several first-author papers in top-tier journals such as the IEEE Internet of Things Journal and Digital Signal Processing. He has also been awarded and has led numerous national and provincial research grants, showcasing his capacity for high-impact research. Furthermore, Dr. Jiang has made significant contributions to the applied technology sector, as evidenced by his patented innovations in signal processing and communication security. His academic rigor, project leadership, and technical acumen place him among the leading emerging scholars in the field.

Professional Profile

Education

Dr. Zhengming Jiang has pursued a comprehensive academic path in communication engineering, demonstrating consistent dedication to excellence throughout his educational journey. He began his undergraduate studies in 2007 at Shaoyang University, where he obtained a Bachelor’s degree in Communication Engineering in 2011. His strong academic performance and interest in signal systems led him to Shenzhen University, where he pursued a Master’s degree in Communication and Information Systems, graduating in 2014. Motivated by a desire to delve deeper into advanced research, he continued at Shenzhen University to earn his Ph.D. in Information and Communication Engineering from 2017 to 2020. His doctoral research was instrumental in shaping his specialization in dual-function radar-communication systems and signal processing techniques. His academic foundation not only reflects deep theoretical knowledge but also practical application, which he further developed during his postdoctoral fellowship beginning in 2023 at the Tsinghua Shenzhen International Graduate School and Guangdong Kekaida Intelligent Robotics Co., Ltd. This postdoctoral phase has allowed him to engage in high-level collaborative research and contribute to national-level innovations. Dr. Jiang’s educational background showcases a well-rounded and in-depth mastery of his field, equipping him with the skills necessary for high-impact research and academic leadership.

Professional Experience

Dr. Zhengming Jiang has a diverse portfolio of academic and research experience, reflecting his progression from foundational teaching roles to high-level research leadership. His professional journey began in 2014 when he served as a teaching assistant in the Computer Science Department at Guangdong University of Science and Technology. Over the course of three years, he contributed to curriculum delivery and engaged students in the fundamentals of communication systems. In January 2021, he advanced to a lecturer position at the Industrial Training Center of Guangdong Polytechnic Normal University, where he continues to teach and mentor students while spearheading innovative research projects. His academic expertise is matched by his research initiative; he has successfully secured and managed several university- and province-level research grants as principal investigator. Most recently, in November 2023, Dr. Jiang began a prestigious postdoctoral appointment at the Shenzhen International Graduate School of Tsinghua University in conjunction with Guangdong Kekaida Intelligent Robotics Co., Ltd. This dual-institution position has enabled him to engage in interdisciplinary collaboration and contribute to cutting-edge research in intelligent communication technologies. His professional trajectory reflects a balance between teaching, project leadership, and research excellence, positioning him as a versatile academic and technical expert.

Research Interests

Dr. Zhengming Jiang’s research interests are deeply rooted in the development and integration of communication and radar technologies. He focuses on dual-function radar-communication systems, an emerging field that enhances spectrum efficiency by combining communication and sensing capabilities. His work explores the use of intelligent reflecting surfaces (IRS) to optimize signal propagation, minimize interference, and improve system robustness in dynamic environments. Dr. Jiang is particularly interested in the physical layer security of vehicular and automotive systems, addressing the growing need for secure communication in intelligent transportation infrastructure. Additionally, his research covers robust beamforming design, especially in complex or error-prone environments, which is vital for ensuring reliable wireless performance. Another important theme in his work is the integration of communication and sensing in millimeter-wave systems, contributing to the advancement of next-generation wireless networks. His interests align with current global trends in smart mobility, 6G technologies, and secure wireless communication. Dr. Jiang’s research not only addresses theoretical challenges but also emphasizes practical implementation and innovation, which is evident in his industry collaboration and patent filings. His ability to identify emerging needs and apply theoretical insights to real-world applications makes his research highly impactful and forward-thinking.

Research Skills

Dr. Zhengming Jiang possesses a comprehensive and advanced skill set in information and communication engineering, with specialized expertise in dual-function radar-communication systems and intelligent signal processing. He has demonstrated exceptional capability in the design and optimization of intelligent reflecting surfaces (IRS), focusing on enhancing wireless signal strength and quality in complex environments. His proficiency in robust beamforming techniques enables him to manage signal integrity in the presence of interference and modeling errors, a critical skill in modern communication systems. Dr. Jiang is also well-versed in millimeter-wave communications, particularly in scenarios requiring high-frequency data transmission and sensing integration. His ability to simulate and model physical layer systems is complemented by strong coding and algorithm development skills, particularly using MATLAB and Python. Furthermore, he is adept at experimental validation and hardware-software interfacing, which are essential for real-world implementation. His capacity to lead multidisciplinary research projects and collaborate with engineering teams has been proven through his work at Guangdong Kekaida and Tsinghua Shenzhen. Whether it is theoretical modeling, algorithm design, or system prototyping, Dr. Jiang exhibits a rare blend of analytical precision and engineering pragmatism, making him a valuable asset in both academic and applied research environments.

Awards and Honors

Dr. Zhengming Jiang has received recognition for his outstanding contributions to the field of communication systems through both academic and innovation-related achievements. Among his notable accomplishments is his role as the principal investigator and key contributor in several research projects funded by prestigious institutions such as the National Natural Science Foundation of China (NSFC). One of his ongoing research contributions includes participation in a significant NSFC general project valued at 550,000 RMB, focused on the modulation mechanisms of AlGaN-based ultraviolet communication devices. He has also led and completed provincial and university-level research grants totaling over 400,000 RMB, demonstrating his ability to conceptualize and execute high-impact studies. His publication record includes multiple first-author papers in high-impact journals such as IEEE Internet of Things Journal and IEEE Systems Journal, underscoring his leadership in scholarly dissemination. In addition, Dr. Jiang is a named inventor in a Chinese patent related to robust anti-interference navigation technologies, further showcasing his innovative contributions. These accomplishments reflect a strong recognition from both academic peers and industry stakeholders. While formal academic honors or teaching awards were not listed, his record of funded research, scholarly output, and intellectual property firmly positions him as a decorated and respected figure in his field.

Conclusion

In conclusion, Dr. Zhengming Jiang stands out as an accomplished early-career researcher with a clear trajectory toward long-term excellence in the fields of communication and radar systems. His educational and professional journey reflects a strong foundation, advanced specialization, and continuous development through prestigious appointments and collaborations. He has consistently demonstrated leadership in research, as evidenced by his role in major grant-funded projects, first-author publications in internationally reputed journals, and innovation in practical technologies such as anti-interference navigation systems. Dr. Jiang’s work directly aligns with pressing global needs, including secure and intelligent vehicular communications, spectrum efficiency, and next-generation wireless networks. His research is not only theoretically robust but also geared toward real-world applications, making a meaningful contribution to both academia and industry. With further expansion into global collaborations, mentorship roles, and interdisciplinary projects, Dr. Jiang is well-poised to reach even higher levels of academic recognition. Based on his current record, he is a strong candidate for awards recognizing research excellence and innovation. His blend of technical expertise, academic productivity, and real-world impact positions him as a leading researcher worthy of distinction.

Publications Top Notes

1. On the Physical Layer Security of Automotive Dual-Function Radar-Communication Systems

Authors: Zheng-Ming Jiang, Mohamed Rihan, Qijun Deng, Ehab Mahmoud Mohamed, Haitham S. Khallaf, Xiaojun Wang, Rasha Omar
Journal: IEEE Internet of Things Journal
Year: 2024
Volume/Issue: 11(3), Pages 5090–5104

2. Intelligent Reflecting Surface Aided Dual-Function Radar and Communication System

Authors: Zheng-Ming Jiang, Mohamed Rihan, Qiang Li, Lei Huang, Qijun Deng, Jihong Zhang, Ehab Mahmoud Mohamed
Journal: IEEE Systems Journal
Year: 2021
Volume/Issue: 16(1), Pages 475–486
Citations: 113 citations

3. Intelligent Reflecting Surface Aided Co-existing Radar and Communication System

Authors: Zheng-Ming Jiang, Qijun Deng, Min Huang, Zhenxing Zheng, Xiaojun Wang, Mohamed Rihan
Journal: Digital Signal Processing
Year: 2023
Volume: 141

4. Robust Beamforming Design for Co-existing Radar and Communication System with Bounded Error Model

Authors: Qijun Deng, Zhengming Jiang, Senming Zhong, Min Huang, Qiang Li, Mohamed Rihan
Journal: IEICE Transactions on Communications
Year: 2024
Volume/Issue: 12(3)

5. Maximum Likelihood Approach to DoA Estimation Using Lens Antenna Array

Authors: Zheng-Ming Jiang, Peichang Zhang, Mohamed Rihan, Lei Huang, Jihong Zhang
Journal: EURASIP Journal on Wireless Communications and Networking
Year: 2019
Volume: 2019: Article 1

Alireza Akoushideh | Computer Science | Best Researcher Award

Published on by World Science Awards

Assist. Prof. Dr. Alireza Akoushideh | Computer Science | Best Researcher Award

Electrical and Computer Department from Iran’s National University of Skill, Iran

Dr. Alireza Akoushideh is an Assistant Professor in Electronics Engineering with a specialization in image processing, parallel processing, and microcontroller-based systems. With over two decades of experience in academia and research, he has made significant contributions to digital electronics, focusing on industrial applications. His expertise extends to supervising research projects, authoring academic books, and securing multiple patents. Dr. Akoushideh has been an active participant in national and international collaborations, including a visiting research position at the University of Twente in the Netherlands and participation in the Erasmus+ program in Romania. In addition to his academic contributions, he has played a vital role in fostering technological innovations as the former manager of the Growth Centre at Guilan Science and Technology Park. His work emphasizes bridging the gap between academia and industry, particularly in the development of applied research projects and commercialization of new technologies. Recognized for his research excellence, he has received multiple awards, including the Best Researcher title at Guilan Technical and Vocational University. With a strong background in electronics and computer engineering, Dr. Akoushideh continues to contribute to advancements in artificial intelligence, IoT, and digital systems, making him a distinguished researcher in his field.

Professional Profile

Education

Dr. Akoushideh has a strong academic foundation in electrical and electronics engineering. He earned his Ph.D. in Electrical Engineering with a specialization in Electronics from Shahid Beheshti University, where his research focused on developing noise-resistant feature extraction operators for texture classification. His doctoral work contributed significantly to the fields of image processing and pattern recognition. Prior to that, he completed his Master’s degree at Amirkabir University of Technology (Tehran Polytechnic), specializing in electronics. His master’s thesis revolved around designing a pacemaker system based on the detection of cardiac arrests, demonstrating his early interest in biomedical applications of electronics. Dr. Akoushideh obtained his Bachelor’s degree from the University of Guilan, where he specialized in electronics engineering. His undergraduate research involved the development of a computer-based microcontroller trainer, highlighting his inclination towards microcontroller-based system design. Throughout his academic journey, he has consistently focused on applying electronics engineering principles to real-world challenges, which is evident in his later research projects and technological innovations. His education, spanning three prestigious Iranian institutions, has provided him with the necessary expertise to excel in both theoretical and applied aspects of electronics, further enriching his contributions to academia, research, and industry.

Professional Experience

Dr. Akoushideh has had an extensive career in academia, research, and industry. He is currently an Assistant Professor at the Technical and Vocational University in Iran, where he teaches courses in image processing, computer architecture, microcontrollers, and digital systems. His role extends beyond teaching, as he actively supervises undergraduate and graduate research projects, guiding students in developing innovative solutions for industrial and technological challenges. He has also served as a visiting researcher at the University of Twente in the Netherlands, where he collaborated on biometrics and pattern recognition research. Additionally, he participated in the Erasmus+ program at Pitesti University in Romania, contributing to international discussions on vocational education and training. Dr. Akoushideh has held managerial roles, including serving as the Growth Centre Manager at Guilan Science and Technology Park, where he played a key role in supporting technology startups and commercializing academic research. His industry experience includes co-founding Rayaneh Gostar Moein Co., where he worked on network design, industrial automation, and electronic content production. His diverse professional background reflects his ability to integrate academic research with industrial applications, making significant contributions to both education and technology-driven initiatives.

Research Interests

Dr. Akoushideh’s research interests lie in the intersection of digital electronics, image processing, artificial intelligence, and microcontroller-based systems. His work primarily focuses on developing advanced image processing techniques for applications such as biometrics, video surveillance, and medical diagnostics. He has also contributed to research in pattern recognition, deep learning, and IoT-based automation systems. His interest in parallel processing has led him to explore hardware acceleration techniques for computationally intensive tasks, improving the efficiency of embedded systems. In addition to theoretical advancements, Dr. Akoushideh is deeply involved in applied research, particularly in developing smart electronic devices and automation systems for industrial and consumer applications. His projects include intelligent power management systems, real-time video analytics, and embedded system design for IoT applications. He is also keen on integrating artificial intelligence into embedded systems, exploring new methods for enhancing efficiency and performance in real-time processing environments. With a strong background in both academic and practical research, his work contributes to the advancement of smart technologies, automation, and digital signal processing, positioning him as a leading researcher in electronics and computer engineering.

Research Skills

Dr. Akoushideh possesses a diverse range of research skills spanning hardware and software domains. He has expertise in digital image processing, machine learning, and deep learning techniques, applying them to areas such as biometrics, video analysis, and industrial automation. His programming proficiency includes MATLAB, Python, C++, and hardware description languages like VHDL, allowing him to develop and implement complex algorithms for embedded systems. His hands-on experience with microcontrollers such as AVR, ARM, and PIC enables him to design and prototype advanced electronic devices. Additionally, he is skilled in PCB design using Altium Designer and FPGA-based system development using Xilinx ISE and Synopsys tools. His research capabilities extend to IoT and smart systems, where he has worked on projects involving sensor networks, remote monitoring, and intelligent control systems. Dr. Akoushideh is also experienced in conducting experimental research, statistical data analysis, and scientific writing, which are essential for publishing in high-impact journals. His interdisciplinary approach, combining hardware and software expertise, makes him highly proficient in designing, developing, and optimizing electronic and computational systems for various applications.

Awards and Honors

Dr. Akoushideh has been recognized multiple times for his contributions to research and technology. He was awarded the Best Researcher title at Guilan Technical and Vocational University in 2022 and previously in 2018 and 2019. In 2021, he received the first award at the Technical and Vocational University of Iran, a national-level recognition of his excellence in research and academia. He was also acknowledged by the Guilan Science and Technology Park for his contributions as an innovator and technologist, winning awards such as “Encouraging Thinkers, Technologists, and Innovators” in 2019. Additionally, he won a provincial award in the Young Idea Supporters category the same year. His entrepreneurial spirit was recognized in 2007 when he was named the Best Entrepreneur in Information Technology by the Ministry of Labor and Social Affairs. His academic achievements include ranking second in his graduating class in electronic engineering at Guilan University in 1997. These awards highlight his dedication to advancing research, education, and innovation, further solidifying his reputation as a leading researcher in his field.

Conclusion

Dr. Alireza Akoushideh is a distinguished researcher with extensive expertise in electronics engineering, particularly in image processing, embedded systems, and IoT applications. His academic journey, spanning Iran’s top universities, has provided him with a strong foundation in both theoretical and applied research. His professional experience as a university professor, visiting researcher, and technology leader has allowed him to make significant contributions to academia and industry. With numerous research projects, patents, and international collaborations, he has established himself as a key figure in his field. His research interests in artificial intelligence, parallel processing, and industrial automation align with current technological advancements, making his work highly relevant. His technical skills in programming, hardware design, and system optimization further enhance his ability to develop innovative solutions. Recognized with multiple awards for research excellence, teaching, and entrepreneurship, he has consistently demonstrated his commitment to knowledge creation and dissemination. Dr. Akoushideh’s career reflects a balance between academic research and practical applications, positioning him as a thought leader in digital electronics and embedded systems. His contributions continue to drive technological innovation, benefiting both academia and industry.

Publications Top Notes

  • Title: Motion-based vehicle speed measurement for intelligent transportation systems
    Authors: A. Tourani, A. Shahbahrami, A. Akoushideh, S. Khazaee, C.Y. Suen
    Year: 2019
    Citations: 33

  • Title: A robust vehicle detection approach based on faster R-CNN algorithm
    Authors: A. Tourani, S. Soroori, A. Shahbahrami, S. Khazaee, A. Akoushideh
    Year: 2019
    Citations: 25

  • Title: Facial expression recognition using a combination of enhanced local binary pattern and pyramid histogram of oriented gradients features extraction
    Authors: M. Sharifnejad, A. Shahbahrami, A. Akoushideh, R.Z. Hassanpour
    Year: 2020
    Citations: 19

  • Title: Iranis: A large-scale dataset of Iranian vehicles license plate characters
    Authors: A. Tourani, S. Soroori, A. Shahbahrami, A. Akoushideh
    Year: 2021
    Citations: 16

  • Title: Iranian license plate recognition using deep learning
    Authors: A.R. Rashtehroudi, A. Shahbahrami, A. Akoushideh
    Year: 2020
    Citations: 15

  • Title: High performance implementation of texture features extraction algorithms using FPGA architecture
    Authors: A.R. Akoushideh, A. Shahbahrami, B.M.N. Maybodi
    Year: 2014
    Citations: 13

  • Title: Copy-move forgery detection using convolutional neural network and K-mean clustering
    Authors: A. Pourkashani, A. Shahbahrami, A. Akoushideh
    Year: 2021
    Citations: 12

  • Title: Accelerating texture features extraction algorithms using FPGA architecture
    Authors: A.R. Akoushideh, A. Shahbahrami
    Year: 2010
    Citations: 12

  • Title: Parallel Implementation of a Video-based Vehicle Speed Measurement System for Municipal Roadways
    Authors: A.J. Afshany, A. Tourani, A. Shahbahrami, S. Khazaee, A. Akoushideh
    Year: 2019
    Citations: 10

  • Title: Challenges of Video-Based Vehicle Detection and Tracking in Intelligent Transportation Systems
    Authors: A. Tourani, A. Shahbahrami, A. Akoushideh
    Year: 2017
    Citations: 9

 

Qichuan Tian | Computer Science | Best Researcher Award

Published on by World Science Awards

Prof. Dr. Qichuan Tian | Computer Science | Best Researcher Award

Prof. Dr. at  Beijing University of Civil Engineering and Architecture, China

Qichuan Tian, born in 1971, is a distinguished professor and technical expert specializing in artificial intelligence, pattern recognition, and computer vision. He holds a Ph.D. in Engineering from Northwestern Polytechnical University (2006) and currently serves as a professor and master’s supervisor at Beijing University of Civil Engineering and Architecture (BUCEA). As the Director of the Department of Artificial Intelligence at the School of Intelligent Science and Technology, he leads research in biometrics, human-computer interaction, and deep learning. He is a member of multiple prestigious organizations, including the National Information Technology Standardization Technical Committee and the Chinese Society of Biomedical Engineering. His career spans academia and industry, with significant contributions in developing national standards, publishing books, and mentoring graduate students. Tian has also played a key role in over 20 research projects funded by national and provincial foundations, solidifying his reputation as a thought leader in AI and computational sciences.

Professional Profile

Education

Qichuan Tian has an extensive academic background in engineering. He obtained his Bachelor of Engineering (1993) and Master of Engineering (1996) from Taiyuan University of Science and Technology. In 2006, he completed his Doctor of Engineering at Northwestern Polytechnical University, specializing in artificial intelligence and computer vision. His academic training laid a strong foundation for his later contributions to AI, biometrics, and deep learning. His studies focused on integrating computational intelligence into practical applications, a theme that continues to define his research and professional endeavors.

Professional Experience

Tian has a diverse career in academia and research. Since 2012, he has served as the Head of the Department of Artificial Intelligence at BUCEA, where he spearheads innovative AI programs. From 2009 to 2010, he was a Visiting Scholar at Auburn University, USA, gaining international exposure in computer science. Between 2006 and 2008, he conducted postdoctoral research at Tianjin University. Previously, he held various roles at Taiyuan University of Science and Technology (1993–2012), where he advanced from Assistant Professor to Associate Professor and later became the Chief Leader of Circuits and Systems. His leadership has been instrumental in shaping AI research and education in China.

Research Interests

Tian’s research interests focus on artificial intelligence, pattern recognition, image processing, and deep learning. He specializes in biometric recognition, computer vision, and human-computer natural interaction. His work extends to security authentication, big data analysis, and IoT-based embedded systems. Tian has published over 100 journal and conference papers, authored six books, and contributed significantly to national standards in AI applications. His interdisciplinary research bridges theoretical advancements with practical AI implementations, making substantial contributions to the field.

Research Skills

With expertise in artificial intelligence and computer vision, Tian possesses strong research skills in deep learning algorithms, biometric recognition systems, and real-time image processing. He has successfully led projects in autonomous driving, green building AI integration, and complex object detection. His experience includes handling large-scale datasets, implementing machine learning frameworks, and designing AI-driven applications. Additionally, he has obtained over 50 invention patents and software copyrights, showcasing his ability to translate theoretical research into impactful technological innovations.

Awards and Honors

Tian’s contributions to academia and AI research have earned him multiple accolades. In 2024, he was recognized among CNKI’s Highly Cited Scholars (Top 5). He received the First Prize for Teaching Achievements at BUCEA in 2021 and was honored for developing a National First-Class Blended Online and Offline Course in 2020. Additionally, he was awarded the Outstanding Master’s Thesis Advisor Award in 2012. His accolades highlight his commitment to education, research, and AI-driven innovations, reinforcing his influence in the field of intelligent science and technology.

Conclusion

Qichuan Tian is a prominent scholar and AI expert dedicated to advancing artificial intelligence and biometric research. His leadership in academia, combined with his extensive research portfolio, underscores his impact on technological advancements in pattern recognition, computer vision, and human-computer interaction. With a career spanning over two decades, Tian has played a pivotal role in shaping AI education, national standards, and industry collaborations. His legacy continues to influence emerging AI technologies and inspire the next generation of researchers in intelligent computing.

Publications Top Notes

  • Title: An improved framework for breast ultrasound image segmentation with multiple branches depth perception and layer compression residual module

    • Authors: K. Cui, Qichuan Tian, Haoji Wang, Chuan Ma
    • Year: 2025

  • Title: Mobile Robot Path Planning Algorithm Based on NSGA-II

    • Authors: Sitong Liu, Qichuan Tian, Chaolin Tang
    • Year: 2024
    • Citations: 1

  • Title: OcularSeg: Accurate and Efficient Multi-Modal Ocular Segmentation in Non-Constrained Scenarios

    • Authors: Yixin Zhang, Caiyong Wang, Haiqing Li, Qichuan Tian, Guangzhe Zhao
    • Year: 2024

  • Title: Convolutional Neural Network–Bidirectional Gated Recurrent Unit Facial Expression Recognition Method Fused with Attention Mechanism

    • Authors: Chaolin Tang, Dong Zhang, Qichuan Tian
    • Year: 2023
    • Citations: 4