kafayat Tajudeen | Biometrics and Security | Innovative Research Award

Innovative Research Award

Kafayat Tajudeen
Al-Hikmah University, Nigeria

Kafayat Tajudeen
Affiliation Al-Hikmah University
Country Nigeria
Scopus ID 58092099200
Documents 5
Citations 10
h-index 2
Subject Area Biometrics and Security
Event Global Tech Excellence Awards
ORCID 0000-0002-9831-1741

Kafayat Tajudeen is a researcher affiliated with Al-Hikmah University whose scholarly activities focus on biometrics, cryptography, information security, and privacy-preserving technologies. Her published work contributes to the advancement of secure communication systems, encryption methodologies, and emerging security frameworks designed to address modern cybersecurity challenges. Through interdisciplinary research integrating biometric authentication and encryption techniques, she has contributed to discussions surrounding digital trust, data protection, and secure information exchange in contemporary computing environments.[1]

Abstract

Kafayat Tajudeen has developed a research profile centered on biometrics, encryption technologies, secure communication systems, and information assurance. Her publications investigate advanced encryption standards, multimedia security, explainable artificial intelligence applications, and residue number system methodologies for strengthening confidentiality and authentication processes. Through contributions addressing contemporary cybersecurity concerns, her work explores practical approaches to protecting digital assets while improving computational efficiency and trustworthiness. The available scholarly record indicates engagement with interdisciplinary security research that combines mathematical frameworks, biometric systems, and emerging computing technologies. These efforts support ongoing developments in secure digital infrastructures and privacy protection.[2]

Keywords

Advanced Encryption Standard, Biometrics, Cybersecurity, Multimedia Security, Explainable Artificial Intelligence, Residue Number Systems, Secure Communication, Authentication Systems, Information Assurance, Data Protection.

Introduction

Kafayat Tajudeen investigates security technologies designed to strengthen digital communication and information protection. Her research emphasizes encryption mechanisms, authentication strategies, and computational frameworks capable of addressing evolving cybersecurity risks. The resulting scholarship contributes to broader efforts aimed at enhancing confidentiality, integrity, and trust within modern information systems and networks.[2]

Research Profile

Kafayat Tajudeen maintains a focused publication portfolio in biometrics and security, supported by documented scholarly outputs indexed through recognized academic databases. Her profile demonstrates interest in encryption standards, secure multimedia processing, privacy enhancement technologies, and interdisciplinary computing applications relevant to contemporary digital security environments and infrastructure protection.[1]

Research Contributions

Kafayat Tajudeen has contributed to investigations of advanced encryption techniques and security architectures intended to improve secure data exchange. Her work evaluates approaches integrating explainable artificial intelligence, residue number systems, and cryptographic methods, offering perspectives on strengthening resilience, efficiency, and reliability within digital communication and authentication frameworks.[2][3]

Publications

Kafayat Tajudeen has authored and co-authored publications addressing advanced encryption standards, multimedia security enhancement, and explainable artificial intelligence applications for cybersecurity. Her documented works examine secure communication models and cryptographic optimization techniques, reflecting an ongoing commitment to addressing practical and theoretical challenges within information security research.[2][3]

Research Impact

Kafayat Tajudeen’s publications have attracted scholarly citations and contribute to discussions concerning cybersecurity, encryption performance, and secure digital ecosystems. Although developing in scale, the citation record indicates emerging academic recognition and demonstrates relevance to researchers exploring secure communication, authentication technologies, and information assurance methodologies globally.[4]

Award Suitability

Kafayat Tajudeen’s specialization in biometrics and security aligns closely with the objectives of the Global Tech Excellence Awards. Her research portfolio demonstrates engagement with technological innovation, encryption enhancement, and cybersecurity advancement, supporting consideration within recognition programs that acknowledge meaningful contributions to secure computing and digital transformation initiatives.[4]

Conclusion

Kafayat Tajudeen represents an emerging contributor within biometrics and cybersecurity research. Her publications highlight interests in encryption technologies, secure communications, and privacy protection. Collectively, her scholarly activities demonstrate alignment with contemporary information security priorities and support continued development of innovative solutions for safeguarding digital environments and infrastructures.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Kafayat Tajudeen, Author ID 58092099200. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58092099200
  2. ResearchGate. (n.d.). Kafayat Tajudeen researcher profile and publication overview.
    https://www.researchgate.net/profile/Kafayat-Tajudeen
  3. Google Scholar. (n.d.). Kafayat Tajudeen citation and publication profile.
    https://scholar.google.com/citations?user=FY2sNroAAAAJ&hl=en&oi=ao
  4. Global Tech Excellence Awards. (n.d.). Award program recognizing innovation and technological excellence.
    https://globaltechexcellence.com/
  5. ORCID. (n.d.). Researcher identifier profile: 0000-0002-9831-1741.
    https://orcid.org/0000-0002-9831-1741

Abdullah Alshammari | Surveillance and Security | Editorial Board Member

Assoc. Prof. Dr. Abdullah Alshammari | Surveillance and Security | Editorial Board Member

University of Hafr Albatin | Saudi Arabia

Assoc. Prof. Dr. Abdullah Alshammari is a researcher at University of Hafr Al-Batin specializing in artificial intelligence, cybersecurity, Internet of Things, and cloud computing. With 16 publications, 186 citations, and an h-index of 8, his work demonstrates consistent contributions to high-impact Q1 journals, including IEEE venues. His research integrates machine learning, blockchain security, and edge computing to address challenges in smart systems, energy efficiency, and digital infrastructure. Collaborating with over 60 international co-authors, he advances interdisciplinary innovation with practical societal impact in secure communication networks, intelligent decision-making systems, and sustainable smart technologies.

 

Citation Metrics (Scopus)

200

150

100

0

Citations
186

Documents
16

h-index
8

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile
           View ORCID Profile
        View Google Scholar Profile

Featured Publications


Intelligent multi-camera video surveillance system for smart city applications.

– In 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 317–323). (2019). Cited By : 47

Power system monitoring for electrical disturbances in wide network using machine learning.

-Sustainable Computing: Informatics and Systems. (2024). Cited By : 26

Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

Dr. Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

Chairperson of the Department of Computer Science and Information Technology | Jubail Industrial College (JIC) | Saudi Arabia

Dr. Faisal Alamri is an accomplished artificial intelligence researcher specializing in computer vision, machine learning, object detection, classification, segmentation, similarity search, adversarial perturbation, and zero-shot learning. He holds a Ph.D. in Computer Science with a focus on computer vision and machine learning from the University of Exeter, and completed his undergraduate and master’s degrees in computer systems engineering and networking. He currently serves as the Computer Science Department Chairperson at Jubail Industrial College, where he oversees academic and administrative activities and leads departmental initiatives. Previously, he worked as a machine learning engineer developing practical AI solutions, a postdoctoral research fellow, and a teaching assistant, and has also contributed as an online tutor and teaching volunteer. His research interests include developing innovative approaches for object detection, image analysis, and real-world AI applications. Dr. Alamri has been recognized for his achievements through multiple certifications and active participation in international conferences, workshops, and professional communities such as IEEE, Kaggle, NVIDIA, and MATLAB. He possesses strong technical skills in Python, MATLAB, C#, SPSS, AWS, Google Cloud ML Engine, and other platforms, and has completed various professional courses in deep learning, AI, cybersecurity, and digital analytics. His dedication to research, education, and community engagement reflects his commitment to advancing both science and society. He has a total of 49 citations, 7 documents, and an h-index of 5.

Profiles: Google Scholar | Scopus | ORCID | LinkedIn

Featured Publications

  1. Alamri, F., & Dutta, A. (2021). Multi-head self-attention via vision transformer for zero-shot learning. arXiv preprint arXiv:2108.00045.

  2. Alamri, F., & Pugeault, N. (2020). Improving object detection performance using scene contextual constraints. IEEE Transactions on Cognitive and Developmental Systems, 14(4), 1320–1330.

  3. Alamri, F., & Dutta, A. (2021). Implicit and explicit attention for zero-shot learning. In DAGM German Conference on Pattern Recognition (pp. 467–483).

  4. Alamri, F., & Dutta, A. (2023). Implicit and explicit attention mechanisms for zero-shot learning. Neurocomputing, 534, 55–66.

  5. Alamri, F., Kalkan, S., & Pugeault, N. (2021). Transformer-encoder detector module: Using context to improve robustness to adversarial attacks on object detection. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 9577–9584). IEEE.

Dr. Shao Cuiping | System Security | Best Researcher Award

Dr. Shao Cuiping | System Security | Best Researcher Award

Doctorate at Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China

👨‍🎓 Profiles

Orcid

Google Scholar

Education

  • Ph.D. in Computer Application Technology (University of Chinese Academy of Sciences, 2019)
  • M.Eng. in Microelectronics (Xi’an Institute of Microelectronics Technology, 2012)
  • B.Eng. in Electronic Science and Technology (Xi’an University of Technology, 2009)

🔬 Research Interests

  • System Security 
  • IC Testability and Reliability 
  • Heterogeneous Chip Optimization Design

🏆 Awards

  • High-Level Talent Recognition (Shenzhen City)
  • 2020 Guangdong Province Science and Technology Progress Award (2nd Prize) 
  • 2020 Shenzhen City Science and Technology Progress Award (1st Prize) 
  • 2019 Wu Wenjun Artificial Intelligence Science and Technology Award (3rd Prize) 
  • Six-Time “Excellent Employee” Award at SIAT
  • “Energetic Rose Award” for SIAT’s 15th Anniversary 
  • 7 Innovation Awards from SIAT

🛠️ Work Experience

  • Research Intern & Assistant Researcher (Shenzhen Institute of Advanced Technology, CAS)
  • Associate Researcher (2020-2024)

📋 Service & Influence

  • Project Reviewer (Guangdong Provincial Department of Science and Technology)
  • Expert Committee Member (Shenzhen Commercial Cryptography Industry Association)
  • Senior Member (China Computer Federation)
  • Member (Chinese Institute of Electronics)
  • Reviewer for IEEE Transactions on VLSI Systems, Circuits and Systems I, and Dependable and Secure Computing

 

Publications

Probabilistic Model-Based Reinforcement Learning Unmanned Surface Vehicles Using Local Update Sparse Spectrum Approximation

  • Authors: Yunduan Cui, Wenbo Shi, Huan Yang, Cuiping Shao, Lei Peng, Huiyun Li
  • Journal: IEEE Transactions on Industrial Informatics
  • Year: 2023

Anomaly recognition method of perception system for autonomous vehicles based on distance metric

  • Authors: Cuiping Shao, Beizhang Chen, Zujia Miao, Yunduan Cui, Huiyun Li
  • Journal: Electronics Letters
  • Year: 2022

Detection of security vulnerabilities in cryptographic ICs against fault injection attacks based on compressed sensing and basis pursuit

  • Authors: Cuiping Shao, Dongyan Zhao, Huiyun Li, Song Cheng, Shunxian Gao, Liuqing Yang
  • Journal: Journal of Cryptographic Engineering
  • Year: 2024

The Bitmap Decryption Model on Interleaved SRAM Using Multiple-Bit Upset Analysis

  • Authors: Jinlong Guo, Guangbo Mao, Wenjing Liu, Cuiping Shao, Ruqun Wu, Yaning Li, Jing Zhao, Cheng Shen, Hongjin Mou, Lei Zhang, Huiyun Li, Guanghua Du
  • Journal: IEEE Transactions on Nuclear Science
  • Year: 2022

Data redundancy mitigation in V2X based collective perceptions

  • Authors: Hui Huang, Huiyun Li, Cuiping Shao, Tianfu Sun, Wenqi Fang, Shaobo Dang
  • Journal: IEEE Access
  • Year: 2020