Shamsuddeen Umaru Adamu | Cybersecurity | Best Researcher Award

Published on by Global CSE Awards

Best Researcher Award

Shamsuddeen Umaru Adamu — Kaduna State University

Shamsuddeen Umaru Adamu
Affiliation Kaduna State University
Country Nigeria
Scopus ID 59104600400
Documents 2
Citations 6
h-index 1
Subject Area Cybersecurity
Event Global CSE Awards
ORCID 0000-0001-8227-220X

Shamsuddeen Umaru Adamu is a researcher affiliated with Kaduna State University, Nigeria, whose documented scholarly work is associated with cybersecurity and related computational research. His research record includes studies addressing cybersecurity frameworks in higher education and natural language processing involving Hausa-language data, reflecting interdisciplinary engagement with security, technology, and African language resources. [1] [2]

Abstract

Shamsuddeen Umaru Adamu is a cybersecurity researcher affiliated with Kaduna State University, Nigeria. His documented research interests include cybersecurity frameworks in higher education and computational language technologies involving Hausa, demonstrating an intersection of information security, artificial intelligence, and African language resources. His publication record includes work examining the adoption and effectiveness of cybersecurity frameworks within higher education institutions, together with contributions to sentiment analysis using low-resource Hausa tweet data. These studies address practical and methodological challenges relevant to secure digital environments and inclusive language technologies. His Scopus record reports two documents, six citations, and an h-index of one, providing measurable evidence of scholarly activity.

Keywords

Cybersecurity, Cybersecurity Frameworks, Higher Education Security, Information Security, Hausa Natural Language Processing, Sentiment Analysis, Low-Resource Languages, African Language Technology, Computational Research, Cybersecurity Research. [1] [2]

Introduction

Cybersecurity research in higher education addresses institutional protection, governance, risk management, and effective security practices across increasingly digital academic environments. Adamu’s documented work examines cybersecurity framework adoption and effectiveness, while related research explores Hausa-language sentiment analysis using low-resource social-media data. These topics demonstrate engagement with contemporary computational challenges. [1] [2]

Research Profile

Adamu’s research profile is centered on cybersecurity, with documented scholarly activity extending into natural language processing and sentiment analysis. His work connects information security concerns with computational methods and African language resources, indicating an interdisciplinary research orientation. The available publication record provides evidence of research activity across security and language-technology themes. [1] [2]

Research Contributions

The documented contributions include examination of cybersecurity framework adoption and effectiveness in higher education institutions, alongside participation in research addressing sentiment analysis for Hausa, a relatively low-resource African language. Together, these works contribute to discussions of institutional cybersecurity and computational approaches for improving language-data analysis in underrepresented linguistic contexts. [1] [2] [3]

Publications

The available publication record includes a systematic literature review concerning cybersecurity framework adoption in higher education and two Scopus-indexed records associated with HausaNLP and SemEval-2023 Task 12. These publications reflect research activity spanning cybersecurity, natural language processing, sentiment analysis, and low-resource African language technologies, with the cited records providing bibliographic verification. [1] [2] [3]

Research Impact

The available Scopus indicators report two documents, six citations, and an h-index of one, providing a quantitative snapshot of Adamu’s indexed research activity. His work on cybersecurity frameworks is relevant to institutional security discussions, while HausaNLP research supports broader efforts to develop computational resources and analytical methods for African low-resource languages. [1] [2]

Award Suitability

Adamu’s documented research activity aligns with the Best Researcher Award through its focus on cybersecurity and related computational research. His publications address applied security questions and low-resource language technology, demonstrating scholarly engagement across complementary areas of computer science. The available bibliographic record and citation indicators provide supporting evidence for recognition consideration. [1] [2] [3]

Conclusion

Shamsuddeen Umaru Adamu’s documented scholarly profile reflects research activity in cybersecurity and computational language technologies. His work addresses cybersecurity frameworks in higher education and sentiment analysis for Hausa-language data, connecting practical security concerns with emerging computational research. The available Scopus indicators and indexed publications provide a basis for academic recognition. [1] [2]

References

  1. A systematic literature review on the adoption and effectiveness of cybersecurity frameworks in higher education institutions. (n.d.). Scopus.
    https://www.scopus.com/inward/record.url?eid=2-s2.0-105029652508&partnerID=MN8TOARS
  2. HausaNLP at SemEval-2023 Task 12: Leveraging African Low Resource TweetData for Sentiment Analysis. (n.d.). Scopus.
    https://www.scopus.com/inward/record.url?eid=2-s2.0-85159598145&partnerID=MN8TOARS
  3. HausaNLP at SemEval-2023 Task 12: Leveraging African Low Resource TweetData for Sentiment Analysis. (n.d.). Scopus.
    https://www.scopus.com/inward/record.url?eid=2-s2.0-85161065173&partnerID=MN8TOARS

Sriven Srilakshmi Pulkaram | CyberSecurity | Innovative Research Award

Published on by Global CSE Awards

Innovative Research Award

Sriven Srilakshmi Pulkaram
California State University, Dominguez Hills

                Sriven Srilakshmi Pulkaram
Affiliation California State University, Dominguez Hills
Country United States
Scopus ID 60211143700
Documents 3
Citations 4
h-index 1
Subject Area CyberSecurity
Event Global CSE Awards
Google Scholar FLw_CQwAAAAJ

Sriven Srilakshmi Pulkaram is a researcher affiliated with California State University, Dominguez Hills, whose scholarly work focuses on cybersecurity, privacy-preserving machine learning, healthcare Internet of Things (IoT), federated learning, homomorphic encryption, and secure data aggregation. Her published contributions address emerging challenges in secure distributed intelligence and privacy-aware healthcare computing environments.[1]

Abstract

This article summarizes the academic profile, research accomplishments, publication record, and scholarly impact of Sriven Srilakshmi Pulkaram. Her research addresses cybersecurity and privacy challenges in healthcare IoT systems through federated learning, encrypted aggregation, and edge-assisted computing architectures designed to improve secure and scalable distributed intelligence.[1]

Keywords

Cybersecurity, Federated Learning, Healthcare IoT, Homomorphic Encryption, Privacy Preservation, Edge Computing, Secure Aggregation, Wearable Devices, Distributed Intelligence, Data Security.

Introduction

The increasing adoption of connected healthcare technologies has intensified concerns regarding privacy, security, and trustworthy data sharing. Sriven Srilakshmi Pulkaram’s research explores advanced cybersecurity mechanisms for healthcare IoT environments, emphasizing federated learning, encrypted computation, and privacy-preserving communication frameworks that support secure and efficient distributed machine learning systems.[1][2]

Research Profile

As a researcher in computer science and cybersecurity, Pulkaram focuses on privacy-enhancing technologies for distributed healthcare applications. Her work combines federated learning, homomorphic encryption, edge computing, and secure networking principles to address confidentiality, scalability, latency, and data protection requirements within modern digital healthcare infrastructures.[1][2]

Research Contributions

Her contributions include the development of privacy-aware federated learning frameworks, encrypted aggregation techniques, and edge-assisted architectures that enhance healthcare IoT security. These studies investigate practical methods for reducing communication overhead while maintaining confidentiality, model accuracy, scalability, and compliance with evolving privacy expectations in sensitive environments.[1][2][3]

Publications

The publication record includes research on encrypted federated learning for wearable healthcare systems, homomorphic encryption in health IoT networks, and privacy-preserving in-network aggregation approaches. These works collectively examine secure machine learning deployment, low-latency communication strategies, and efficient protection mechanisms for healthcare-related distributed data processing.[1][2][3]

Research Impact

The research contributes to the growing body of knowledge surrounding secure artificial intelligence and healthcare cybersecurity. By addressing privacy, latency, and scalability challenges simultaneously, these studies support the advancement of practical frameworks that may facilitate trustworthy deployment of distributed intelligence across healthcare and IoT ecosystems.[1][2][3]

Award Suitability

The Innovative Research Award recognizes researchers whose work demonstrates originality and relevance. Pulkaram’s investigations into privacy-preserving healthcare computing, encrypted machine learning, and cybersecurity-driven solutions align with the objectives of innovation-oriented recognition programs by addressing significant technical challenges through emerging computational methodologies.[1][2]

Conclusion

Sriven Srilakshmi Pulkaram has established an emerging research profile focused on cybersecurity and privacy-preserving healthcare technologies. Her scholarly contributions demonstrate engagement with contemporary challenges involving federated learning, encrypted computation, and secure IoT systems, supporting ongoing advancements in trustworthy and scalable digital healthcare environments.[1][2][3]

References

  1. Khan, H., Kavati, R., Pulkaram, S. S., & Jalooli, A. (2025). End-to-end privacy-aware federated learning for wearable health devices via encrypted aggregation in programmable networks. Sensors, 25(22), 7023.
    https://www.mdpi.com/1424-8220/25/22/7023
  2. Pulkaram, S. S., et al. (2025). Securing Federated Learning in Health IoT with Edge-Assisted Homomorphic Encryption. IEEE Conference Proceedings.
    https://ieeexplore.ieee.org/abstract/document/11393779
  3. Pulkaram, S. S., et al. (2025). Efficient Privacy-Preserving In-Network Data Aggregation for Low-Latency Healthcare IoT. IEEE Conference Proceedings.
    https://ieeexplore.ieee.org/abstract/document/11393722
  4. Elsevier. (n.d.). Scopus author details: Sriven Srilakshmi Pulkaram, Author ID 60211143700. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60211143700

Mojtaba Rafiee | Cryptography | Innovative Research Award

Published on by Global CSE Awards

Innovative Research Award

Mojtaba Rafiee
University of Isfahan, Iran

Mojtaba Rafiee
Affiliation University of Isfahan
Country Iran
Scopus ID 56689591300
Documents 5
Citations 53
h-index 4
Subject Area Cryptography
Event Global CSE Awards
ORCID 0000-0001-9365-1803

Mojtaba Rafiee is a researcher affiliated with the University of Isfahan whose scholarly work focuses on cryptography, privacy-preserving systems, and secure cloud computation. His research contributions emphasize functional encryption, encrypted set operations, and adaptive security models applicable to modern distributed systems and secure communication infrastructures.[1] His published studies demonstrate ongoing engagement with advanced cybersecurity challenges involving encrypted cloud datasets and secure multi-client cryptographic protocols.[2]

Abstract

Mojtaba Rafiee has contributed to research in cryptography and secure cloud computing through studies centered on functional encryption, private set operations, and adaptive security frameworks. His work addresses challenges related to data privacy, encrypted cloud datasets, and secure information sharing across distributed systems. By examining multi-adjustable join schemes and multi-client encryption models, his publications support the advancement of efficient privacy-preserving computation methodologies applicable to modern cybersecurity environments.[1][2] The scholarly impact of these studies reflects continued interest in practical cryptographic applications designed for scalable and secure computational infrastructures.[3]

Keywords

Cryptography, Functional Encryption, Secure Cloud Computing, Data Privacy, Encrypted Datasets, Multi-Client Encryption, Adaptive Security, Private Set Operations, Cybersecurity, Secure Computation.

Introduction

The increasing demand for secure digital communication has expanded research interest in cryptographic systems capable of preserving privacy within distributed computing environments. Mojtaba Rafiee’s work contributes to this field through studies focused on functional encryption and privacy-preserving cloud operations designed for modern computational infrastructures.[1]

Research Profile

Mojtaba Rafiee is affiliated with the University of Isfahan and specializes in cryptography and secure computation research. His publications investigate adaptive security mechanisms, encrypted cloud data processing, and functional encryption frameworks that support secure information exchange across distributed digital platforms.[2]

Research Contributions

His research contributions include the development of multi-adjustable join schemes and secure set intersection mechanisms applicable to encrypted cloud environments. These studies address data confidentiality challenges while maintaining computational efficiency and adaptable security structures for multi-client cryptographic applications.[1][4]

Publications

The publication record of Mojtaba Rafiee includes articles published in recognized journals such as IEEE Transactions on Dependable and Secure Computing, The Journal of Supercomputing, and The Computer Journal. These studies collectively examine encryption methodologies, secure cloud datasets, and adaptive privacy-preserving systems.[1][2]

  • Multi-Adjustable Join Schemes with Adaptive Indistinguishably Security
  • Flexible Multi-Client Functional Encryption for Set Intersection
  • Security of Multi-Adjustable Join Schemes: Separations and Implications
  • Private Set Operations over Encrypted Cloud Dataset and Applications

Research Impact

The documented citation record and publication activity indicate scholarly engagement within the field of cryptography. His studies contribute to advancing privacy-preserving computational methods and support ongoing academic discussion regarding secure cloud infrastructures and encrypted communication technologies.[3]

Award Suitability

Mojtaba Rafiee’s research profile aligns with the objectives of the Global CSE Awards due to his contributions to cryptography and secure computing methodologies. His publications address contemporary cybersecurity challenges while presenting practical frameworks for secure data sharing and encrypted cloud computation.[1]

Conclusion

The academic contributions of Mojtaba Rafiee reflect continued research activity in cryptography and secure cloud technologies. His studies provide relevant insights into adaptive encryption systems and privacy-preserving computation, supporting the broader advancement of dependable and secure digital communication infrastructures.[2]

References

  1. Rafiee, M. (2023). Multi-Adjustable Join Schemes with Adaptive Indistinguishably Security. IEEE Transactions on Dependable and Secure Computing.
    https://ieeexplore.ieee.org/document/10363626
  2. Rafiee, M. (2023). Flexible multi-client functional encryption for set intersection. The Journal of Supercomputing.
    https://link.springer.com/article/10.1007/s11227-023-05129-y
  3. Rafiee, M., & Khazaei, S. (2021). Security of Multi-Adjustable Join Schemes: Separations and Implications. IEEE Transactions on Dependable and Secure Computing.
    https://ieeexplore.ieee.org/document/9366363
  4. Rafiee, M., & Khazaei, S. (2020). Private Set Operations over Encrypted Cloud Dataset and Applications.
    https://ieeexplore.ieee.org/document/9579286
  5. Elsevier. (n.d.). Scopus author details: Mojtaba Rafiee, Author ID 56689591300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=56689591300