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

Eshetie Teka | Cyber Security | Innovative Research Award

Published on by Global CSE Awards

Innovative Research Award

Eshetie Teka
University of Gondar, Ethiopia
                Eshetie Teka
Affiliation University of Gondar
Country Ethiopia
Google Scholar ID 12Ydj_QAAAAJ
Documents 3
Citations 9
h-index 1
Subject Area Cyber Security
Event Global CSE Awards

Eshetie Teka is affiliated with the University of Gondar, Ethiopia, and has contributed to interdisciplinary research in cyber security, machine learning, and health data analytics. His academic work focuses on predictive analytics and blockchain-enabled cyber security frameworks designed to address distributed network vulnerabilities and public health challenges. Through collaborative research activities, he has participated in studies involving ensemble machine learning algorithms for child stunting prediction and blockchain-based protection mechanisms against Distributed Denial of Service attacks. His scholarly publications demonstrate an emerging contribution to applied computing and data-driven security research within academic and technological domains.[1][2]

Abstract

This article presents an overview of the academic and research contributions of Eshetie Teka in the areas of cyber security, blockchain systems, and machine learning applications in health analytics. His research activities demonstrate interdisciplinary integration between computational intelligence and practical societal challenges. The documented publications highlight analytical approaches for predictive health assessment and blockchain-supported protection against cyber threats. These studies contribute to emerging technological advancements and support data-driven decision-making within modern digital infrastructures and healthcare-oriented analytical environments.[1]

Keywords

Cyber Security, Blockchain Technology, Machine Learning, Ensemble Algorithms, Predictive Analytics, DDoS Protection, Health Informatics, Data Science, Ethiopia, Distributed Systems.

Introduction

Eshetie Teka’s academic activities focus on integrating computational technologies with practical problem-solving approaches in cyber security and healthcare analytics. His work reflects the growing importance of machine learning and blockchain technologies in modern research environments. The studies demonstrate interdisciplinary collaboration and evidence-based technological innovation within digital systems and predictive modeling applications.[2]

Research Profile

The research profile of Eshetie Teka includes cyber security systems, blockchain-enabled protection frameworks, and machine learning-driven predictive analytics. His publications indicate collaborative engagement in solving healthcare and networking challenges through computational methodologies. The research demonstrates a combination of applied data science, information security principles, and modern analytical technologies.[1]

Research Contributions

His contributions include predictive modeling for identifying child stunting conditions using ensemble machine learning methods and blockchain-based security architectures for DDoS attack prevention. These studies support technological advancement in healthcare data analytics and network protection. The contributions illustrate practical applications of computational intelligence in real-world environments.[1][2]

Publications

    • Predicting stunting status among under five children in Ethiopia using ensemble machine learning algorithms.[1]
    • Designing of blockchain-based cyber security for the protection of Distributed Denial of Service (DDoS) attacks on client–server networks.[2]

Research Impact

The research outputs contribute to ongoing discussions regarding data-driven healthcare analysis and cyber security resilience. By applying machine learning and blockchain methodologies, the studies provide frameworks that may support decision-making, digital protection, and predictive assessment processes. These contributions demonstrate the relevance of interdisciplinary computational research within emerging technological sectors.[2]

Award Suitability

Eshetie Teka’s interdisciplinary research activities align with the objectives of the Global CSE Awards, particularly within cyber security and intelligent computing applications. His studies on blockchain-enabled protection systems and predictive machine learning models reflect innovation-oriented academic engagement and demonstrate emerging scholarly contributions suitable for international academic recognition initiatives.[1]

Conclusion

The academic profile of Eshetie Teka highlights growing involvement in computational research focused on cyber security and predictive analytics. His collaborative publications demonstrate the integration of machine learning and blockchain technologies into practical applications. These scholarly contributions indicate meaningful participation in advancing research within interdisciplinary technology-oriented academic domains.[1][2]

References

    1. Ayele, M. K., Baye, G. A., Yesuf, S. H., Engda, A. A., & Mitiku, E. T. (2025). Predicting stunting status among under five children in Ethiopia using ensemble machine learning algorithms. Nature.com listing. Nature.
      https://www.nature.com/articles/s41598-025-03206-1
    2. Mitiku, E. T., Munaye, Y. Y., Selvakumar, S., Mitiku, G. A., Belete, A. A., Zeru, S. A., et al. (2024). Designing of blockchain-based cyber security for the protection of Distributed Denial of Service (DDoS) attacks on client–server networks. Discover Data, 4(1), 8.
      https://link.springer.com/article/10.1007/s44248-026-00107-0
    3. Google Scholar. (n.d.). Eshetie Teka – Google Scholar profile. Google Scholar.
      https://scholar.google.com/citations?user=12Ydj_QAAAAJ&hl=en