Cornelia Aurora Gyorodi | Security | Innovative Research Award

Published on by Computer Vision Awards

Innovative Research Award

Cornelia Aurora Gyorodi — University of Oradea

Cornelia Aurora Gyorodi
Affiliation University of Oradea
Country Romania
Scopus ID 15925277900
Documents 44
Citations 401
h-index 10
Subject Area Security
Event Global Tech Excellence Awards
ORCID 0000-0002-7815-4355

Cornelia Aurora Gyorodi is a researcher at the University of Oradea, Romania, whose scholarly work encompasses cybersecurity, Industrial Internet of Things (IIoT), federated learning, database technologies, and intelligent software applications. Her recent publications address privacy-preserving intrusion detection, data-intensive NoSQL systems, and chatbot-supported database management, demonstrating a research profile connecting information security with contemporary computing infrastructures. [1] [2] [3]

Abstract

Cornelia Aurora Gyorodi’s research profile reflects sustained engagement with security and contemporary computing systems, particularly Industrial Internet of Things (IIoT) protection, federated learning, database performance, and accessible database management. Her recent collaborative studies examine privacy-preserving intrusion detection, comparative NoSQL database performance, and chatbot-supported interaction with databases. These works connect cybersecurity, distributed computing, data management, and intelligent interfaces. The research demonstrates an applied orientation toward practical computing challenges, including privacy, communication efficiency, scalable data processing, and usability. Collectively, these contributions provide a coherent basis for recognition within an innovative research context focused on security and emerging digital technologies. [1] [2] [3]

Keywords

Cybersecurity; Industrial Internet of Things; Federated Learning; Intrusion Detection; NoSQL Databases; MongoDB; RavenDB; Database Management; Chatbots; Artificial Intelligence; Privacy Preservation; Distributed Computing. [1] [2] [3]

Introduction

Contemporary digital infrastructures increasingly depend on interconnected devices, distributed learning, and data-intensive applications, creating simultaneous opportunities and security challenges. Gyorodi’s recent research addresses these developments through studies of IIoT intrusion detection, federated learning, database technologies, and conversational database management. The work places security within broader technological systems and practical application contexts. [1] [2] [3]

Research Profile

The research profile is centered on computer security and data-oriented computing, with particular relevance to IIoT environments. The federated-learning study investigates collaborative intrusion detection while limiting exposure of raw data, whereas the database study evaluates MongoDB and RavenDB under IIoT-inspired workloads. A separate conference contribution explores chatbot-driven database management for non-technical users. [1] [2] [3]

Research Contributions

The documented contributions span three complementary areas. Federated learning research evaluates privacy-preserving intrusion detection for distributed IIoT networks, including communication and detection considerations. Database research provides an experimental comparison of document-oriented systems across data-intensive operations. The chatbot study addresses interaction between non-technical users and database-management functionality through conversational interfaces. [1] [2] [3]

Publications

Recent publications associated with Gyorodi include a 2026 Future Internet article on federated-learning-based intrusion detection in IIoT networks, a 2026 Future Internet article comparing MongoDB and RavenDB for IIoT-inspired applications, and a 2025 IEEE conference paper on chatbot-driven database management. Together, these works illustrate research across security, databases, distributed systems, and user-oriented computing. [1] [2] [3]

Research Impact

The documented research has practical relevance to organizations developing secure industrial networks, distributed machine-learning systems, and data-intensive applications. The IIoT study reports that federated learning can achieve detection performance comparable to centralized approaches while reducing raw-data transmission, while the database comparison provides evidence for technology selection under controlled workloads. The chatbot research extends accessibility considerations to database management. [1] [2] [3]

Award Suitability

The documented record aligns with an Innovative Research Award through its combination of cybersecurity, distributed learning, IIoT, database engineering, and conversational computing. The publications address contemporary technical problems from complementary perspectives, including privacy preservation, intrusion detection, database performance, scalability, and user accessibility. The research also demonstrates interdisciplinary connections between security and applied computing systems. [1] [2] [3]

Conclusion

Cornelia Aurora Gyorodi’s documented scholarship presents a coherent research trajectory in security and modern computing, with recent work addressing federated intrusion detection, IIoT-oriented database performance, and chatbot-assisted database management. These studies collectively demonstrate engagement with emerging technologies and applied research questions involving privacy, security, scalability, performance, and accessibility. [1] [2] [3]

References

  1. Pecherle, G. D., Győrödi, R. Ș., & Győrödi, C. A. (2026). Federated learning-based intrusion detection in Industrial IoT networks. Future Internet, 18(1), 2.
    https://doi.org/10.3390/fi18010002
  2. Ciumac, M., Győrödi, C. A., Győrödi, R. Ș., & Costea, F. M. (2026). Performance evaluation of MongoDB and RavenDB in IIoT-inspired data-intensive mobile and web applications. Future Internet, 18(1), 57.
    https://doi.org/10.3390/fi18010057
  3. Gruian, A., Győrödi, C. A., & Győrödi, R. Ș. (2025). Empowering non-technical users: A chatbot-driven approach to database management. In 2025 18th International Conference on Engineering of Modern Electric Systems (EMES) (pp. 1–6). IEEE.
    https://doi.org/10.1109/EMES65692.2025.11045570

Hyun-A Park | Security | Innovative Research Award

Published on by Computer Vision Awards

Innovative Research Award

Hyun-A Park
Honam University, South Korea

Hyun-A Park
Affiliation Honam University
Country South Korea
Scopus ID 60396208000
Documents 7
Citations 13
h-index 2
Subject Area Security
Event Global Tech Excellence Awards

Hyun-A Park is a researcher affiliated with Honam University in South Korea whose scholarly work includes security-oriented research involving privacy-preserving navigation and level-of-detail processing. Park’s documented research contribution, LOD (Level of Detail) Based Optimized Privacy-Preserving Navigation, addresses privacy protection and computational efficiency in navigation systems through an integrated technical framework. [1] The research profile indexed in Scopus records seven documents, thirteen citations, and an h-index of two. [2]

Abstract

Hyun-A Park’s research is situated at the intersection of security, privacy protection, navigation, and efficient information processing. Park’s documented work on level-of-detail based privacy-preserving navigation proposes an integrated approach for reducing unnecessary information exposure while maintaining computational and communication efficiency in navigation environments. The research considers differentiated representation of spatial information, privacy-aware route optimization, secure communication, and adaptive resource management. The work demonstrates a research direction focused on balancing security requirements with practical system performance. [1] Park’s Scopus profile records seven documents, thirteen citations, and an h-index of two, providing bibliographic evidence of an emerging research trajectory. [2]

Keywords

Privacy-preserving navigation; security; level of detail; drone navigation; route optimization; information processing; communication security; adaptive systems; data protection; navigation systems.

Introduction

Privacy and security are increasingly important in navigation systems that process spatial, personal, and operational information. Park’s research addresses this challenge by combining level-of-detail processing with privacy-preserving navigation strategies. The documented study considers how selective information representation can reduce unnecessary exposure while supporting effective navigation and resource utilization in technologically complex environments. [1]

Research Profile

Park’s research profile reflects an emphasis on security-related computing and privacy-aware navigation. The available bibliographic record identifies Honam University as the institutional affiliation and lists seven documents, thirteen citations, and an h-index of two. These indicators describe an emerging scholarly profile supported by research addressing practical challenges in secure information handling and navigation technologies. [2]

Research Contributions

Park’s principal documented contribution integrates level-of-detail management with privacy-preserving navigation. The study proposes differentiated processing of spatial information, privacy-aware route optimization, secure communication mechanisms, and adaptive resource considerations. This combination connects visualization efficiency, privacy protection, navigation planning, and security into a unified research framework relevant to intelligent navigation systems. [1]

Publications

The documented publication record includes the chapter LOD (Level of Detail) Based Optimized Privacy-Preserving Navigation, authored by Hyun-A Park and published in the Springer volume Emerging Trends in Data Science, Information and Knowledge Engineering. The chapter appears on pages 470–482 and is identified by DOI 10.1007/978-3-032-22196-4_34. [1]

Research Impact

Park’s research has relevance to security-conscious navigation applications where privacy, processing efficiency, and communication protection must be considered together. The documented work presents a framework that connects privacy protection with adaptive information processing and secure navigation. Its potential significance lies in addressing practical design considerations for intelligent and resource-constrained navigation environments. [1]

Award Suitability

Hyun-A Park demonstrates characteristics relevant to an Innovative Research Award through research that combines privacy protection, security, navigation, and adaptive computational techniques. The documented publication provides evidence of an integrated technical approach to a contemporary security problem. The Scopus-indexed research record further supports recognition of an emerging scholarly contribution within the field. [1] [2]

Conclusion

Hyun-A Park’s documented research presents a focused contribution to security-oriented computing, particularly privacy-preserving navigation and adaptive information processing. The integration of level-of-detail techniques with privacy and security mechanisms provides a technically relevant direction for intelligent navigation systems. The available publication and bibliographic records support consideration for research recognition. [1] [2]

References

  1. Park, H.-A. (2026). LOD (Level of Detail) based optimized privacy-preserving navigation. In R. Stahlbock, G. M. Weiss, H. R. Arabnia, & L. Deligiannidis (Eds.), Emerging Trends in Data Science, Information and Knowledge Engineering (pp. 470–482). Springer Nature Switzerland.
    https://doi.org/10.1007/978-3-032-22196-4_34
  2. Elsevier. (n.d.). Scopus author details: Hyun-A Park, Author ID 60396208000. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60396208000

Zied Guitouni | Cryptography | Innovative Research Award

Published on by Computer Vision Awards

Innovative Research Award

Zied Guitouni — Faculty of Sciences of Monastir, Tunisia

Zied Guitouni
Affiliation Faculty of Sciences of Monastir
Country Tunisia
Scopus ID 24766321800
Documents 9
Citations 27
h-index 3
Subject Area Cryptography
Event Global Tech Excellence Awards
ORCID 0000-0002-5707-134X

Zied Guitouni is a researcher affiliated with the Faculty of Sciences of Monastir, Tunisia, whose research activity is centered on cryptography and security-oriented computing. His work addresses contemporary challenges involving blockchain, Internet of Things security, intrusion detection, medical image protection, and emerging quantum-inspired encryption approaches, reflecting an interdisciplinary direction within cybersecurity research. [1] [2] [3]

Abstract

Zied Guitouni’s research profile reflects a focused contribution to cryptography and cybersecurity, with applications spanning blockchain-enabled Internet of Things systems, smart-city intrusion detection, and medical image protection. His published work explores security architectures, lightweight machine-learning methods, and quantum chaotic techniques for addressing evolving digital threats. These studies connect cryptographic principles with practical requirements for connected infrastructures and sensitive information systems. The combination of security engineering, intelligent detection, blockchain technologies, and advanced encryption demonstrates a research direction responsive to emerging cybersecurity challenges. His publication record provides evidence of continuing engagement with applied security research and interdisciplinary technological development. [1] [2] [3]

Keywords

Cryptography; cybersecurity; blockchain; Internet of Things; intrusion detection; smart cities; medical image encryption; quantum chaos; information security; lightweight security systems; applied cryptography.

Introduction

Modern connected environments require security mechanisms capable of protecting distributed devices, digital infrastructures, and sensitive information against increasingly sophisticated threats. Guitouni’s research addresses these requirements through cryptographic engineering and cybersecurity applications involving blockchain, IoT, intelligent intrusion detection, and medical image encryption, linking theoretical security principles with practical technological contexts. [1] [2] [3]

Research Profile

The researcher’s profile is characterized by applied cryptography and cybersecurity studies addressing multiple digital environments. His work encompasses elliptic-curve cryptographic implementation, blockchain-based IoT security, neural-network-supported intrusion detection, and encryption techniques for medical imagery. This range indicates an interdisciplinary research orientation combining cryptographic methods, intelligent computing, network protection, and application-specific security requirements. [1] [2] [3]

Research Contributions

The reported contributions include advanced VLSI implementation of ECDSA for blockchain-oriented IoT applications, a lightweight feed-forward neural-network approach for smart-city intrusion detection, and quantum chaotic techniques for medical image encryption. Collectively, these studies investigate computational efficiency, network threat detection, and data confidentiality, demonstrating practical applications of cryptographic and security technologies. [1] [2] [3]

Publications

The publication record supplied for this recognition profile includes studies published in Springer journals covering blockchain-based IoT security, smart-city cybersecurity, and medical image encryption. The subjects demonstrate continuity around applied security, while the methodological approaches range from VLSI cryptographic implementation and neural-network intrusion detection to quantum chaotic encryption, reflecting diverse technical strategies. [1] [2] [3]

Research Impact

The potential impact of this research lies in its relevance to security-sensitive technological domains. Efficient cryptographic hardware can support constrained IoT environments, lightweight intrusion detection can contribute to smart-city protection, and advanced image encryption can strengthen confidentiality for medical information. These application areas position the work within important contemporary cybersecurity priorities. [1] [2] [3]

Award Suitability

The research profile is suitable for consideration for an Innovative Research Award because it demonstrates application-oriented work across several cybersecurity challenges. The combination of cryptographic hardware, intelligent intrusion detection, blockchain security, and advanced encryption illustrates technical breadth and innovation-oriented problem solving. The publications also provide identifiable scholarly evidence supporting the relevance of this research direction. [1] [2] [3]

Conclusion

Zied Guitouni’s documented research presents a coherent focus on cryptography and cybersecurity with applications in IoT, blockchain, smart cities, and medical information protection. His studies combine hardware implementation, machine learning, and advanced encryption techniques, establishing a multidisciplinary foundation for continued research addressing practical security challenges across emerging digital technologies. [1] [2] [3]

References

  1. Guitouni, Z. (2026). Advanced VLSI ECDSA design for real-time blockchain-based IoT system applications. Journal of Electrical Systems and Information Technology.
    https://link.springer.com/article/10.1007/s44291-026-00172-4
  2. Guitouni, Z. (2025). A lightweight FFNN-based intrusion detection system for smart city cybersecurity. Journal of Supercomputing.
    https://link.springer.com/article/10.1007/s11227-025-08031-x
  3. Guitouni, Z. (2025). Quantum chaotic techniques for medical image encryption in next-generation IoMT applications. Journal of Supercomputing.
    https://link.springer.com/article/10.1007/s11227-025-07574-3

Kafayat Tajudeen | Cryptography | Innovative Research Award

Published on by Computer Vision Awards

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 Cryptography
Event Global Tech Excellence Awards
ORCID 0000-0002-9831-1741

The Innovative Research Award recognizes scholarly excellence demonstrated through impactful scientific publications, responsible research practices, and measurable academic contributions. Kafayat Tajudeen has developed research focused on cryptography and cybersecurity, contributing to secure communication technologies and intelligent network protection through peer-reviewed publications. [1]

Abstract

Kafayat Tajudeen’s research portfolio demonstrates continuing contributions to cryptography and cybersecurity through studies addressing encryption techniques, secure message transmission, and intelligent network attack detection. Her published work explores practical approaches for strengthening information security using advanced encryption standards and hybrid deep learning methodologies. Supported by peer-reviewed publications and indexed scholarly output, these contributions align with internationally recognized research standards and illustrate meaningful academic development within the broader field of information security while supporting innovation, reliability, privacy, and resilient digital communication systems. [1] [2]

Keywords

Cryptography, Cybersecurity, Advanced Encryption Standard, Deep Learning, Distributed Denial of Service, Internet of Things, Information Security, Secure Communication, Artificial Intelligence, Network Security.

Introduction

Cryptography remains fundamental for protecting digital information, ensuring confidentiality, authentication, and integrity across modern communication systems. Kafayat Tajudeen’s academic interests reflect contemporary cybersecurity challenges by investigating encryption technologies and intelligent security mechanisms designed to strengthen resilient computing environments and safeguard sensitive digital infrastructure. [1]

Research Profile

Affiliated with Al-Hikmah University, Kafayat Tajudeen has established a developing research profile within cryptography and cybersecurity. Her Scopus-indexed publications demonstrate scholarly engagement with secure computing technologies while reflecting measurable academic productivity through citations, collaborative research, and internationally accessible scientific dissemination. [3]

Research Contributions

Her research contributions include evaluating advanced encryption methods for enhanced message security and developing hybrid deep learning approaches for detecting user datagram protocol-based distributed denial of service attacks in Internet of Things environments. These investigations support secure, intelligent, and adaptive cybersecurity solutions. [1] [2]

Publications

The researcher’s publication record includes peer-reviewed studies published through internationally recognized academic publishers. These publications examine encryption algorithms, intelligent threat detection, and cybersecurity applications, demonstrating commitment to producing scientifically validated research addressing practical and emerging challenges in information security. [1] [2]

Research Impact

The available citation metrics indicate that the published research has attracted scholarly attention within the cybersecurity community. Continued citation growth, indexed publications, and practical relevance demonstrate an emerging academic impact while supporting future interdisciplinary investigations into secure digital communication and intelligent cyber defense. [3]

Award Suitability

The Innovative Research Award appropriately recognizes researchers demonstrating originality, scientific quality, and measurable scholarly influence. Based on published contributions in cryptography, indexed research output, and ongoing engagement with cybersecurity innovation, Kafayat Tajudeen satisfies important indicators commonly associated with academic research recognition. [1] [3]

Conclusion

Kafayat Tajudeen’s scholarly activities contribute to advancing cryptographic security and intelligent cybersecurity research. Through peer-reviewed publications, recognized indexing, and measurable academic performance, her work reflects sustained scientific engagement and supports continued innovation addressing contemporary information security challenges across academic and applied computing environments. [1] [3]

References

  1. Author(s). (2025). A systematic review on advanced encryption standard cryptography to enhance message security. Multimedia Tools and Applications. Springer.
    https://doi.org/10.1007/s11042-025-21041-4
  2. Author(s). (2026). Hybrid deep learning models for detecting user datagram protocol-based distributed denial of service attacks in Internet of Things networks. Discover Internet of Things. Springer.
    https://doi.org/10.1007/s43926-026-00305-x
  3. Elsevier. (n.d.). Scopus Author Profile: Kafayat Tajudeen. Author ID: 58092099200. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58092099200