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

Afnan-Alhassan-Surveillance Award- Best Researcher Award 

Published on by Computer Vision Awards

Dr. Afnan-Alhassan-Surveillance Award- Best Researcher Award 

Shaqra University-Saudi Arabia 

Author Profile

Early Academic Pursuits

Dr. Afnan Mohammed Alhassan began her academic journey with a Bachelor's Degree in Computer Science from Shaqra University, Saudi Arabia, in 2015. Excelling in her studies, she graduated with First Class Honors, setting a solid foundation for her future academic pursuits. Eager to further her knowledge and skills, she pursued a Master of Science in Computer Science at North Carolina Agricultural and Technical State University, USA, graduating with First Class Honors in 2018. Her thirst for knowledge led her to pursue a Ph.D. in Data Mining, specializing in Knowledge Engineering, at USM, Malaysia, where she demonstrated exceptional dedication and expertise in her chosen field.

Professional Endeavors

Dr. Alhassan's professional journey is marked by her contributions as a Teaching Assistant, Lecturer, and now Assistant Professor at Shaqra University, Saudi Arabia. Her commitment to academia and passion for teaching is evident in her progression through these roles. Additionally, she actively engages in research, focusing on various areas including Machine Learning, Medical Image Processing, Computer Vision, and Pattern Recognition, among others.

Contributions and Research Focus On Surveillance Award

Dr. Alhassan's research endeavors have made significant contributions to the field of computer science, particularly in healthcare applications. Her publications span a wide range of topics, from heart disease diagnosis to brain tumor classification and Alzheimer's disease diagnosis. Her innovative approaches, such as utilizing swarm algorithms and deep learning techniques, showcase her ability to combine theoretical knowledge with practical applications to address complex challenges in medical imaging and diagnosis.

Afnan Alhassan's exceptional expertise in surveillance technology has garnered widespread recognition, culminating in the prestigious Surveillance Award. With a dedication to advancing the field, Afnan's innovative contributions have revolutionized surveillance practices, enhancing security measures across various domains.

Accolades and Recognition

Throughout her academic and professional journey, Dr. Alhassan has received numerous accolades and recognition for her outstanding achievements. These include awards for academic excellence, best Ph.D. poster and presentation awards, and recognition from esteemed institutions such as the Saudi Arabian Cultural Mission of USA and the Saudi Cultural Attaché in Kuala Lumpur. Her work has been acknowledged both locally and internationally, reflecting the high caliber of her contributions to the field.

Her groundbreaking research has led to the development of cutting-edge surveillance systems, characterized by unparalleled efficiency and reliability. Afnan's commitment to excellence and her ability to push the boundaries of technological innovation have positioned her as a leader in the field, earning her the esteemed Surveillance Award.

Impact and Influence

Dr. Alhassan's research has the potential to significantly impact the healthcare industry by enhancing diagnostic accuracy and efficiency through advanced computational techniques. Her expertise in machine learning and medical image processing positions her as a key figure in the development of innovative solutions for disease diagnosis and treatment monitoring. Her research not only advances scientific knowledge but also holds the promise of improving patient outcomes and healthcare practices.

Legacy and Future Contributions

As a prominent figure in the field of computer science, Dr. Alhassan's legacy lies in her groundbreaking research and dedication to education. Through her mentorship and academic contributions, she inspires future generations of researchers and educators to push the boundaries of knowledge and innovation. Her future contributions are anticipated to further advance the intersection of computer science and healthcare, ultimately benefiting society as a whole through improved healthcare technologies and practices.

Notable Publication