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

Catalin Dumitrescu | Biometrics and Security | Best Researcher Award

Published on by Computer Vision Awards

Prof. Catalin Dumitrescu | Biometrics and Security | Best Researcher Award

Prof. Habil. Artificial Intelligence | University Politehnica of Bucharest | Romania

Assoc. Prof. Dr. Catalin Dumitrescu is a distinguished researcher and academic specializing in Artificial Intelligence (AI), Digital Signal Processing (DSP), and Machine Learning (ML) with a strong interdisciplinary focus on computer vision, cognitive radio, cyber defence, and multimedia security. His research integrates advanced AI algorithms into industrial electronics, telecommunications, and defence technologies, with a particular emphasis on IMINT/SIGINT systems and cyber defence infrastructures.With an impressive research portfolio comprising over 50 scientific publications, his work has garnered 536 citations, an h-index of 10, and i10-index of 15, reflecting his growing influence in the fields of intelligent systems and adaptive signal processing. Dr. Dumitrescu’s publications in leading journals such as Sensors, Electronics, Applied Sciences, and Fractal and Fractional (MDPI) highlight his expertise in deep learning, visual classification, object detection, and decision-making algorithms. His recent studies focus on AI-driven noise reduction, fractal-based steganography for data security, and UAV detection systems using sensor data fusion and fuzzy logic.His research interests span a wide spectrum, including neural networks for image and audio processing, machine learning-based EEG signal classification, brain-computer interfaces, digital watermarking and cryptography, and real-time signal and image analysis. Through collaborations with academia and industry, he has contributed to the development of automated, intelligent systems for security, communication, and transportation applications, bridging theoretical innovation with practical deployment.Dr. Dumitrescu’s commitment to advancing AI and DSP research extends to mentoring and consultancy, where he collaborates with organizations across industrial electronics, telecommunication, and defence sectors. His work has had a significant societal impact in enhancing the reliability, efficiency, and security of next-generation digital systems. His contributions continue to shape the global discourse on intelligent signal processing, autonomous systems, and secure information technologies.

Profiles: Google Scholar | ORCID 

Featured Publications

Abdulwahid Al Abdulwahid | Cyber Security | Best Researcher Award

Published on by Computer Vision Awards

Assoc. Prof. Dr. Abdulwahid Al Abdulwahid | Cyber Security | Best Researcher Award

Associate Professor | Jubail Industrial College | Saudi Arabia

Dr. Abdulwahid Al Abdulwahid is an Associate Professor of Cybersecurity and Program Director at Jubail Industrial College, Royal Commission for Jubail and Yanbu, with extensive expertise in artificial intelligence for cybersecurity, IoT and Industry 4.0 security, cloud computing privacy, biometrics, and the human aspects of cybersecurity. He holds a PhD in Computing (Cyber Security) from the University of Plymouth, UK, an MSc in Management of Information Technology from the University of Nottingham, and a BSc in Computer and Information Systems from King Faisal University, along with a Graduate Teaching Associate certification from Plymouth University. Over two decades of professional academic and administrative experience, he has served in roles such as Deputy Director for Planning and Development, College Deputy for Student Affairs, and Department Chairperson, alongside delivering specialized lectures, workshops, and training programs locally, regionally, and internationally. His research interests focus on advancing secure and usable authentication systems, AI-driven cybersecurity solutions, and quality-driven approaches in computing education. He is highly skilled in academic accreditation, governance, quality management, and strategic leadership, in addition to contributing as a reviewer, auditor, and public speaker. As an active member of professional and community organizations including ACM, the Saudi Scientific Society for Cybersecurity, Hemaya, and the Saudi Society for Quality, he continues to foster collaboration between academia, industry, and society. His research impact is reflected through 118 citations by 113 documents, 14 publications, and an h-index of 6.

Profiles: Google Scholar | Scopus | ORCID

Featured Publications

  1. Al Abdulwahid, A., Clarke, N., Stengel, I., Furnell, S., & Reich, C. (2016). Continuous and transparent multimodal authentication: Reviewing the state of the art. Cluster Computing, 19(1), 455–474.

  2. Guo, Y., Wang, Y., Khan, F., Al-Atawi, A. A., Abdulwahid, A. A., Lee, Y., & Marapelli, B. (2023). Traffic management in IoT backbone networks using GNN and MAB with SDN orchestration. Sensors, 23(16), 7091.

  3. Al Abdulwahid, A. (2022). Detection of middlebox-based attacks in healthcare Internet of Things using multiple machine learning models. Computational Intelligence and Neuroscience, 2022, 2037954.

  4. Alassafi, M. O., AlGhamdi, R., Alshdadi, A. A., Abdulwahid, A. A., & Bakhsh, S. T. (2019). Determining factors pertaining to cloud security adoption framework in government organisations: An exploratory study. IEEE Access, 7, 136822–136835.

  5. Abdulwahid, A. A. (2023). Classification of ethnicity using efficient CNN models on MORPH and FERET datasets based on face biometrics. Applied Sciences, 13(12), 7288.