Best Paper Award
Nana Kwame Gyamfi
Vilnius Gediminas Technical University, Lithuania
Nana Kwame Gyamfi is an academic researcher and computer science professional affiliated with Vilnius Gediminas Technical University, Lithuania. His research focuses on machine learning, deep learning, cybersecurity, anomaly detection, database security, and big data architectures. His scholarly contributions include research on automated malware detection, intelligent security systems, algorithm evaluation, and data-driven applications across information systems. His academic profile reflects interdisciplinary research connecting computational intelligence, information security, and practical technology applications. [1]
Abstract
Nana Kwame Gyamfi’s research examines the application of machine learning and computational intelligence to cybersecurity, anomaly detection, and information systems. His scholarly work addresses automated system-level malware identification, host-based intrusion detection, big data analytics, and empirical algorithm selection. His contributions connect machine-learning methodologies with secure data processing, database-oriented applications, and practical information technology challenges. [2] [3]
Keywords
Machine Learning; Deep Learning; Machine Learning on Databases; Database Security; Big Data Analytics; Anomaly Detection; Malware Detection; Host-Based Intrusion Detection; Cybersecurity; System Call Analysis; Artificial Intelligence; Support Vector Machine; Random Forest; Algorithm Selection; Information Systems; Data Protection; Information Security Governance; Hybrid Machine Learning; Security Analytics.
Introduction
Machine learning has become an important approach to identifying complex patterns, detecting anomalies, and strengthening information system security. Within this research landscape, the integration of intelligent algorithms with large-scale data environments supports the development of more effective analytical and security mechanisms. Nana Kwame Gyamfi’s research addresses these challenges through empirical investigations of machine learning, system-level malware detection, and data-driven security methodologies. His publications examine computational approaches relevant to secure information processing and intelligent decision-making. [2] [3]
Research Profile
Nana Kwame Gyamfi is a researcher at Vilnius Gediminas Technical University and Senior Lecturer at Kumasi Technical University. His research focuses on machine learning, deep learning, cybersecurity, big data, and database security. He holds an M.Phil. in Computer Science and has experience in academic research, teaching, supervision, and information security management. [1]
Research Contributions
Nana Kwame Gyamfi’s research focuses on machine-learning-based malware detection, anomaly identification, cybersecurity, and big data analytics. His contributions include hybrid learning models, algorithm evaluation, banking security, mobile forensics, and artificial intelligence applications. He also has experience coordinating research projects, managing data quality, and developing monitoring and evaluation frameworks across interdisciplinary programmes. [2] [3]
Publications
Nana Kwame Gyamfi’s scholarly publications cover malware detection, machine learning, cybersecurity, big data, and information systems. Notable works include a comprehensive review of automated system-level malware detection, research on bank fraud detection using support vector machines, and an investigation of internet banking service quality. These publications illustrate his interdisciplinary engagement with computational intelligence and applied information technology. [2] [3] [4]
Research Impact
Nana Kwame Gyamfi’s research profile reports 37 documents, 453 citations, and an h-index of 8. His publications address cybersecurity, malware detection, machine learning, and financial security. Beyond research, he contributes through academic teaching, research supervision, journal peer review, institutional service, and professional expertise in information security governance and risk management. [1][2] [3]
Award Suitability
The Best Paper Award under the International Database Scientist Awards recognizes scholarly contributions demonstrating research relevance, methodological quality, originality, and academic significance. Nana Kwame Gyamfi’s documented publications on machine-learning-based malware detection, algorithm evaluation, and computational security provide relevant evidence of research activity. His work connecting intelligent analytical methods with data-intensive information systems offers a substantive basis for consideration within the award’s evaluation framework. [2] [3]
Conclusion
Nana Kwame Gyamfi’s academic work demonstrates sustained engagement with machine learning, cybersecurity, anomaly detection, big data, and information systems. His publications examine computational approaches to security challenges and intelligent data analysis. His academic and professional experience spans teaching, supervision, governance, and applied technology programmes, supporting consideration for Best Paper Award recognition.
External Links
References
- Gyamfi, N. K. (n.d.). Google Scholar academic profile and citation record. Google Scholar. https://scholar.google.com/citations?user=oCekwTQAAAAJ&hl=en
- Gyamfi, N. K., & Abdulai, J. D. (2018). Bank fraud detection using support vector machine. In 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE. https://ieeexplore.ieee.org/abstract/document/8614994
- Gyamfi, N. K., Goranin, N., Ceponis, D., & Čenys, H. A. (2023). Automated system-level malware detection using machine learning: A comprehensive review. Applied Sciences, 13(21), Article 11908. https://doi.org/10.3390/app132111908
- Nimako, S. G., Gyamfi, N. K., & Wandaogou, A. M. M. (2013). Customer satisfaction with internet banking service quality in the Ghanaian banking industry. International Journal of Scientific & Technology Research, 2(7), 165–175. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=oCekwTQAAAAJ&citation_for_view=oCekwTQAAAAJ:2osOgNQ5qMEC