ABCGSA: A Novel Hybrid Artificial Bee Colony Gravitational Search Algorithm for Feature Selection of Fingerprint Intramodal Biometric System

Authors

  • Janet O. Jooda Department of Computer Engineering, Redeemer’s University, Nigeria
  • Sunday Ajagbe Department of Computer Science, University of Zululand, South Africa and Department of Computer Engineering, Abiola Ajimobi Technical University, Nigeria
  • Misan Paul Etchie School of Informatics, Computing and Cyber Systems, Northern Arizona University, USA
  • Oluyinka T. Adedeji Department of Information System Science, Ladoke Akintola University of Technology, Nigeria
  • Alice O. Oke Department of Computer Engineering, Ladoke Akintola University of Technology, Nigeria
  • Pragasen Mudali Department of Computer Science, University of Zululand, South Africa

DOI:

https://doi.org/10.47852/bonviewAIA62028670

Keywords:

feature selection, ABC, GSA, Fingerprint Intramodal Biometric System (FIBS), pattern recognition

Abstract

The feature selection (FS) algorithm extracts the most relevant and significant data from the large-dimensional feature space in the dataset, enhancing the biometric system’s classification accuracy. However, optimizing the feature diversity of high-dimensional feature space of the FS algorithm has been given little attention in existing FS algorithms. Therefore, a hybrid artificial bee colony (ABC)- based disruptive selection and Gravitational Search Algorithm (GSA) to form ABCGSA for enhanced FS prior to the classification phase of Fingerprint Intramodal Biometric System (FIBS) was developed. The developed ABCGSA was used to select optimal feature sets during FS to enhance the sensitivity, recognition accuracy (RA), false positive rate (FPR), false negative rate (FNR), and recognition time (RT) of FIBS. When compared with the existing algorithms used for FS, the results showed that ABCGSA’s sensitivity and RA increased by 3.21% and 4.73%, while FPR and FNR decreased by 8.96% and 3.21%, and RT improved by 20 s, thus enhancing the efficacy of FIBS. The developed algorithm could be used for FS of an intramodal biometric system to enhance access control in facilities. 

 

Received: 3 December 2025 | Revised: 24 February 2026 | Accepted: 1 April 2026

 

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work. 

 

Data Availability Statement

The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/ruizgara/socofing

 

Author Contribution Statement

Janet O. Jooda: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Sunday A. Ajagbe: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Misan Paul Etchie: Software, Validation, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Oluyinka T. Adedeji: Conceptualization, Methodology, Formal analysis, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition. Alice O. Oke: Conceptualization, Methodology, Formal analysis, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition. Pragasen Mudali: Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition.


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Published

2026-08-20

Issue

Section

Research Article

How to Cite

Jooda, J. O., Ajagbe, S., Etchie, M. P., Adedeji, O. T., Oke, A. O., & Mudali, P. (2026). ABCGSA: A Novel Hybrid Artificial Bee Colony Gravitational Search Algorithm for Feature Selection of Fingerprint Intramodal Biometric System. Artificial Intelligence and Applications. https://doi.org/10.47852/bonviewAIA62028670