Biometric-Driven Digital Village Governance: A SWOC-Enabled Model with Fraud Prevention

Abstract

In the digital age, the integration of advanced technologies and biometrics plays a pivotal role in transforming governance models. This research introduces an innovative approach to digital village governance, leveraging the potential of biometric data in biomedical applications while incorporating robust fraud detection mechanisms. The proposed model harnesses the power of Swarm Fish Optimization Classification (SWOC) to enhance the accuracy and efficiency of biometric-based authentication systems, ensuring the security of village governance processes. The study outlines the multifaceted components of the Intelligent Digital Village Governance Model, emphasizing its adaptability to the unique needs of rural communities. Central to this model is the utilization of biometric data, such as fingerprints, for user identification, access control, and the delivery of essential services. SWOC, with an optimization and classification algorithm inspired by swarm behavior, is integrated to refine the accuracy of biometric identification and detect fraudulent activities. Experimental results demonstrate the efficacy of SWOC in enhancing the accuracy of biometric-based authentication, thereby strengthening the security of digital village governance. The model's adaptability, scalability, and compliance with ethical standards are also discussed, ensuring responsible deployment in rural settings.