An Enhanced Data Collection System for Social Enterprises: Securing Impact with Machine Learning and Cryptography
Year: 2025
Type: article
Abstract: The automated data collection system (ADCS) represents a thorough framework that is designed to tackle diverse data management issues within social enterprises. The ADCS implements data collection and analysis methods that are an accurate, secure, and scalable system utilizing automation and advanced cryptographic security while aligning with the Sustainable Development Goals (SDGs). The system uses latent Dirichlet allocation for thematic modeling and categorization, drawing insights to improve keyword relevance in contrast to traditional frequency-based methods. The system uses a cyclical feedback process that consistently enhances keyword evaluations to adapt to evolving data environments and maintain enduring accuracy. Role-based access control (RBAC) along with a strong cryptographic architecture ensures the safety of data. The ADCS provides a reliable and practical framework for making data-driven decisions while directly supporting social entrepreneurs, NGOs, academics, and policymakers. ADCS is a cutting-edge inclusive solution that streamlines SDG alignment while guaranteeing robust data security and empowering organizations to achieve lasting impact alongside operational excellence. The article outlines the system’s unique features and compares them with existing options that illustrate its ability to revolutionize automated data management practices in social enterprises and beyond. Received: 14 January 2025 | Revised: 20 March 2025 | Accepted: 21 April 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request from the corresponding author. Author Contribution Statement Fardin Muttaki: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – original draft, Visualization, Project administration. Aqeel Sahi: Conceptualization,Validation, Formal analysis, Writing – original draft, Writing – review & editing, Supervision. Shahab Abdulla: Methodology, Validation, Investigation, Data curation, Writing – review & editing, Visualization, Supervision, Funding acquisition. Kaled Aljebur: Software, Resources.
Institution: University of Southern Queensland
Language: English
Subfield: General Social Sciences
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W4410350342