HDIM: Hybrid DeepNet Insight Model for Analyzing Security of Workers

This study analyzes challenges faced by workers using the Hybrid DeepNet-Insight Model (HDIM). Primary data from field surveys and structured interviews were combined with secondary data sources to evaluate worker vulnerability and social security access. The model achieved an accuracy of 91.4 % in classifying workers into categories of vulnerability and identified key factors influencing their socioeconomic conditions. Results revealed that 47.2 % of workers work 10-12 hours daily, with 52.3 % having no access to any social security benefits. Furthermore, 56.3 % of workers lack awareness of their legal rights. Insights derived emphasize the urgent need for wage regulation, social security inclusion, and legal literacy programs. The proposed methodology provides actionable recommendations for policymakers to alleviate vulnerabilities among unorganized workers. This study not only offers a framework for evaluating labor conditions but also aligns data-driven insights with legal standards for practical implementation. Future research should explore longitudinal data to assess the long-term impact of proposed interventions on worker conditions.