HLANN: Legal Analysis of Social Security and Working Conditions Among Unorganized Workers in Rubber Plantations using Hybrid Law-Aware Neural Network Model

Labor laws play a critical role in safeguarding the rights of unorganized workers in industries like rubber plantations. This study presents "HLANN: Legal Analysis of Social Security and Working Conditions Among Unorganized Workers in Rubber Plantations using Hybrid Law-Aware Neural Network Model." By integrating legal frameworks with worker feedback, the HLANN model leverages a hybrid architecture of CNN and LSTM to analyze compliance with labor laws and predict policy gaps. The dataset includes legal provisions, survey data, and worker interviews, preprocessed and annotated for meaningful analysis. Key findings indicate compliance gaps in maternity benefits (60%) and healthcare provision (50%), with compliance in minimum wages being relatively high (72%). The model achieved an overall accuracy of 91.5%, validated using a comprehensive dataset. The sentiment analysis of worker grievances reveals 65% negative sentiments, highlighting dissatisfaction due to unsafe working conditions, inadequate healthcare, and low wages. Predictive insights emphasize the need for stricter enforcement mechanisms and targeted reforms in maternity benefits and healthcare. This study not only provides actionable insights but also sets a precedent for leveraging AI in labor law analysis.