HSLNF: Hybrid Socio-Legal Neural Framework for Data Analysis

The enactment of the Transgender Persons Act marked a pivotal step in addressing the socio-economic challenges faced by transgender individuals in the labor market. This study, titled “HSLNF: Impact Analysis of Labour Laws on Transgender Persons Post-Enactment of the Transgender Persons Act Using a Hybrid Socio-Legal Neural Framework,” evaluates the socio-economic and legal impact of the Transgender Persons Act on employment trends, wage equality, and workplace discrimination. A Hybrid Socio-Legal Neural Framework (HSLNF) was designed, integrating socio-economic variables such as employment rates and wage gaps with legal metrics like compliance rates and resolved cases. Primary data was collected through surveys, interviews, and focus groups with transgender individuals, while secondary data included government reports and legal documents. Sentiment analysis and text analysis of legal documents were conducted using natural language processing (NLP). The neural model achieved an accuracy of 94.5% in predicting trends and assessing labor law impacts. Results indicate a 12.7% increase in employment rates and a 25.4% decrease in severe workplace discrimination. Wage disparities, however, remain significant, with the gap reducing from $360 in 2015 to $320 in 2024. Legal awareness improved significantly, with compliance rates rising to 70% in 2024. These findings underscore the Act's effectiveness while highlighting the need for further reforms to achieve full inclusivity and equity. The study concludes with recommendations for policy development and advocacy for transgender rights.