Shaik Jakeer Hussain

India's Sustainable Rural Development (SRD) policy is a pivotal step toward achieving equitable growth and strengthening rural governance structures. This study focuses on enhancing Gram Panchayat governance by proposing an Improved Genetic Algorithm (IGA)-based approach to streamline decision-making, optimize resource allocation, and foster participatory rural development. Household survey data collected in Karnataka (November 2022) inform the analysis, emphasizing best practices in rural governance and policy implementation. The findings highlight the critical role of local leadership, particularly village heads and Gram Panchayat Secretaries, in effectively overseeing SRD initiatives. Key recommendations include appointing village heads as SRD directors, deploying specialized officers to priority rural areas, and adopting standardized governance protocols. Between 2018 and 2022, performance metrics such as economic growth, citizen engagement, environmental sustainability, and rural wastewater management were evaluated. Results demonstrate that integrating IGA into rural development strategies significantly enhances resilience and sustainability outcomes. The proposed framework underscores the potential for improved funding mechanisms and administrative efficiency in addressing pressing rural challenges. This research provides valuable insights for policymakers, contributing to the advancement of India’s SRD goals and fostering a more inclusive and sustainable rural future.

In the era of digitization, news spreads, which has turned out to be a big challenge because people seem to be being systematically misinformed. This lowers the credibility of information given on the web and brings threats against social stability BiLSTM and LSTM fusion for fake news classification: Two-way truth seeker. The study of this research focuses on developing an improved advanced model of deep learning that has been trained on a large dataset of labeled news which increases its ability to efficiently detect fake news. The system utilizes the concept of a BiLSTM and LSTM network fusion approach which enables the process to efficiently capture long-range dependency from forward and backward directions to improve the detection of false information. As a result, the presented combination provides improved accuracy and robustness in detecting fake news. Extensive experiments and evaluations were performed using a diverse set of news articles. This project will not only help in the domain of fake news detection but also prove how well-advanced deep learning works on solving the real-world problem.

Agriculture, being a vital industry, relies significantly on predicting and improving crop yields for sustainable food production. In this context, millet, a staple crop in various regions globally, holds immense agricultural importance due to its nutritional value and resilience to harsh environmental conditions. The approach outlined in this study revolves around the utilization of advanced machine learning techniques, specifically XGBoost, which is a robust gradient boosting algorithm known for its effectiveness in handling structured data and making accurate predictions. This algorithm is employed to create a predictive model for forecasting millet yields.