This study employs Support Vector Machine (SVM) analysis to assess agricultural productivity in Tamil Nadu, India. SVM, a powerful machine learning technique, is utilized to analyze diverse agricultural datasets encompassing crop yields, climatic factors, soil properties, and socioeconomic indicators. The SVM model is trained to predict and evaluate agricultural productivity based on historical data, providing insights into the factors influencing crop performance in the region. Results highlight the effectiveness of SVM in identifying critical variables affecting agricultural outcomes and offer valuable insights for policymakers and agricultural stakeholders aiming to enhance productivity and sustainability in Tamil Nadu.

This study explores the digital evolution of business ecosystems, drawing insights from the Korean Pop (K-pop) industry. As a global phenomenon, K-pop has leveraged digital technologies to transform traditional business models, revolutionizing fan engagement, content distribution, and revenue generation. This research examines key digital strategies employed by K-pop agencies and artists, including social media marketing, digital content creation, and online community management. By analyzing these strategies, the study identifies lessons that can be applied to other industries undergoing digital transformation. The findings underscore the importance of adapting to digital trends, fostering fan communities, and optimizing digital platforms to sustain competitive advantage in modern business ecosystems.