Applications for Economic and Social Sustainability Vuppalapati, Chandrasekar. creator author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

electronic

electronic resource

access XXI, 467 p. 245 illus. online resource. This volume uses advanced machine learning techniques to analyze government communication to evaluate policy effectiveness. The book develops policy effectiveness foundation models by cohorting historical budget policies with statistical models which are built on well reputed data sources including economic events, macroeconomic trends, and ratings and commerce terms from international institutions. By signal mining policies to the economic outcome patterns, the book aims to create a rich source of successful policy insights in terms of their effectiveness in bringing development to the poor and underserved communities to ensure the spread of wealth, social wellbeing, and standard of living to the common denomination of society rather than a selected quotient. Enabling academics and practitioners across disciplines to develop applications for effective policy interventions, this volume will be of interest to a wide audience including software engineers, data scientists, social scientists, economists, and agriculture practitioners. Chapter 1: Introduction -- Chapter 2 : Natural Language Models -- Chapter 3: Large Language Models -- Chapter 4 : Macroeconomic Indicators, Aggregates, and Framework -- Chapter 5 : Economic Sustainability -- Chapter 6 : Social Sustainability -- Chapter 7: Conclusion. by Chandrasekar Vuppalapati. Operations research Economic policy Social policy Machine learning Artificial intelligence Data structures (Computer science) Information theory Operations Research and Decision Theory Socio-Economic Policy Machine Learning Artificial Intelligence Data Structures and Information Theory T57.6-.97 9783031560972 https://doi.org/10.1007/978-3-031-56097-2 https://doi.org/10.1007/978-3-031-56097-2 240530 20251030112242.0 978-3-031-56097-2