What is law? What is the source of law? What is the law for? How does law differ from other norms or codes of conduct? What is the difference between law and morality? Who is obligated to follow the law and why? What is the difference between moral and legal obligation? This book addresses these foundational questions about the law in general, and seeks to reorient our thoughts to the specific nature of law in India, the India of today, and the possible India of the future. This volume: covers relevant foundational elements, concepts and questions of the discipline; brings the uniqueness of Indian Philosophy of Law to the fore; critically analyzes the major theories of jurisprudence; examines legal debates on secularism, rationality, religion, rights and caste politics; and presents useful cases and examples, including free speech, equality and reservation, queer law, rape and security, and the ethics of organ donation. Lucid and accessible, the book will be indispensable to students, teachers and scholars of law, philosophy, politics as well as philosophy of law, sociology of law, legal theory and jurisprudence.
This book is a robust attempt to furnish the latest scientific information for any multidisciplinary research team engaged in plant drug research. It focuses on the medicinal plants used in various pharmaceutical formulations and presents a few of newer aspects of herbal drug research. The utility of the book lies in its versatility through which it has covered a wide spectrum of research activities in the field of herbal drugs. The book will help academicians, researchers and industry leaders get every possible vital information in a single volume.
Sentiment analysis and prediction of contemporary Music can have a wide range of applications in modern society, for instance, selecting music for public institutions such as hospitals or restaurants to potentially improve the emotional well-being of personnel, patients, and customers respectively. In this project, a music recommendation system is built upon a Naive Bayes Classifier trained to predict the sentiment of songs based on song lyrics alone. Online streaming platforms have become one of the most important forms of music consumption. Most streaming platforms provide tools to assess the popularity of a song in the forms of scores and rankings. In this book, we address two issues related to song popularity. First, we predict whether an already popular song may attract higher-than-average public interest and become viral. Second, we predict whether sudden spikes in the public interest will translate into long-term popularity growth. We base our findings on data from the streaming platform Billboard, Spotify, and consider appearances in its "Most-Popular" list as indicative of popularity, and appearances in its "Virals" list as indicative of interest growth. We approach the problem as a classification task and employ a Support Vector Machine model built on popularity information to predict interest, and vice versa.
Sentiment analysis and prediction of contemporary Music can have a wide range of applications in modern society, for instance, selecting music for public institutions such as hospitals or restaurants to potentially improve the emotional well-being of personnel, patients, and customers respectively. In this project, a music recommendation system is built upon a Naive Bayes Classifier trained to predict the sentiment of songs based on song lyrics alone. Online streaming platforms have become one of the most important forms of music consumption. Most streaming platforms provide tools to assess the popularity of a song in the forms of scores and rankings. In this book, we address two issues related to song popularity. First, we predict whether an already popular song may attract higher-than-average public interest and become viral. Second, we predict whether sudden spikes in the public interest will translate into long-term popularity growth. We base our findings on data from the streaming platform Billboard, Spotify, and consider appearances in its "Most-Popular" list as indicative of popularity, and appearances in its "Virals" list as indicative of interest growth. We approach the problem as a classification task and employ a Support Vector Machine model built on popularity information to predict interest, and vice versa.
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