Saptarshi Ghosh – författare

Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data. Investigates various methodologies and algorithms for data summarization, clustering and classification Covers both theory and practical applications from around the world, across all related disciplines of Intelligent Information Filtering and Organization Systems Explores different challenges and issues related to spam filtering, attribute selection, and classification for large datasets

Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data.- Investigates various methodologies and algorithms for data summarization, clustering and classification- Covers both theory and practical applications from around the world, across all related disciplines of Intelligent Information Filtering and Organization Systems- Explores different challenges and issues related to spam filtering, attribute selection, and classification for large datasets

This book explores AI's impact on legal evidence, with a particular focus on its use within Indian law, by identifying the key feature of an Artificial intelligence-driven Justice Delivery System. It seeks to bridge the gap in the literature on the intersection of AI and law, specifically the evaluation of evidence. The book focuses on AI's potential to enhance the evaluation of evidence in various legal contexts. It considers the reliability, admissibility, and standardisation of AI-generated evidence in legal proceedings.The key features of the book are:* Presents case studies and examples from multiple branches of law, exploring AI's use in evidence collection, analysis, and evaluation across diverse legal domains.* Offering a unique perspective on AI and legal evidence within India, the book provides a critical look at how AI can be integrated into India's legal framework and the implications for the country's judicial system.* Addresses the policy gaps and regulatory frameworks needed to govern AI in the legal sphere, with a focus on India's evolving stance on AI.* Emphasizes the need for ethical standards and privacy protection while using AI to evaluate legal evidence, examines the challenges in ensuring the validity and fairness of AI-generated evidence.* Highlights the future potential of AI in transforming the legal industry, particularly in terms of improving efficiency, reducing bias, and enabling more effective evidence evaluation.The book will be useful for academicians and scholars interested in Artificial Intelligence's impact and use in the legal system.