This book presents the result of an innovative challenge, to create a systematic literature overview driven by machine-generated content. Questions and related keywords were prepared for the machine to query, discover, collate and structure by Artificial Intelligence (AI) clustering. The AI-based approach seemed especially suitable to provide an innovative perspective as the topics are indeed both complex, interdisciplinary and multidisciplinary, for example, climate, planetary and evolution sciences. Springer Nature has published much on these topics in its journals over the years, so the challenge was for the machine to identify the most relevant content and present it in a structured way that the reader would find useful. The automatically generated literature summaries in this book are intended as a springboard to further discoverability. They are particularly useful to readers with limited time, looking to learn more about the subject quickly and especially if they are new to the topics. Springer Nature seeks to support anyone who needs a fast and effective start in their content discovery journey, from the undergraduate student exploring interdisciplinary content to Master- or PhD-thesis developing research questions, to the practitioner seeking support materials, this book can serve as an inspiration, to name a few examples.

It is important to us as a publisher to make the advances in technology easily accessible to our authors and find new ways of AI-based author services that allow human-machine interaction to generate readable, usable, collated, research content.

A Machine-Generated Literature Overview of Computational Artificial Intelligence and Methods for industries An experimental text into machine generated and curated content A starting guide to CAMS for industries

Autorentext

Dr. Vijayarangan Natarajan earned his Ph.D. in Mathematics in 2001 from the Ramanujan Institute for Advanced Studies in Mathematics at the University of Madras in Chennai. His doctoral research focused on Krein H, J-algebras, and triple systems.

His research interests span a wide range of areas, including mobile computing, Hilbert algebras, Jordan algebras, Lie algebras, number theory, elliptic curve cryptography, communication protocols, quantitative analysis, real and complex analysis in image processing, artificial intelligence, machine learning predictions, and stochastic computing.

In June 2000, he received the Best Research Paper Award from the Ramanujan Mathematical Society. Dr. Natarajan has approximately 28 years of diverse academic and industry experience. He has presented his research at numerous universities around the world and has published over 100 works, including research papers, patents, and books.

Inhalt

1. Overview of CAMS.- 2. Computations and methods.- 3. Industrial challenges and solutions.- 4. Future trends.