Bøger af Nirav Joshi

This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material¿s electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods.

This book provides a unique approach to understand the Nuclear Physics, especially from the experimental end. The highlight of this book is that special care has been taken to provide more experimental information, considering real experimental data which has been published in several journals. Special experimental focus is given to methodologies involving: offline gamma counting and online particle detection. The book provides information about recent developments in accelerators, overview of the detectors and concise information of associated electronics, data acquisition systems and computers for data analysis.