Название: Artificial Intelligence, Machine Learning, and Data Science Technologies: Future Impact and Well-Being for Society 5.0
Автор: Neeraj Mohan, Ruchi Singla, Priyanka Kaushal
Издательство: CRC Press
Год: 2022
Страниц: 311
Язык:
Формат: pdf (true)
Размер: 28.8 MB

This book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, and how all countries around the world are feeling this impact.

AI in the present era is improving and gaining popularity with its new modernization for solving real- world problems in an optimized way. It allows us to replace fixed routine and multiple human tasks with AI coded computer machinery and robotics through continuous knowledge skills, i.e., machine and deep learning. These learning methods are inspired by human creativeness; nature inspires algorithms, robust ecological colonization of unwanted plants, etc. Some of these AI algorithms and techniques are discussed.

Forecasting is a highly challenging task. Various statistical models and soft computing techniques have been proposed by researchers and professional analysts to obtain more accurate forecasts. Among the various forecasting models, artificial neural networks (ANNs) are the most accurate, fast and reliable techniques. ANN finds its application in almost all the fields whether it is social, economic, engineering, atmospheric sciences or field of applied mathematics. The most salient features of ANN are that it is able to handle non- linearity, seasonality, noise and robustness of data. The motive behind ANN based computation is to design such mathematical algorithms that impersonate works in parallel to information processing as done by the human brain. The most significant feature of ANN is that they have the capacity of approximating any continuous function with high precision. ANN has the unique ability of generalizing.

The audience is graduate and postgraduate students, researchers, academicians, institutions, and professionals who are interested in exploring key technologies like Artificial Intelligence, Machine Learning, and Data Science.