Advanced Analytics For Industry 4 0
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Advanced Analytics for Industry 4.0
The evolution of modern technology has affected all the industry dimensions. Mother industries play a critical role in providing the precursor materials for other industries, and a small improvement in these can make a big change in others. This book covers the analytics revolution in Industry 4.0 for the mother industries, such as mining, oil and gas, and steel. It focuses on the use of advanced analytics and artificial intelligence to improve the business decisions aimed at increasing the quality and quantity of mother industries' products. It helps to design and implement their digital transformation strategies in these industries. Key Features: Provides a concise overview of state of the art for mother industries' executives and managers. Highlights and describes critical opportunity areas for industry operations optimization. Explains how to implement advanced data analytics through case studies and examples. Provides approaches and methods to improve data-driven decision-making. Brings experience and learning in digital transformation from adjacent sectors. This book is aimed at researchers, professionals, and graduate students in data science, manufacturing, automation, and computer engineering.
Cloud Analytics for Industry 4.0
Author: Sirisha Potluri
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2022-10-03
This book provides research on the state-of-the-art methods for data management in the fourth industrial revolution, with particular focus on cloud.based data analytics for digital manufacturing infrastructures. Innovative techniques and methods for secure, flexible and profi table cloud manufacturing will be gathered to present advanced and specialized research in the selected area.
Robust Methods for Data Reduction
Robust Methods for Data Reduction gives a non-technical overview of robust data reduction techniques, encouraging the use of these important and useful methods in practical applications. The main areas covered include principal components analysis, sparse principal component analysis, canonical correlation analysis, factor analysis, clustering, dou