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Data-centric approaches to industrial decisions = technology, digitisation and business decisions /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-centric approaches to industrial decisions/ edited by P. K. Kapur, Omar Alhazmi, Shakshi Singhal.
Reminder of title:	technology, digitisation and business decisions /
other author:	Kapur, P. K.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	vii, 478 p. :ill., digital ;24 cm.
[NT 15003449]:	Data Preprocessing and Fault Big Data Analysis for Open Source Software -- Towards a Practical Semantic Web of Things Framework for Underwater Sensor Network -- From Solid Waste to Mathematics of OR: The Concept of Recycling -- Human Emotion Detection using Speech Recognition in Real Time -- Case Study of Brand Resilience with special reference to S7 crisis of Samsung Electronics -- Online fraud on Social media sites -- Artificial Intelligence: Revolutionizing Industrial Decisions.
Contained By:	Springer Nature eBook
Subject:	Industrial management - Decision making -
Online resource:	https://doi.org/10.1007/978-981-96-7556-2
ISBN:	9789819675562

Data-centric approaches to industrial decisions = technology, digitisation and business decisions /
Data-centric approaches to industrial decisionstechnology, digitisation and business decisions /[electronic resource] :edited by P. K. Kapur, Omar Alhazmi, Shakshi Singhal. - Singapore :Springer Nature Singapore :2025. - vii, 478 p. :ill., digital ;24 cm. - Asset analytics. Performance and safety management,2522-5170. - Asset analytics.Performance and safety management..

Data Preprocessing and Fault Big Data Analysis for Open Source Software -- Towards a Practical Semantic Web of Things Framework for Underwater Sensor Network -- From Solid Waste to Mathematics of OR: The Concept of Recycling -- Human Emotion Detection using Speech Recognition in Real Time -- Case Study of Brand Resilience with special reference to S7 crisis of Samsung Electronics -- Online fraud on Social media sites -- Artificial Intelligence: Revolutionizing Industrial Decisions.

This book explores mathematical modeling, predictive analytics, and interdisciplinary tools in formulating, implementing, and evaluating industrial strategies. This book presents cutting-edge research that intersects advanced computational techniques with practical decision-making across industries. This edited book showcases a curated collection of peer-reviewed contributions from international scholars and practitioners, addressing the growing complexity of business decisions in the era of big data, AI, and digital transformation. From cyber-physical systems to sustainability analytics and business intelligence, each chapter provides a robust academic foundation while offering real-world implications for policymakers, managers, and researchers. With a focus on actionable insights, the book is a vital reference for operations research, analytics, and digital strategy professionals and an enriching resource for postgraduate and doctoral scholars across business and engineering disciplines.

ISBN: 9789819675562

Standard No.: 10.1007/978-981-96-7556-2doiSubjects--Topical Terms:

3787669
Industrial management
--Decision making

LC Class. No.: HD30.23

Dewey Class. No.: 658.403

Data-centric approaches to industrial decisions = technology, digitisation and business decisions /
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505 0 $a Data Preprocessing and Fault Big Data Analysis for Open Source Software -- Towards a Practical Semantic Web of Things Framework for Underwater Sensor Network -- From Solid Waste to Mathematics of OR: The Concept of Recycling -- Human Emotion Detection using Speech Recognition in Real Time -- Case Study of Brand Resilience with special reference to S7 crisis of Samsung Electronics -- Online fraud on Social media sites -- Artificial Intelligence: Revolutionizing Industrial Decisions.
520 $a This book explores mathematical modeling, predictive analytics, and interdisciplinary tools in formulating, implementing, and evaluating industrial strategies. This book presents cutting-edge research that intersects advanced computational techniques with practical decision-making across industries. This edited book showcases a curated collection of peer-reviewed contributions from international scholars and practitioners, addressing the growing complexity of business decisions in the era of big data, AI, and digital transformation. From cyber-physical systems to sustainability analytics and business intelligence, each chapter provides a robust academic foundation while offering real-world implications for policymakers, managers, and researchers. With a focus on actionable insights, the book is a vital reference for operations research, analytics, and digital strategy professionals and an enriching resource for postgraduate and doctoral scholars across business and engineering disciplines.
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