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Artificial Intelligence for Fashion Industry in the Big Data Era

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Cover of 'Artificial Intelligence for Fashion Industry in the Big Data Era'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction: Artificial Intelligence for Fashion Industry in the Big Data Era
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    Chapter 2 AI-Based Fashion Sales Forecasting Methods in Big Data Era
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    Chapter 3 Enhanced Predictive Models for Purchasing in the Fashion Field by Applying Regression Trees Equipped with Ordinal Logistic Regression
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    Chapter 4 A Data Mining-Based Framework for Multi-item Markdown Optimization
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    Chapter 5 Social Media Analytics for Decision Support in Fashion Buying Processes
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    Chapter 6 Review of Artificial Intelligence Applications in Garment Manufacturing
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    Chapter 7 AI for Apparel Manufacturing in Big Data Era: A Focus on Cutting and Sewing
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    Chapter 8 A Discrete Event Simulation Model with Genetic Algorithm Optimisation for Customised Textile Production Scheduling
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    Chapter 9 An Intelligent Fashion Replenishment System Based on Data Analytics and Expert Judgment
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    Chapter 10 Blockchain-Based Secured Traceability System for Textile and Clothing Supply Chain
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    Chapter 11 Artificial Intelligence Applied to Multisensory Studies of Textile Products
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    Chapter 12 Evaluation of Fashion Design Using Artificial Intelligence Tools
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    Chapter 13 Garment Wearing Comfort Analysis Using Data Mining Technology
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    Chapter 14 Garment Fit Evaluation Using Machine Learning Technology
Attention for Chapter 12: Evaluation of Fashion Design Using Artificial Intelligence Tools
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Citations

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Chapter title
Evaluation of Fashion Design Using Artificial Intelligence Tools
Chapter number 12
Book title
Artificial Intelligence for Fashion Industry in the Big Data Era
Published by
Springer, Singapore, January 2018
DOI 10.1007/978-981-13-0080-6_12
Book ISBNs
978-9-81-130079-0, 978-9-81-130080-6
Authors

Yan Hong, Xianyi Zeng, Pascal Brunixaux, Yan Chen, Hong, Yan, Zeng, Xianyi, Brunixaux, Pascal, Chen, Yan

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 19%
Student > Ph. D. Student 2 13%
Professor 2 13%
Lecturer > Senior Lecturer 1 6%
Other 1 6%
Other 3 19%
Unknown 4 25%
Readers by discipline Count As %
Design 3 19%
Engineering 3 19%
Business, Management and Accounting 2 13%
Social Sciences 1 6%
Chemical Engineering 1 6%
Other 1 6%
Unknown 5 31%