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Artificial intelligence for fashion ...

Thomassey, Sebastien.

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Artificial intelligence for fashion industry in the big data era

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial intelligence for fashion industry in the big data era/ edited by Sebastien Thomassey, Xianyi Zeng.
其他作者:	Thomassey, Sebastien.
出版者:	Singapore :Springer Singapore : : 2018.,
面頁冊數:	viii, 288 p. :ill., digital ;24 cm.
內容註:	AI based fashion sales forecasting methods in big data era -- Enhanced Predictive Models for Purchasing in the Fashion Field by Applying Regression Trees Equipped with Ordinal Logistic Regression -- A Data Mining Based Framework for Multi-Item Markdown Optimization -- Social Media Analytics for decision support in Fashion Buying Processes -- Review of Artificial Intelligence Applications in Garment Manufacturing -- AI for Apparel Manufacturing in Big Data Era: A Focus on Cutting and Sewing -- A Discrete Event Simulation Model with Genetic Algorithm Optimization for Customized Textile Production Scheduling -- An intelligent fashion replenishment system based on data analytics and expert judgement -- Blockchain based secured traceability system for textile and clothing supply chain -- Artificial intelligence applied to multisensory studies of textile products -- Evaluation of Fashion Design Using Artificial Intelligence Tools -- Garment wearing comfort analysis using data mining technology -- Garment fit evaluation using machine learning technology.
Contained By:	Springer eBooks
標題:	Clothing trade - Data processing. -
電子資源:	http://dx.doi.org/10.1007/978-981-13-0080-6
ISBN:	9789811300806

Artificial intelligence for fashion industry in the big data era
Artificial intelligence for fashion industry in the big data era[electronic resource] /edited by Sebastien Thomassey, Xianyi Zeng. - Singapore :Springer Singapore :2018. - viii, 288 p. :ill., digital ;24 cm. - Springer series in fashion business,2366-8776. - Springer series in fashion business..

AI based fashion sales forecasting methods in big data era -- Enhanced Predictive Models for Purchasing in the Fashion Field by Applying Regression Trees Equipped with Ordinal Logistic Regression -- A Data Mining Based Framework for Multi-Item Markdown Optimization -- Social Media Analytics for decision support in Fashion Buying Processes -- Review of Artificial Intelligence Applications in Garment Manufacturing -- AI for Apparel Manufacturing in Big Data Era: A Focus on Cutting and Sewing -- A Discrete Event Simulation Model with Genetic Algorithm Optimization for Customized Textile Production Scheduling -- An intelligent fashion replenishment system based on data analytics and expert judgement -- Blockchain based secured traceability system for textile and clothing supply chain -- Artificial intelligence applied to multisensory studies of textile products -- Evaluation of Fashion Design Using Artificial Intelligence Tools -- Garment wearing comfort analysis using data mining technology -- Garment fit evaluation using machine learning technology.

This book provides an overview of current issues and challenges in the fashion industry and an update on data-driven artificial intelligence (AI) techniques and their potential implementation in response to those challenges. Each chapter starts off with an example of a data-driven AI technique on a particular sector of the fashion industry (design, manufacturing, supply or retailing), before moving on to illustrate its implementation in a real-world application.

ISBN: 9789811300806

Standard No.: 10.1007/978-981-13-0080-6doiSubjects--Topical Terms:

3331759
Clothing trade
--Data processing.

LC Class. No.: HD9940.A2 / A785 2018

Dewey Class. No.: 687.0285

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520 $a This book provides an overview of current issues and challenges in the fashion industry and an update on data-driven artificial intelligence (AI) techniques and their potential implementation in response to those challenges. Each chapter starts off with an example of a data-driven AI technique on a particular sector of the fashion industry (design, manufacturing, supply or retailing), before moving on to illustrate its implementation in a real-world application.
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