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Artificial Intelligence on Fashion and Textiles

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Cover of 'Artificial Intelligence on Fashion and Textiles'

Table of Contents

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    Book Overview
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    Chapter 1 A Clothing Recommendation System Based on Expert Knowledge
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    Chapter 2 Coordinated Optimization of Production and Delivery Operations in Apparel Supply Chains Using a Hybrid Intelligent Algorithm
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    Chapter 3 Intelligent Cashmere/Wool Classification with Convolutional Neural Network
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    Chapter 4 Yarn Quality Prediction for Spinning Production Using the Improved Apriori Algorithms
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    Chapter 5 Everybody Immersive Fashion_Human–Computer Interaction in VR
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    Chapter 6 Fabric Defect Detection Based on Faster RCNN
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    Chapter 7 Woven Light: An Investigation of Woven Photonic Textiles
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    Chapter 8 A Piezoelectric Energy Harvester for Wearable Applications
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    Chapter 9 Surrogate-Based Modeling and Optimization of the Bleach Washing for Denim Fabrics
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    Chapter 10 Costume Expert Recommendation System Based on Physical Features
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    Chapter 11 Cognitive Characteristics Based Autonomous Development of Clothing Style
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    Chapter 12 Fabric Identification Using Convolutional Neural Network
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    Chapter 13 Discrete Hashing Based Supervised Matrix Factorization for Cross-Modal Retrieval
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    Chapter 14 Sparse Discriminant Principle Component Analysis
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    Chapter 15 New Product Design with Popular Fashion Style Discovery Using Machine Learning
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    Chapter 16 Challenges in Knitted E-textiles
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    Chapter 17 The CF+TF-IDF TV-Program Recommendation
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    Chapter 18 Minimize the Cost Function in Multiple Objective Optimization by Using NSGA-II
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    Chapter 19 Two-Layer Mixture Network Ensemble for Apparel Attributes Classification
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    Chapter 20 3D Digital Modeling and Design of Custom-Fit Functional Compression Garment
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    Chapter 21 Fine-Grained Apparel Image Recognition Based on Deep Learning
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    Chapter 22 Learning a Discriminative Projection and Representation for Image Classification
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    Chapter 23 Fashion Outfit Style Retrieval Based on Hashing Method
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    Chapter 24 Supervised Locality Preserving Hashing
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    Chapter 25 Co-designing Interactive Textile for Multisensory Environments
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    Chapter 26 Parametric Stitching: Co-designing with Machines
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    Chapter 27 Live: Scape BLOOM: Connecting Smart Fashion to the IoT Ecology
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    Chapter 28 Traps in Multisource Heterogeneous Big Data Processing
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    Chapter 29 Convolutional Neural Networks for Finance Image Classification
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    Chapter 30 Rough Possibilistic Clustering for Fabric Image Segmentation
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    Chapter 31 Fashion Meets AI Technology
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    Chapter 32 Fashion Style Recognition with Graph-Based Deep Convolutional Neural Networks
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    Chapter 33 Fabric Defect Detection with Cartoon–Texture Decomposition
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    Chapter 34 Fabric Texture Removal with Deep Convolutional Neural Networks
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    Chapter 35 Optimal Gabor Filtering for the Inspection of Striped Fabric
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    Chapter 36 Robust Feature Extraction for Material Image Retrieval in Fashion Accessory Management
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    Chapter 37 Woven Fabric Defect Detection Based on Convolutional Neural Network for Binary Classification
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    Chapter 38 Complex Textile Products and Reducing Consumer Waste
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    Chapter 39 A Fast Parallel and Multi-population Framework with Single-Objective Guide for Many-Objective Optimization
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    Chapter 40 Multiple Criteria Group Decision-Making Based on Hesitant Fuzzy Linguistic Consensus Model for Fashion Sales Forecasting
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    Chapter 41 Probabilistic Linguistic Linear Least Absolute Regression for Fashion Trend Forecasting
Overall attention for this book and its chapters
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Citations

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Readers on

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16 Mendeley
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Title
Artificial Intelligence on Fashion and Textiles
Published by
Springer International Publishing, November 2018
DOI 10.1007/978-3-319-99695-0
ISBNs
978-3-31-999694-3, 978-3-31-999695-0
Editors

Wong, Wai Keung

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 > Ph. D. Student 4 25%
Student > Master 3 19%
Student > Doctoral Student 2 13%
Professor 1 6%
Other 1 6%
Other 1 6%
Unknown 4 25%
Readers by discipline Count As %
Computer Science 4 25%
Engineering 3 19%
Business, Management and Accounting 1 6%
Arts and Humanities 1 6%
Neuroscience 1 6%
Other 1 6%
Unknown 5 31%