Automated fabric color prediction

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Date

2023-05

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BRAC University

Abstract

"This paper focuses on addressing some challenges faced by colorists and explores various approaches to predict fabric color changes after dyeing processes. It empha- sizes the importance of color prediction in the textile industry and proposes suitable models that can effectively carry out color prediction tasks based on given recipes. By implementing such predictive models, the textile industry can improve efficiency, reduce labor-intensive practices, and enhance the overall quality control process. The methods used in this study are supervised machine learning techniques, in- cluding multiple linear regression, decision tree, random forest, and neural network. Among these models, the most appropriate one is selected and further optimized using feature engineering techniques to improve accuracy"

Description

Cataloged from PDF version of thesis.
Includes bibliographical references (pages 20-21).
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

Keywords

Color prediction, Decision tree, Linear regression, Neural network, Feature Engineering

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