Automated fabric color prediction

dc.contributor.advisorJahan, Sifat E
dc.contributor.advisorMostakim, Moin
dc.contributor.authorMuhtashima, Fawzia
dc.contributor.authorMaksurah, Fawzia
dc.date.accessioned2024-04-24T06:11:11Z
dc.date.available2024-04-24T06:11:11Z
dc.date.issued2023-05
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 20-21).
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
dc.description.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"
dc.identifier.otherID 19101204
dc.identifier.otherhttps://dspace.bracu.ac.bd/server/api/core/items/32515eb9-756f-4a83-ab15-c3e179249ae5
dc.identifier.urihttp://hdl.handle.net/10361/22664
dc.language.isoen
dc.publisherBRAC University
dc.sourceBRAC University Institutional Repository
dc.subjectColor prediction
dc.subjectDecision tree
dc.subjectLinear regression
dc.subjectNeural network
dc.subjectFeature Engineering
dc.titleAutomated fabric color prediction
dc.typeThesis

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