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A neural network‐based instrumental method for light fastness assessment with dataset validation
Author(s) -
Karami Samaneh,
Izadan Hossein,
Zeinal Hamadani Ali
Publication year - 2020
Publication title -
color research and application
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.393
H-Index - 62
eISSN - 1520-6378
pISSN - 0361-2317
DOI - 10.1002/col.22545
Subject(s) - repeatability , artificial neural network , computer science , artificial intelligence , mean squared error , pattern recognition (psychology) , statistics , mathematics
A novel instrumental method for the light fastness assessment has been established. The accuracy of the visual method is affected by undesirable constraints, such as the different severity and the complications due to off‐tone color change of the specimens. Thus, it was desired to develop an instrumental method of the light fastness assessment. In this regard, a neural network‐based instrumental method of the light fastness assessment was developed. First, a proper light fastness panel was prepared, and then the visual light fastness assessments of experienced and inexperienced observers were collected. The accuracy and repeatability of the visual assessment results from these two groups of the observers were analyzed using different statistical criteria. The statistical analysis has shown that the mean of three trials of the inexperienced observers can be combined with the mean of the results obtained by experienced. Thus, all the results from the inexperienced and experienced observers were used to prepare a valid dataset for training a neural network. Different neural network structures trained with the prepared valid dataset. Among all of the implemented structures, the most accurate neural network structure is the one with one hidden layer and a 3‐7‐1 structure. The root mean square error and correlation coefficients of proposed 3‐7‐1 NN are 0.32 and 0.975, respectively, for the test sets. According to these results and the results from the comparison of the instrumental and visual assessment of the light fastness, it was concluded that the proposed neural network can be used for the instrumental light fastness assessment.