Tversky Similarity based Deep Neural Learning Classification for Engineering Alloys

• Published in: IOP conference series. Materials Science and Engineering Vol. 1258; no. 1; pp. 12059 - 12068
• Main Authors: Raja, P M Siva, Vidhya, S, Sumithra, R.P., Ramanan, K
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.10.2022
• Subjects: Classification; Cloth; Computing costs; Data analysis; Design; Learning; Linkages; Materials engineering; Regression coefficients; Similarity; Uncertainty
• ISSN: 1757-8981; 1757-899X
• DOI: 10.1088/1757-899X/1258/1/012059

Integrated Computational Materials Engineering (ICME) is an environment friendly technique used for performing cloth discovery and design. Computational methods introduced a new deep studying classification approach to display screen the candidate cloth designs. During the product designing stage, the ingredients are customised to meet particular needs. In ICME processes, there is always a degree of uncertainty in the process, structure, and property components. Uncertainties may be quantified, reduced, and propagated via structure–property links using the Tversky Similarity based Deep Neural Learning Classification (TS-DNLC) Method. In TS-DNLC Method, number of compound data are considered as input and given to the input layer. An input compound data is given to hidden layer 1. In that layer, regression is employed for performing the compound data analysis with structure–property linkages. After that, the regression coefficient value is sent to the hidden layer 2. In that layer, Tversky similarity function is used to identify the similarity between the regression coefficient value of training compound data and threshold value. Tversky similarity value varies from 0 to 1 and the results are transmitted to the output layer. By this way, TS-DNLC Method improves the performance of structure–property linkages. The computational cost of proposed TS-DNLC Method is higher than conventional uncertainty quantification.