Development of Association Rule Mining Model for Gender Classification. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Tiwari, Meena; Shanthi, V.; Mishra, Ashish
• Is Part Of: IOP conference series. Volume 1022:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: support vector machine -- Fuzzy C-means -- SVM -- Gender classification -- fingerprint images -- association rule mining; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1022/1/012064
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Fingers have unique and developmental properties for biometric authentication systems. This is because fingerprints have the following characteristics: workable, distinctive (clear), durable, accurate and reliable, and accept security and identity worldwide. Fingers are seen as legal proof of this evidence in courts around the world. SVM support machines, NN is a neural network, FCM is a fingerprint classification system used in various modes, such as the Fuzzy C method, which is widely used to adopt a model. Gender determination from fingerprints is an important step in reducing the list of offensive searches in anthropology, as few machine-based methods have been requested for gender acceptance and correction. Several researchers have carried out gender fingerprint analysis and obtained competitive results. This article explains the difference between the genders using communication methods and classification methods. It is recommended that competent studies be combined with different methods and methods with comparative indicators to predict results. This allows researchers to conduct a comprehensive study and further research on models for the management of gender-specific mines.