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This paper is concerned with a growing rule-based fuzzy model and its design realized with the aid of fuzzy clustering. The objective of this study is to develop a new design methodology concerning incremental fuzzy rules formed through fuzzy clustering. The proposed model consists of three functional components : (a) The premise part of the fuzzy rules involves membership functions designed with...
This study elaborates on a design of a face recognition algorithm realized with feature extraction from 2D-LDA and the use of polynomial-based radial basis function neural networks (P-RBF NNS). The overall face recognition system consists of two modules such as the preprocessing part and recognition part. The proposed polynomial-based radial basis function neural networks is used as an the recognition...
Recommender systems are important for e-commerce companies as well as researchers. Recently, granular association rules have been proposed for cold-start recommendation. However, existing approaches reserve only globally strong rules; therefore some users may receive no recommendation at all. In this paper, we propose to mine the top-k granular association rules for each user. First we define three...
This paper is concerned with a new architecture of an optimized FCM-based interval type-2 fuzzy neural network classifier developed with aid of Fuzzy C-Means (FCM) clustering and Particle Swarm Optimization (PSO). The premise part of the rules of this architecture is realized by two FCM clustering algorithms. These FCM clustering algorithms run for several values of the fuzzification coefficient subsequently...
Recently, vast research attention has been put to develop automated procedures for pavement inspection and evaluation. The current work concentrates on developing a multi-stage expert system for pavement distress detection and classification. Mixture of Wavelet modulus and Three Dimensional Radon Transform (3DRT) are used for knowledge generation. The features and parameters of the peaks are finally...
Nowadays, multi-label classification methods are of growing interest. Due to the relationships among the labels, traditional single-label classification methods are not directly applicable to the multi-label classification problem. This paper presents a novel multi-label classification framework based on the variable precision neighborhood rough sets, called Multi-Label classification using Rough...
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