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S. Vijayalakshmi

Bio: S. Vijayalakshmi is an academic researcher. The author has contributed to research in topics: Fuzzy set & Artificial neural network. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

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TL;DR: In this paper, a fuzzy-based convolutional neural network (CNN) is proposed for image classification, and an interval type-2 fuzzy based CNN is proposed to handle uncertain information effectively.
Abstract: Last two decades, neural networks and fuzzy logic have been successfully implemented in intelligent systems. The fuzzy neural network system framework infers the union of fuzzy logic and neural system framework thoughts, which consolidates the advantages of fuzzy logic and neural network system framework. This FNN is applied in many scientific and engineering areas. Wherever there is an uncertainty associated with data fuzzy logic place a vital rule, and the fuzzy set can represent and handle uncertain information effectively. The main objective of the FNN system is to achieve a high level of accuracy by including the fuzzy logic in either neural network structure, activation function, or learning algorithms. In computer vision and intelligent systems such as convolutional neural network has more popular architectures, and their performance is excellent in many applications. In this article, fuzzy-based CNN image classification methods are analyzed, and also interval type-2 fuzzy-based CNN is proposed. From the experiment, it is identified that the proposed method performance is well.

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