An Efficient Pattern Mining Convolution Neural Network (CNN) algorithm with Grey Wolf Optimization (GWO)

• URL: http://arxiv.org/abs/2204.04704v1
• Date: Sun, 10 Apr 2022 15:18:42 GMT
• Title: An Efficient Pattern Mining Convolution Neural Network (CNN) algorithm with Grey Wolf Optimization (GWO)
• Authors: Aatif Jamshed, Bhawna Mallick, Rajendra Kumar Bharti
• Abstract summary: This paper proposed a novel model of feature analysis method with the CNN based on Convoluted Pattern of Wavelet Transform (CPWT) feature vectors. The performance of this proposed method can be validated by comparing with traditional state-of-art methods.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Automation of feature analysis in the dynamic image frame dataset deals with complexity of intensity mapping with normal and abnormal class. The threshold-based data clustering and feature analysis requires iterative model to learn the component of image frame in multi-pattern for different image frame data type. This paper proposed a novel model of feature analysis method with the CNN based on Convoluted Pattern of Wavelet Transform (CPWT) feature vectors that are optimized by Grey Wolf Optimization (GWO) algorithm. Initially, the image frame gets normalized by applying median filter to the image frame that reduce the noise and apply smoothening on it. From that, the edge information represents the boundary region of bright spot in the image frame. Neural network-based image frame classification performs repeated learning of the feature with minimum training of dataset to cluster the image frame pixels. Features of the filtered image frame was analyzed in different pattern of feature extraction model based on the convoluted model of wavelet transformation method. These features represent the different class of image frame in spatial and textural pattern of it. Convolutional Neural Network (CNN) classifier supports to analyze the features and classify the action label for the image frame dataset. This process enhances the classification with minimum number of training dataset. The performance of this proposed method can be validated by comparing with traditional state-of-art methods.

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