An Efficient Approach for Enhancing Contrast Level and Segmenting Satellite Images: HNN and FCM Approach

The traditional recognition method of whitewash behavior of accounting statements needs to analyze a large number of special data samples. The learning rate of the algorithm is low, resulting in low recognition accuracy. To solve the aforementioned problems, this article proposes a method to identify the whitewash behavior of university accounting statements based on the FCM clustering algorithm. This article analyzes the motivation of university accounting statement whitewashing behavior, studies the common means of statement whitewashing, and establishes a fuzzy set for the identification of university accounting statement whitewashing behavior. By calculating the fuzzy partition coefficient, the membership matrix of whitewash behavior recognition is established, and the whitewash behavior is classified through the iteration of the FCM algorithm. The comparative experimental results show that the recognition method has good recognition performance, low recognition error rate, and recognition accuracy of 82%.

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An Efficient Approach for Enhancing Contrast Level and Segmenting Satellite Images: HNN and FCM Approach

Cited by 3 publications

References 29 publications

Optimized deep networks for road extraction using satellite images

Optimized deep networks for road extraction using satellite images

Cosine-similarity watershed algorithm for water-body segmentation applying deep neural network classifier

An FCM clustering algorithm based on the identification of accounting statement whitewashing behavior in universities

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