Gaussian Coordinate Feature Set Optimisation (GCFSO) Model for Analysing the Impact of Urbanisation on Cultural Identities and City Images

Abstract

Urbanization, characterized by the rapid growth of cities and the associated changes in social, economic, and physical landscapes, has been a significant global phenomenon in recent decades. As cities expand and transform, they play a crucial role in shaping cultural identity and projecting a distinct city image. The constructed framework process comprises the multi-modal image processing with the Gaussian Coordinate estimation with the Optimization model. The proposed model comprises the estimation of the feature sets associated with the feature set for the classification of cultural identity. The model comprises the Gaussian Coordinate Feature Set Optimization (GcFSO). The GcFSO model estimates the feature sets in the data related to the cultural identity for the shaping of images to analyze the urbanization process. The GcFSO model computes the feature set based on the Mandami Fuzzy set model for the classification of cultural identity in China. The fuzzy set rules are computed for the estimation of the features in the process of Urbanization. The performance of the GcFSO model is examined with the deep learning model for classification. The simulation analysis stated that GcFSO model achieves a higher classification accuracy of 0.99 with a minimal error rate.