Economic analysis of world cities using improved deep shallow learning network with intelligent shell game optimization

Abstract

An economic analysis is required to determine the financial status of every city throughout the world The economic growth of a city depends on multiple factors like health, finances, transport, industry, and so on. Therefore, it is necessary to have a user-friendly factor for handling the economic data regarding the financial evaluation outcomes The main motive behind this research work is to tackle the issue of low prediction accuracy of the conventional economic and financial growth trend assumption model by developing a model using the latest deep network technology. The data regarding the economic status of a city is collected from standard online sources. The collected data are given to the preprocessing for economic prediction in any city throughout the world phase. After that the optimal attributes from the preprocessed data are extracted with the help of a newly suggested Accuracy-based Shell Game Optimization (ASGO) algorithm Consequently, the chosen optimal attributes are given as input to the final prediction stage. The economy prediction of a city is done using the Optimized and Deep Shallow Learning Network (ODSLN). The parameters in the ODSLN are tuned using the same ASGO algorithm. This helps in enhancing the prediction functionality of the deployed model over large dimensional data. The developed model is validated with standard performance metrics against other conventional prediction models. Throughout the result analysis, the developed model attains a 94% accuracy rate and 93% sensitivity rate which is much better than the existing approaches. The efficiency of the suggested deep learning-based economic prediction model is evaluated against the recently developed model based on several performance measures.