An automated economics prediction model for world cities using adaptive deep capsule network with attention mechanism

Globalization leads to expanding international trade and the integration of national economies into the global economy. Global cities also known as world cities, are increasingly recognized as powerful and economically competitive hubs in the global economy. These cities are characterized by their high levels of interconnectedness, both within their urban fabric and with other major cities around the globe. A global city’s economic strength is a key factor since it attracts foreign investors and citizens. Thus, examining the economics of global cities has gained popularity among specialists as a research topic. However, several economic methods have been utilized to forecast the world economy in recent decades. Yet, their prediction rates are quite low. Hence, analyzing the economics in the world cities has become a hot research topic among experts. Despite the implementation of various economic prediction techniques over the past decades, their performance in accurately forecasting economic outcomes remains low. Hence, in this research work, an automated economic analysis strategy is introduced for the world cities to tackle this problem. Firstly, data from various benchmark sources have been collected to gather data on world cities for predicting economic status. Further, the garnered data are involved with the data pre-processing, where the data are processed to produce better predictions without any false rate. Subsequently, deep features are extracted from the resultant pre-processed data to enhance network performance. Finally, the extracted deep features are subjected to the Adaptive Deep Capsule Network with Attention Mechanism (ADCapNet-AM) for the economic forecast of the world cities. Here, the Improved Humboldt Squid Optimization Algorithm (IHSOA) is employed for optimizing the network parameters in ADCapNet-AM. Finally, the predicted outcomes are analyzed and balanced with the existing prediction techniques to showcase the effectiveness of the designed model.