Research on Artificial Intelligence Classification and Statistical Methods of Financial Data in Smart Cities

Area of Science:

• Data Science
• Artificial Intelligence
• Financial Analytics

Background:

• Effective financial data classification and information extraction are crucial for smart cities.
• Existing data mining algorithms require improvement for handling multidimensional and incomplete financial datasets.

Purpose of the Study:

• To improve financial data classification and information extraction using advanced data mining techniques.
• To develop an intelligent financial data processing model for smart cities by integrating artificial intelligence and statistical methods.

Main Methods:

• Improved data mining algorithm incorporating linear combination of principal components for missing variables.
• Dimensionality reduction for multidimensional financial data.
• Data standardization and integration with statistical methods to build an intelligent processing model.

Main Results:

• The proposed methods demonstrate effective dimensionality reduction and data standardization.
• Experimental analysis using data simulation confirms the utility of the artificial intelligence and statistical methods.
• The developed model shows significant improvements in financial data classification and analysis.

Conclusions:

• The artificial intelligence classification and statistical methods proposed are vital for financial data analysis in smart city contexts.
• The study successfully integrates advanced data processing techniques for more effective financial data management.
• The findings support the application of these methods for enhanced decision-making in smart city financial systems.