Abstract:

In this article, we introduce a notion for predicting insurance policy costs and consumer sentiment towards policies through the application of machine learning and artificial intelligence. Artificial intelligence will act in a similar manner to the human brain (as our brain help us in making decision as word hard if marks is less else take easy). In a rising number of disciplines, artificial intelligence (AI) has recently improved state-of-the-art outcomes. Yet, there are still a number of obstacles that prevent its use in e-government applications, both for enhancing e-government systems and citizen interactions. In this article, we discuss the problems with e-government systems and suggest a paradigm for automating and facilitating e-government functions. To be more precise, we first present a framework for the administration of information resources for e-government. The second step is to create a collection of deep learning models with the goal of automating various e-government services. Finally, we suggest an intelligent egovernment platform architecture that enables the creation and execution of AI egovernment applications. Our main objective is to enhance the current state of egovernment services using reliable AI approaches in order to decrease processing times, lower costs, and increase public happiness.

Description:

Artificial intelligence, egovernance, deep learning