Design and Implementation of Cost-Effective Assurance Engineering Using Wake-Sleep Algorithm-Based WideResNet with Nonlinear Analysis and Topology Integration

Abstract

The proliferation of artificial intelligence (AI) in recent years has resulted in an increase in the AI-driven applications across diverse fields. The assurance of the credibility of these AI systems has emerged as a crucial worry. This paper introduces a new model for achieving cost-effective assurance engineering by employing the Wake-Sleep algorithm with the WideResNet architecture. The Wake-Sleep method is utilized in unsupervised learning. This methodology improves the comprehensibility of AI but also establishes a basis for cost-efficient assurance engineering. The Wake-Sleep algorithm has two distinct phases: the Wake Phase, which entails the generative model using actual data, and the Sleep Phase, which enhancement of the encoder through the generated data utilization. The framework is the WideResNet neural network design, which is widely recognized for its effectiveness. The characteristics offer an equilibrium between computational resources and model performance, making it a viable choice for cost-effective solution.