The ES structure described by soft subsets or soft M-subsets does not yield a lattice structure due to its restriction on parameter sets, and so cannot be used in information theory. This study proposes a new ES structure on soft sets that addresses the deficiencies of the prior structure. Using mathematical concepts, we can construct and entirely new system of soft sets. As a result, the ES structure is derived from a finite collection of basic soft sets and offers complicated soft sets via its ES components, allowing for it to be operated by computers, as this is more acceptable to conventional mathematical viewpoints. We rewrote this using a soft J-subset and demonstrated that (ES, ${\tilde{\vee }}_{ES}$, ${\tilde{\wedge }}_{ES}$) is a distributive lattice. This will play an important role in decision-making problems and contribute to a better understanding of human recognition processes. During the process of reaching a decision, several groups of parameters develop, and the ES structure in this article takes these parameters into consideration in order to handle the intricate issues that arise. In soft set theory, this research gives insight into the cognitive field.

The advent of FUZZY technology has revolutionized healthcare, empowering smarter medical devices and equipment. However, the successful operation of these FUZZY-driven systems is contingent on high power quality. This paper introduces an innovative FUZZY-driven energy management system that combines convolutional neural networks (CNNs) for real-time power quality event detection, long short-term memory (LSTM) networks for predictive analytics, and reinforcement learning for optimized control. Through extensive simulations on an IEEE 13-bus test feeder, we demonstrate the system’s superior performance in detecting and mitigating power quality disturbances. The CNN-based detection achieves 97% accuracy in classifying events, while the LSTM enables 95% accurate prediction of emerging issues. The reinforcement learning controller achieves 50% faster voltage sag restoration, 20% greater harmonic reduction, and 30% faster critical load recovery during outages compared to conventional methods. Key challenges, including data quality concerns, cybersecurity risks, and integration with legacy infrastructure, are discussed. This work represents a significant advancement in applying FUZZY technology to healthcare power quality management, offering a comprehensive solution that balances efficiency, reliability, and patient safety. The proposed system provides a scalable framework for modernizing power quality monitoring and control in healthcare facilities.