Traditional statistical, physical, and correlation models for chaotic time series prediction
have problems, such as low forecasting accuracy, computational time, and difficulty …

Efficient integration of wind energy requires accurate wind power forecasting. This prediction
is critical in optimising grid operation, energy trading, and effectively harnessing renewable …

This paper focuses on the growth, development, and future of various forms of fractional-
order neural networks. Multiple advances in structure, learning algorithms, and methods …

This paper focuses on deriving and validating the fractional‐order form of rectified linear unit
activation function and its linear and nonlinear variants. The linear variants include the leaky …

A smart grid is a modern electricity system enabling a bidirectional flow of communication
that works on the notion of demand response. The stability prediction of the smart grid …

In real-world datasets, missed data is often expected due to sensor errors, environmental
conditions, communication errors, and other technical limitations. These factors can affect …

Saybolt color is a standard measurement scale used to determine the quality of petroleum
products and the appropriate refinement process. However, the current color measurement …

Operating solar photovoltaic (PV) panels at the maximum power point (MPP) is considered
to enrich energy conversion efficiency. Each MPP tracking technique (MPPT) has its …

Chaos plays a prominent role in nonlinear systems like energy, finance, and weather. In
these systems, a changing parameter to time yields a chaotic time series that contains a lot …

This paper focuses on develo** a prediction model to predict heart stroke using the
parameters, namely, age, hypertension, previous heart disease status, average body …