An unsupervised wavelet neural network model for approximating the solutions of non-linear nervous stomach model governed by tension, food and medicine

Abstract

The human stomach is a complex organ. Its role is to degrade food particles by using mechanical forces and chemical reactions in order to release nutrients. All ingested items, including our nutrition, should first pass through the stomach, making it arguably the most crucial segment in the gastrointestinal tract. Computational and mathematical modeling of the stomach is an emerging field of biomechanics where several complex phenomena, such as solid mechanics of the gastric wall, gastric electrophysiology, and fluid mechanics of the digesta need to be addressed. Developing a meshfree comprehensive algorithm for solving the nervous stomach model that enables analysing the relationships between these phenomena remains one of the most significant challenges in biomechanics. This research dedicates to study the dynamics of nervous stomach model governed by a mathematical representation depending on three categories viz. Tension (T), Food (F) and Medicine (M), i.e. TFM model. In this regard, a machine learning paradigm, namely POLYnomial WinOwed with Gaussian (PolyWOG) Wavelet Neural Network (PWNN) model has been implemented for handling the non-linear TFM models. We compared the obtained outcomes of present work with results of a well-known numerical computing paradigm and an existing wavelet neural algorithm. Also, we have done statistical assessment studies at different testing points, which reveal that the proposed architecture is effective and accurate.