Development of Trace Transform using Machine Learning for reducing the tracing line calculation

An important approach presented in this paper is to apply the idea of machine learning to improve the visual processing of the trace transform algorithm by using machine learning to predict the answer. In terms of calculating the results of each tracing line, each such tracing line must be calculated to find only one solution from a single equation, which will have different forms of complex equations. There are 22 basic equations options to choose from, the results of which can effectively reduce the overall runtime of the trace transform. The idea in this research can reduce the complexity of trace's algorithm by applying the predictive results obtained from training data into the machine learning system to create a model for implementation. The training model will be used to predict the results which will replace the original function calls for repetitive calculations and use only forecasting.