Improvement of Electrical Tomographic Imaging of Moisture by Mixing Machine Learning Models

The article proposes the optimization of the selection of machine learning algorithms used in tomographic applications. This study used electrical impedance tomography (EIT) to illustrate the distribution of moisture inside the walls of the buildings under study. The first task is to discover the ideal settings of hyperparameters used in machine learning algorithms to increase the efficiency of obtaining reliable tomographic images. The second aim of the research is to choose the optimal method of converting measurements into images. The process of turning input observations into output photos is handled by machine learning models. This is called an ill-posed problem or an inverted problem that is difficult to solve because there are not enough arguments. Ensuring the selection of the correct model hyperparameters is an essential task of machine learning. The selection of these hyperparameters has a direct impact on the quality of the reconstruction. Using the k-nearest neighbors algorithm as an example, this article shows how hyperparameter optimization can be applied to regression and classification models. This technology was created to track and visualize the distribution of moisture inside the walls of buildings and other structures. The facts revealed during the investigation showed that the proposed techniques are effective.