The geometric model accuracy is crucial for product design. More complex surfaces are represented by the approximation methods. On the contrary, the approximation methods reduce the design quality. A new alternative calculation method is proposed. The new method can calculate both conical sections and more complex curves. The researcher is able to get an analytical solution and not a sequence of points with the destruction of the object semantics. The new method is based on permutation and other symmetries and should have an origin in the internal properties of the space. The classical method consists of finding transformation parameters for symmetrical conic profiles, however a new procedure for parameters of linear transformations determination was acquired by another method. The main steps of the new method are theoretically presented in the paper. Since a double result is obtained in most stages, the new calculation method is easy to verify. Geometric modeling in the AutoCAD environment is shown briefly. The new calculation method can be used for most complex curves and linear transformations. Theoretical and practical researches are required additionally.
In this paper, we present the impact of the data normalization on the classification model performance. In first part of this paper, we present the structure of our dataset, where we discuss the features of the data set and basic statistical analysis of the data. In this research, we worked with the medical data about the patients with the Parkinson disease. In second part of this paper, we present the process of data normalization and the impact of scaling data on the classification model performance. In this research, we used the XGBoost model as our classification model. The main classification task was to classify whether the patient is ill with Parkinson disease or not. Since the data set contains more numerical parameters of different scaling, the main aim of this paper was to investigate the impact of the data normalization (scaling) on the performance of the classification model.