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.

Short-Term Load Prediction (STLP) is an important part of energy planning. STLP is based on the analysis of historical data such as outdoor temperature, heat load, heat consumer configuration, and the seasons. This research aims to forecast heat consumption during the winter heating season. By preprocessing and analyzing the data, we can determine the patterns in the data. The results of the data analysis make it possible to form learning algorithms for an artificial neural network (ANN). The biggest disadvantage of an ANN is the lack of precise guidelines for architectural design. Another disadvantage is the presence of false information in the analyzed training data. False information is the result of errors in measuring, collecting, and transferring data. Usually, trial error techniques are used to determine the number of hidden nodes. To compare prediction accuracy, several models have been proposed, including a conventional ANN and a wavelet ANN. In this research, the influence of different learning algorithms was also examined. The main differences were the training time and number of epochs. To improve the quality of the raw data and remove false information, the research uses the technology of normalizing raw data. The basis of normalization was the technology of the Z-score of the data and determination of the energy‒entropy ratio. The purpose of this research was to compare the accuracy of various data processing and neural network training algorithms suitable for use in data-driven (black box) modeling. For this research, we used a software application created in the MATLAB environment. The app uses wavelet transforms to compare different heat demand prediction methods. The use of several wavelet transforms for various wavelet functions in the research allowed us to determine the best algorithm and method for predicting heat production. The results of the research show the need to normalize the raw data using wavelet transforms. The sequence of steps involves following milestones: normalization of initial data, wavelet analysis employing quantitative criteria (energy, entropy, and energy‒entropy ratio), optimization of ANN training with information energy–entropy ratio, ANN training with different training algorithms, and evaluation of obtained outputs using statistical methods. The developed application can serve as a control tool for dispatchers during planning.

This paper aims at the time-series data analysis. We propose the possibility of adding additional features to the existing time series data set, to improve the prediction performance of the prediction model. The main goal of our research was to find a proper method for building a prediction model for the time-series data, using also machine learning methods. In this phase of research, we aim at the data analysis and proposal of the ways to add additional features to our dataset. In this paper, we aim at adding derived parameters from one of the original features. We also propose incorporating LAG’s into the dataset as new features, to enhance the prediction performance on the time series based data.

Dealing with data from the field of medicine is nowadays very current and difficult. On a global scale, a large amount of medical data is produced on an everyday basis. For the purpose of our research, we understand medical data as data about patients like results from laboratory analysis, results from screening examinations (CT, ECHO) and clinical parameters. This data is usually in a raw format, difficult to understand, non-standard and not suitable for further processing or analysis. This paper aims to describe the possible method of data preparation and preprocessing of such raw medical data into a form, where further analysis algorithms can be applied.

This article is devoted to the initial phase of data analysis of failure data from process control systems. Failure data can be used for example to detect weak spots in a production process, but also for failure prediction. To achieve these goals data mining techniques can be used. In this article, we propose a method to prepare and transform failure data from process control systems for application of data mining algorithms, especially cluster analysis.