Energy consumption is related to all types of human economic activity. Energy production significantly affects the state of the environment; therefore, high quality planning of electricity consumption is a priority issue in the development strategy of any country. All responsibilities for planning electricity consumption and purchasing electricity fall on energy supply companies. High-quality planning of electricity consumption will lead to a reduction in the costs of energy supply companies, which will have an impact on reducing the costs of creating final products for the average consumer. This article is devoted to the problem of increasing the efficiency of energy supply companies to meet the needs of household consumers. The work substantiates the necessity of planning electricity supplies in the necessary volume to ensure the rational use of energy and economic resources. The problem of performing forecasting by a person is described, when the result depends both on the analyst's qualification in the subject field and on his qualification in analysis methods. Arguments are presented regarding the use of modern methods of artificial intelligence - neural networks, which have the ability to learn on a set of data and allow to reveal hidden relationships and regularities between data. The article provides an analysis of the latest research and publications on the use of artificial neural networks in energy forecasting, which confirmed the feasibility of using the chosen method for a given problem. The process of using a multilayer neural network for forecasting the volume of electricity sales to consumers is described. The nature of the data describing the volume of monthly hourly electricity purchases for household consumers is given, an example of a time series is shown. The peculiarities of the input parameters for the neural network are determined: month of the year, day of the week, type of day (holiday or working), the average maximum value of hourly electricity purchase

Anemia is considered one of the most common diseases that affect the human body. In case of prolonged existence, it can lead to the development of chronic diseases due to excessive load on the vital organs. Effective treatments are available with early detection of the disease and largely depend on its underlying cause. In terms of severity, anemia can be divided into mild, moderate, and severe according to the level of hemoglobin. The morphological classification provides for the division of anemia into microcytic, normocytic, and macrocytic depending on the average volume of red blood cells. According to the hemoglobin, content anemia can be hypochromic, normochromic and hyperchromic. The article explores scientific and technical information on classification and fuzzy logic as well as describes the development of a decision support system for anemia diagnosis using a fuzzy logic inference model. During the analysis of existing literature, it was found that the use of naive Bayesian methods is optimal for solving the problem of determining anemia. But there is a possibility of minor errors in measuring features, so it is necessary to use the theory of fuzzy sets. Modulation of fuzzy logic inference was implemented using the Mamdani algorithm. The developed information system has two levels. The first level determines the severity of anemia. The second level is responsible for belonging to one of the classes: microcytic hypochromic, normocytic normochromic, and macrocytic. The software was written in Java on the basis of its own library to implement a computational algorithm for morphological classification of anemia and classification by severity according to the indicators of hematological laboratory tests. The application of the proposed architectural design of the system saves time for decision-making and eliminates the need for additional manpower to solve the problems of information and analytical support for management decision-making in support of business processes of anemia monitoring

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The article reveals the topic of developing an expert system of psychoneurological diseases using the method of differential diagnostics. The task of the system of differential medical diagnosis is to determine the diseases that the patient may suffer from, based on the observation of his symptoms. The method used in diagnosing the disease is differential. This method weeds out diseases because they do not match any facts or symptoms, which in the end must lead to the only possible disease. The developed and implemented expert system includes: a mechanism for accessing the database of symptoms for each of the correlating diseases, an algorithm for forming ES input parameters, a decision-making method based on a logical mechanism. Various decision-making mechanisms were investigated and analyzed in the work, which made it possible to avoid shortcomings and improve the work of the expert system. ES work relies on a knowledge base of symptoms. The database is a collection of differential diagnostic features, corresponding frequencies of occurrence for each of the diagnosed diseases. The application has two modes of operation: the mode of operation with the knowledge base, which provides direct work with the database and support for all necessary operations for the full functioning of the system. The mode of analysis in which the specialist receives support in making a decision when making a diagnosis. The user sets the patient's existing symptoms, after which the managed data is processed. At the end, the user receives the result of the performed analysis with the most probable diagnosis to the least probable one with a calculated conformity assessment. Such a system has high efficiency, reliability, accessibility and productivity. The use of such a system allows you to avoid redundancy of information, thereby reducing the time for primary data processing