Overview and investigate time complexity of computer vision algorithms for face recognition. Main article idea is to compare two popular computer vision librarieobjs, they are OpenCV and dlib, explore features, analyze pros and cons each of them and understand in what situation each of them suit the best. Method. The technologies of computer vision, which are used for face recognition was worked out. Research of two popular computer vision libraries was conducted. Their features are analyzed and the advantages and disadvantages of each of them are estimated. Examples of building recognition application based on histogram-oriented gradients for face finding, face landmark estimation for face orientation, and deep convolutional neural network to compare with known faces. The article generalizes the concept of face recognition. The scientific basis for facial recognition and the construction of a complete recognition system was described. The basic principles of the programs for face recognition are formulated. A comparative analysis of the productivity of both libraries in relation to - the time of execution to the number of iterations of the applied algorithms was presented. Also built two simple applications for face recognition based on these libraries and comparing their performance.

This paper is about existing problems of concerning precise forecasting of clinical consequences of a certain type of disease which are analyzed. It is shown that existing systems of machine learning are differently effective and have automated training, assessment and interpretation of deep learning models. The choice of software SurvivalNet (SN) that allows users to train and interpret deep survival models is substantiated.

In this paper the initialization of the city is considered, which consists of several steps, including the creation of city objects with their locations, creation of residents with their attributes and own daily schedules, etc. A description of the model is provided as a tuple of attributes. The adequacy of the simulation model is checked based on the statistical data from the city of Lviv, Ukraine. Generated locations of city ecosystem objects are presented. The daily schedule of residents is simulated. A possible work schedule for each specialty is given, and separate schedules are created for working days and holidays. A unique schedule is predicted for the resident, which depends on their age and work specialty. The dynamics of visits to facilities by residents on weekdays and at weekends are analyzed. Based on the conducted experiments, the adequacy of the model and its realistic reflection of the functioning of the city's ecosystem during the day are proven. It means that by using this model, researchers can assess the impact of different behavioral scenarios on the residents within the city ecosystem more reliably. This enables a better understanding of how certain actions or changes in behavior can affect the spread and control of diseases in a specific geographic area. This model has the potential to serve as a foundation for future modeling of systems at the medium and macro scales.

Concepts of data mining, database and data warehouse are reviewed in this article. An analogy between data warehouse and database are drawn. Attributes of data warehouse, its architecture and work mechanisms are described. Data warehouse architecture is highlighted. Concept of meta data is pointed out. Approaches and variations of data mining are reviewed. Tools and methods of data mining are described. Approaches to methods of data analysis are summarised. Analysis of data mining methods is submitted.