Mobile applications today are a necessary tool for work, study, entertainment and communication with the entire information world. Every year, the requirements for applications are increasing, as well as the need for stable and multifunctional software tools that will be able to quickly perform the tasks assigned to them. Although most applications require Internet access to provide access to services, it is also necessary to ensure that data is stored on the device itself in order to allow offline access to data. To solve this problem when developing mobile applications, there are many different tools, but the most common is the Room library, which is included in the "Android Jetpack" package. The article provides a brief description of the functionality of this library. The work of all the main components with basic annotations is considered. The interaction of the main components of the library is also schematically presented and an example of implementation in a mobile application for the Android operating system is shown.

Keywords: database, sqlite, Android Jetpack, Room, android

Any mobile application consists of work screens that contain various information. Every year there are more and more requirements for applications. This is due to the constant growth in the number of users of various services that require certain software components for their functioning. The use of primitive development methods, as well as outdated components, leads to an increase in the timing of design, implementation and implementation of the project. But the development of technology does not stand still, which makes it possible to solve the emerging time-consuming tasks in an easier form and much faster. And an example of this is the developed library "The Navigation Component", which allows you to create a convenient and understandable navigation scheme inside the application, and this is simply necessary when a mobile application has many working windows. The article will list the components of the library "The Navigation Component". The main attributes are shown. The principle of operation of each component is considered.

Keywords: navigation diagram of the application, The Navigation Component, application, Android

This article discusses the formation and connection of layers in the task of classifying images of traffic signs, as well as calculating weights on the corresponding layers of the neural network. The authors describe the biological structure of brain neurons, as well as their comparison with artificial neural networks. A conceptual model of an artificial neuron and a neural network with a description of structural elements is presented. The matrix structure of the weights of the neural network is given. The process of converting an RGB image of a road sign into an input layer of a neural network is described. A corresponding description is provided for each hidden layer. In addition, a description of the convolution layers and the maximum pool is given, as well as an explanation of the need to use this type of layers in a convolutional neural network. The authors also described an algorithm for the formation of a convolutional neural network for the classification of road signs. Examples of the operation of this neural network are given.

Keywords: convolutional neural networks, classification, deep learning, big data, mathematical modeling, computer science

This article discusses the problems of determining the organs of air respiration on computed tomography images using convolutional neural networks of the U-NET architecture. The prospects of using neural networks in the analysis of medical images, as well as the use of the U-NET architecture for semantic segmentation of images are described. The structure of an artificial neural network based on the U-NET architecture is being formed. The structure of the layers of this network is visualized and the components of this structure are described. Special attention is paid to the description and implementation of the convolution process. The formula for determining the weight coefficients of the separation boundary is presented. Algorithms for the formation of an artificial neural network model and an algorithm for constructing layers are proposed. A method of increasing data for a training sample of images of medical images is considered. The image of the result of the determination of the chest organs and the corresponding mask are presented.

Keywords: convolutional neural networks, U-NET architecture, deep learning, image recognition, machine learning

This article discusses the problems of constructing convolutional neural networks for determining road objects. The general relevance and formulation of the problem of determining road objects is presented. The rationale for the use of artificial neural networks for determining road objects has been formed. The Retinanet network architecture is used as the main architecture of an artificial neural network for determining road objects. The general concept of this architecture and the main subnets are visualized. Error functions for the main subnets of the Retina net network are described. The design description of algorithms for constructing data annotation for training an artificial neural network, as well as algorithms for constructing the neural network architecture of classification, regression and feature pyramid is given. The dynamics of changes in the general error function when determining road objects is determined. The result of training an artificial neural network is presented.

Keywords: convolutional neural networks, classification, regression, convolutional neural networks, deep learning, big data, mathematical modeling, computer science, RetinaNet architecture

In the process of conducting information activities, a large set of data accumulates, which reflects the specific features of the work performed. The stored information is not always in an orderly and understandable form, which makes it very difficult to work with it. This complicates the analysis, increases the processing time. Neural networks can solve this problem. Today, neural networks are widely used in many fields of activity, due to their application, for example, it becomes possible to analyze the market situation more thoroughly and make appropriate decisions that directly affect success. Thanks to the use of a neural network, it is possible to carry out a set of information in a convenient form for analysis. The article will provide a list of information about the self-organizing Kohonen map, concerning the principles of the neural network. The processing of test data with visualization of maps is considered.

Keywords: Kohonen self-organizing map, Kohonen networks, neural networks, cluster, processing, Self Organizing Map, SOM

Mobile applications are widely used by many people in everyday tasks. Every year there is an increasing need for more functional, convenient and reliable software tools that can provide fast and safe work in various fields. But to develop such an application, it is necessary to use an architecture that meets all the requirements. In this paper, we will talk about the use of components from Android Architecture Components developed by Google, which allow you to implement some design patterns taking into account the features of the Android operating system. The article will provide a list of the most used components, as well as a brief reference on their functionality. The work of one of the components with the basic elements of the Android operating system is considered. The interaction of components is also schematically shown by the example of the implementation of one of the design patterns.

Keywords: application architecture, Android Architecture Components, application, android

In this article discusses the problems of determining traffic signs for driving a motor vehicle using an artificial neural network apparatus. The relevance of research at this point in time is described, as well as the advantages of using neural networks in determining traffic signs. The input data for determining traffic signs for convolutional neural networks are presented. The architecture of the convolutional neural classification network is formed, in particular, the sequence of layers of the image classification network is considered. A mathematical description of the modeling of the error function and the stochastic gradient descent method is given. A mathematical model of the learning process of an artificial neural network, as well as activation functions: linear functions and sigmoids is presented. An algorithm for forming an artificial neural network model is proposed. The learning process of this function is visualized on the graph. The result of the training is presented.

Keywords: artificial neural networks, classification, convolutional neural networks, deep learning, big data, mathematical modeling, informatics