Society has always paid great attention to the problem of human behavior that does not comply with established social and generally accepted norms. Recently, interest in the problem of deviations in human behavior has increased significantly. Today, the study of deviant behavior is an interdisciplinary problem that is being solved within the framework of various scientific disciplines. Recognition of anomalies in human behavior is a complex and currently undisclosed research problem. In the process of identifying behavioral anomalies, the recognition of emotions by various signs plays a leading role. In order to increase the accuracy of the results, it makes sense to perform a comprehensive assessment of emotions on several signs at once, such as facial expression, posture, vocal signs (intonation, speech speed, etc.). The article presents existing algorithms and methods for recognizing emotions. The rationale for the choice of software product development tools is given. The functional requirements for the application are presented in the form of a diagram of use cases in UML 2.0 notation. The architecture of an Android application for recognizing anomalies in human behavior in the form of diagrams of components and classes of the conceptual level is shown. Prototypes of the user interface are demonstrated.

Keywords: abnormal behavior, algorithms and methods of emotion recognition, software architecture, functional requirements, user interface

The article proposes a method of automatic recognition of the type of building for an environmental monitoring system. based on convolutional neural networks. To train the neural network, the Keras library was chosen, containing numerous implementations of the main components of neural networks, such as layers, target and transfer functions, optimizers, and many tools to simplify working with images and text. The processes of network implementation using the Google Colab cloud platform, the preparation of a training set, the training of a constructed neural network, and its testing during training are described. The result of this work is a convolutional neural network model, capable of determining with accuracy of the order of 90-92 percent what type of buildings is shown on the cartographic image, which allows us to automate this process and use it as a subsystem for the environmental monitoring system of atmospheric air.

Keywords: environmental monitoring system for air, building type recognition, convolutional neural networks, machine learning, computer vision