No abstract

The tasks that unmanned aircraft systems solve include the detection of objects and determining their state. This paper reports an analysis of image recognition methods in order to automate the specified process. Based on the analysis, an improved method for recognizing images of monitored objects by a convolutional neural network using a discrete wavelet transform has been devised. Underlying the method is the task of automating image processing in unmanned aircraft systems. The operability of the proposed method was tested using an example of processing an image (aircraft, tanks, helicopters) acquired by the optical system of an unmanned aerial vehicle. A discrete wavelet transform has been used to build a database of objects' wavelet images and train a convolutional neural network based on them. That has made it possible to improve the efficiency of recognition of monitored objects and automate a given process. The effectiveness of the improved method is achieved by preliminary decomposition and approximation of the digital image of the monitored object by a discrete wavelet transform. The stages of a given method include the construction of a database of the wavelet images of images and training a convolutional neural network. The effectiveness of recognizing the monitored objects' images by the improved method was tested on a convolutional neural network, which was trained with images of 300 monitored objects. In this case, the time to make a decision, based on the proposed method, decreased on average from 0.7 to 0.84 s compared with the artificial neural networks ResNet and ConvNets. The method could be used in the information processing systems in unmanned aerial vehicles that monitor objects; in robotic complexes for various purposes; in the video surveillance systems of important objects

The subject matter of the research is the models and technologies of analyzing the processes of developing the loyalty and preferences of social networks users. The goal of the research is to increase the efficiency of marketing analysis of clients' preferences and promoting products and services. The following tasks are solved in the article: the analysis of the methods and technologies of simulation modelling; the survey of available simulation packages; designing a datalogical context chart; developing the agent-based model of the impact of social networks on preferences; developing a database for data storage; a graphical analysis of preferences. The following research methods are usedsimulation modelling methods, Laravel and YouTube Data and Analytics API methods. The following results were obtained: the model that explains how advertising affects the development of clients' preferences, as well as the impact of clients' communication on loyalty, the information system for the graphical analysis of Wow-How Studio of YouTube channel The model that reveals the impact of advertising and clients' communication was developed. The suggested model proves the fact that the communication of social networks users greatly increases a number of actual clients as well as the level of loyalty. Using such technologies as Laravel and YouTube Data API, the designed application enables clear and timely monitoring and analyzing Wow-How Studio channel, which is very important to be always aware of the preferences of potential clients and to know what can be interesting for them and how to meet their expectations. The results can be implemented in enterprises of various spheres that offer their products and services. Any company is interested in obtaining the maximum profit and integration into social networks is free advertising and as a result attracts additional profit.

The subject matter of the article is semantic networks of distributed search in e-learning. The goal is to synthesize a decision tree and a stratified semantic network that allows network intelligent agents in the e-learning to construct inference mechanisms according to the required attributes and specified relationships. The following results are obtained. The model of the base decision tree in elearning is suggested. To simulate the decision tree in e-learning, the logic of predicates of the first order was used, which enabled making calculations both at the nodes of the tree and at its edges, and making decisions based on the results of calculations; applying partitioning operations to select individual fragments; specifying the solutions with further expanding the inference upper vertices; expanding the multi-level model vertically and horizontally. At the first stage of the model formalization, the graph of the base decision tree was constructed, whose nodes represent a substructure capable of performing an autonomous search subtask. The second stage is filling the base tree with semantic information and organizing its interaction with network intelligent agents. To provide the tree branches of decisions in e-learning with information, the process of stratified expansion of the base decision tree was suggested where the components of the decision node were detailed and the links among the received sub-units were established both on the horizontal and on the vertical levels. It is shown that in order to establish a set of goals and search problems on the studied structure, it suffices to determine: the graphs of goals and search problems for each node type; a set of edges that determine the dependence of the execution of search targets for the nodes that are not of the same type; a set of pointers that establish probable relationships for redistributing resources in accordance with the requirements of intelligent agents; communication mapping. The developed mathematical model of the base decision tree enabled a stratified expansion. Determining intensions and extensions allowed stratified semantic networks to be used for searching. Conclusions. The method of synthesizing a decision tree and a stratified semantic network is suggested; this method enables considering them as closely interrelated ones in the context of distributed search in elearning. As a result, the process of searching and designing inference mechanisms can be formalized by the network intelligent agents according to the required attributes and given relationships.