The sustainable functioning of the transport system requires solving the problems of identifying and classifying road users in order to predict the likelihood of accidents and prevent abnormal or emergency situations. The emergence of unmanned vehicles on urban highways significantly increases the risks of such events. To improve road safety, intelligent transport systems, embedded computer vision systems, video surveillance systems, and photo radar systems are used. The main problem is the recognition and classification of objects and critical events in difficult weather conditions. For example, water drops, snow, dust, and dirt on camera lenses make images less accurate in object identification, license plate recognition, vehicle trajectory detection, etc. Part of the image is overlapped, distorted, or blurred. The article proposes a way to improve the accuracy of object identification by using the Canny operator to exclude the damaged areas of the image from consideration by capturing the clear parts of objects and ignoring the blurry ones. Only those parts of the image where this operator has detected the boundaries of the objects are subjected to further processing. To classify images by the remaining whole parts, we propose using a combined approach that includes the histogram-oriented gradient (HOG) method, a bag-of-visual-words (BoVW), and a back propagation neural network (BPNN). For the binary classification of the images of the damaged objects, this method showed a significant advantage over the classical method of convolutional neural networks (CNNs) (79 and 65% accuracies, respectively). The article also presents the results of a multiclass classification of the recognition objects on the basis of the damaged images, with an accuracy spread of 71 to 86%.

In the article the avatar-based learning and teaching (A-BL&T) as a concept of control and managing knowledge in modern socio-economic conditions is proposed to use for assessment a university's economic efficiency. It is shown that all elements, methods and techniques (tools) do not operate in isolation, but rather are interrelated, complementing each other. All of them are used in the process of management and, in a combination, are powerful tools for increasing efficiency of management. Based on the example of avatar-based learning and teaching in Russian universities as modern educational environments, a conceptual model, methodology, and methods have been obtained for the automation of planning and calculation of the academic load in the university.

This chapter is about study results of use of avatar-based management techniques for breaking down language and cultural barriers in modern Russian corporations. In the chapter, specially developed avatar-based management technique mixed with RTI—respond to intervention—is used in USA for education process correction. The authors of the chapter developed the innovation tools and based the results analysis of the deliberations of citizens in social networks on topics related to online services. Intelligent visualization techniques for big data analytic was used with avatar-based management techniques introduced by Professor Mkrttchian in 2017 in his book Sliding Mode in Intellectual Control and Communication: Emerging Research and Opportunitie”, published by IGI Global and Indexed by SCOPUS.