This paper presents the results of a study aimed at developing a method for semantic segmentation of thermal images using a modified neural network algorithm that differs from the original neural network algorithm by a higher speed or processing graphic information. As part of the study, a modification of the DeepLabv3+ semantic segmentation neural network algorithm was carried out by reducing the number of parameters of the neural network model, which made it possible to increase the speed of processing graphic information by 48% – from 27 to 40 frames per second. A training method is also presented that allows to increase the accuracy of the modified neural network algorithm; the accuracy value obtained was 5% lower than the accuracy of the original neural network algorithm.

Keywords: neural network algorithms, semantic segmentation, machine learning, data augmentation

The article discusses the sources and types of data used to create a digital student profile, as well as possible ways of using them in educational analytics. A digital profile is a comprehensive description of a student's academic, behavioral, and social characteristics collected from various sources. The data coming from educational institutions' information systems, social networks, instant messengers, mobile applications, video content platforms, questionnaires, and video cameras are analyzed. The importance of a digital profile is due to its ability to support personalization of learning and improve the efficiency of educational processes. The article highlights numeric, categorical, binary, ordinal, and unstructured data types, as well as metadata and derived data used for data analysis in DataScience and machine learning algorithms. Examples include grades, participation in educational events, social activity, preferences, text comments, and video recordings. Attention is also paid to the analysis of possible ways of using this data to predict academic performance, identify learning difficulties, and assess student engagement and motivation.

Keywords: digital student profile, educational analytics, data types, data sources, data analysis, personalization of learning, machine learning in education, datascience, educational data mining, crisp-dm, semma

The article presents the results of an on-site test spatial metal farm of an aircraft hangar with a span of 72 m. A comparative analysis of the data obtained by the simulation of the farm from the predicted loads (taking into account the actual geometry) and the on-site tests is performed. During the assessment of the technical condition and testing, significant errors were identified in the design and manufacture of gate frames structures,which could lead to loss of bearing capacity or unsuitability for normal operation. Based on the test results, measures were developed and implemented to strengthen the elements of the farm, repeated full-scale tests were conducted. Based on the foregoing, the span structure is recognized as suitable for safe operation.

Keywords: test, metal truss, bolted connections, compliance, bearing capacity, deformability, gate frame, reliability, static calculation