The use of deep learning algorithms for the classification of crop diseases is one of the promising areas in agricultural technology. This is due to the need for rapid and accurate detection of plant diseases, which allows timely measures to be taken to treat them and prevent their spread. One of them is to increase productivity and maintain land quality through the timely detection of diseases and pests in agriculture and their elimination. Traditional classification methods in machine learning and algorithms in deep learning were compared to note the high accuracy in detecting pests and crop diseases. The advantages and disadvantages of each model considered during training were taken into account, and the Inception V3 algorithm was incorporated into the application. They can monitor the condition of crops on a daily basis with the help of new technology-applications on gadgets. Aerial photographs used by research institutes and agricultural grain centers do not show the changes that occur in agricultural grains, that is, diseases and pests. Therefore, the method proposed in this paper determines the types of diseases and pests of cereals through a mobile application and suggests ways to deal with them.

The presented scientific article is a comprehensive study of machine learning and deep learning methods in the context of emotion recognition in text data. The main goal of the study is to conduct a comprehensive analysis and comparison of various machine learning and deep learning methods to classify emotions in text. During the work, special attention was paid to the analysis of traditional machine learning algorithms, such as multinomial naive Bayes (MNB), multilayer perceptron (MLP), and support vector machine (SVM), as well as the use of deep learning methods based on long short-term memory (LSTM). The experimental part of the study involves the analysis of different data sets covering a variety of text styles and contexts. The experimental results are analyzed in detail, identifying the advantages and limitations of each method. The article provides practical recommendations for choosing the optimal method depending on the specific tasks and context of the application. The data obtained is important for the development of intelligent systems that can effectively adapt to the emotional aspects of interaction with users. Overall, this work makes a significant contribution to the field of emotion recognition in text and provides a basis for further research in this area.

Currently, there is a need to improve the educational system and develop interdisciplinary studies at all stages of education, from school to postgraduate education. The implementation of cross-curriculum connections promotes a holistic view of natural phenomena and the relationship between them, that is, this knowledge becomes more meaningful and applicable in practice. The article proposes an approach to building a conceptual model of the content of education in the form of a thesaurus and ontology, the use of which will ensure that the educational information is adaptively selected and put straight. The article discusses the possibility of and experience in using ontological modeling and engineering for the conceptual description of school and higher education. The article discusses the development of the ontological science, technology, engineering, and mathematics (STEM) Education at School model. The article builds an ontological model, which is an integration of the ontology of school and the ontology of university. When filling in the knowledge base, it becomes possible to identify interdisciplinary relationships. The use of ontological engineering methods will improve the quality of education for schoolchildren and students through the semantic description of knowledge of the subject area by using interdisciplinary and STEM approaches in the process of education.

Currently, there is a need to improve the education system and develop interdisciplinary research at all levels of education, from school to postgraduate education. The introduction of interdisciplinary connections contributes to the formation of a holistic understanding of natural phenomena and the connections between them. Thus, this knowledge becomes more meaningful and applicable in practice. This article proposes a conceptual model of the content of education in the form of a thesaurus and ontology. The use of these models will allow you to adaptively select and systematize educational information. The article also discusses the possibilities and experience of using ontological modeling and engineering for the conceptual description of school and higher education. In addition, the article discusses the development of an ontological model of the process of expanding teachers’ competencies with the integration of science, technology, engineering and mathematics (STEM) education. The use of ontological engineering methods will improve the quality of teacher education through the semantic description of knowledge in the subject area and the use of interdisciplinary and STEM approaches in the educational process.

Recommendation systems play a key role in the digital environment, providing personalized recommendations in online stores, streaming services, social networks, and educational platforms. This paper presents a comprehensive review of recommendation system models, including content and collaborative filtering, hybrid approaches, and state-of-the-art algorithms based on deep learning, reinforcement learning, and graph neural networks. The advantages and disadvantages of different methods, their accuracy, performance, scalability and adaptability to new data are analyzed. The main challenges such as the cold-start problem, data sparsity, bias of algorithms, the need for explainability of recommendations and privacy assurance are reviewed. Special attention is paid to the prospects of implementing recommendation systems in educational platforms. The importance of using hybrid and intelligent systems to effectively analyze user data and build recommendations tailored to individual needs is emphasized. The conclusion is drawn about further development of recommendation systems, which will be associated with the integration of the latest artificial intelligence technologies, optimization of computational resources and expansion of their application area in various digital ecosystems. The work can be useful for researchers, developers and practitioners working in the field of artificial intelligence and educational technologies.

The article deals with construction of the ontological model to provide knowledge transfer between universities. It discusses a need to consider requirements of the professional standard for a specialist and labor market demand in process of educational programs design. Ontological and modular-competence approaches were used to simulate the mapping of competences. An ontological model of the professional competence standard was developed. The information model distributed knowledge database in the university consists of the ontology educational programs, the ontology of professional competencies demanded in the regional labor market and the ontology of scientific knowledge in the university. Classes taxonomy of the developed ontological model of professional competences is presented. The method of analyzing hierarchies was used to assess the conformity of learning outcomes with the functional requirements of labor market.