Analysis of the rise in prices for consumer goods is a state’s priority task. The state assumes the obligation to regulate pricing in all spheres of consumption. First of all, the prices for essential commodities to which agricultural products belong are analyzed. The article shows the changes in prices for consumer goods of agricultural products (sugar) during a pandemic. The analysis of forecasting prices for sugar and its impact on the development of its production is carried out. The construction of the forecast model was based on extrapolation. The structure of a forecast model for price changes was based on the analysis of the time series of the Autoregressive Integrated Moving Average (ARIMA) class. This model consists of an autoregressive model and a moving average model. A forecast of the volume of domestic sugar transportation by rail has been completed. The algorithms implemented this model for searching for initial approximations and optimal parameters for the predictive model. The Hirotsugu Akaike Information Criterion (AIC) was used to select the best model. The algorithms were implemented in the Python programming language. The quality check of the description was performed with a predictive model of actual data. An economic interpretation of the rise in sugar prices and proof of the forecast’s truth obtained from a financial point of view were carried out.

The relevance of the study is dictated by the current state in the field of telephone fraud. According to research conducted by Kaspersky Lab, the share of users who encountered various unwanted spam calls in the spring of 2022 was at the level of 71%. The subject of the research is machine learning and deep learning technologies for determining emotions by the timbre of the voice. The authors consider in detail such aspects as: the creation of a marked-up dataset; the conversion of WAV audio format into a numerical form convenient for fast processing; machine learning methods for solving the problem of multiclass classification; the construction and optimization of neural network architecture to determine emotions in real time. A special contribution to the study of the topic is that the authors implemented a fast method of conversion sound formats into numerical coefficients, which significantly increased the speed of data processing, practically without sacrificing their informativeness. As a result, the models were trained by machine learning algorithms quickly and efficiently. It should be particularly noted that the architecture of a convolutional neural network was modeled, which allowed to obtain the quality of model training up to 98%. The model turned out to be lightweight and was taken as the basis for training the model to determine emotions in real time. The results of the real-time operation of the model were comparable with the results of the trained model. The developed algorithms can be implemented in the work of mobile operators or banks in the fight against telephone fraud. The article was prepared as part of the state assignment of the Government of the Russian Federation to the Financial University for 2022 on the topic "Models and methods of text recognition in anti-telephone fraud systems" (VTK-GZ-PI-30-2022).

Introduction. Online learning is applied in the educational system more and more actively every year. The bulk of learning activities in many programmes and courses is shifting from the classroom towards individual work. The interaction between the student and the teacher is decreasing, which negatively affects the quality of education. The aim of the research is the development of methods and algorithms for assessing the psycho-emotional state of learners and their involvement in the teaching process in online education, as well as evaluation of their impact on the efficiency of training. Materials and methods. More than 100 students of Russian State Agrarian University – Moscow Timiryazev Agricultural Academy and Financial University took part in the survey. The following characteristics were measured during the online teaching: involvement in the educational process, students’ psycho-emotional state. In order to build the training algorithms assessing students’ engagement in online learning, the modernised dataset DAiSEE was used; for assessing the psycho-emotional state – the dataset fer2013. Convolutional neural networks were used as model algorithms. The ROC curve and accuracy parameters were used as metrics for model training quality. The Farrar-Glauber test was used for the analysis of multicollinearity of parameters. To prove the efficiency of the developed methods, the statistical criteria – Fisher’s F-test and Student’s t-test – were used. Results and discussion. The developed models demonstrated an excellent quality of training. The accuracy of recognising student engagement in the learning process exceeded 90%, the accuracy of identifying the emotions in online learning was over 90%. The inculcation of the said algorithms in the online learning system showed due efficiency. It was proved that the control group (23 students) and the experimental group (25 students) differed significantly in statistical terms (temp = 2.53; p < 0.05). The learners’ involvement, their emotions and the actual knowledge of the subject area did not show any strong correlation with each other (FGemp=3.61 < FGcrit=7.81). Conclusion. It was proved that the introduction of artificial intelligence systems in the learning process made it possible to adjust the online learning system with regard for the obtained recommendations on the student’s psycho-emotional state and his/her involvement in the training process. Thus, the interaction between the student and the teacher, although weakened in comparison with face-to-face training, nevertheless remains at a sufficiently high level and allows influencing the students’ motivation and mood, which leads to improved quality of education.

Critical information infrastructure (CII), including the financial sector, plays a key role in ensuring the sustainable functioning of economic systems and the financial stability of States. However, the growing digitalization of the financial industry and the introduction of innovative technologies are opening up new attack vectors for attackers. Modern cyber attacks are becoming more sophisticated, and traditional defenses are proving ineffective against new, previously unknown threats. There is an urgent need for more flexible and intelligent cybersecurity systems. Thus, the subject of the study is modern intelligent methods and technologies for protecting critical information infrastructure (CII) from cyber attacks. The object of the research is methods and means of ensuring the protection of critical information infrastructure using artificial intelligence and machine learning technologies. The methodological basis of this study is a comprehensive analysis of the scientific literature on the use of intelligent methods and technologies to protect critical information infrastructure. During the review and critical analysis of relevant scientific publications, key problems and unresolved tasks requiring further scientific research and practical developments in this subject area were identified. This methodological approach allowed us to form a holistic view of the current state and prospects for the development of intelligent cybersecurity tools for critical financial systems, as well as to identify priority areas for further research. The main directions of scientific novelty of this research are: 1. A detailed review of promising approaches based on artificial intelligence and machine learning technologies to ensure effective protection of CII organizations from modern complex cyber attacks. 2. Identification and analysis of a number of key scientific and technical problems that need to be solved to increase reliability, interpretability and trust in intelligent cybersecurity systems, including issues of robustness to attacks, active online learning, federated and differential private data processing. 3. Identification of promising areas for further research and development in the field of application of specialized methods of secure and trusted AI to protect critical financial infrastructure. Thus, this research makes a significant contribution to the development of scientific and methodological apparatus and practical solutions for the use of intelligent methods to ensure cybersecurity.