The times of travel agents selecting tourist routes in the office seem to be finally passing. The potential of artificial intelligence (AI) technologies in the tourism industry exceeds the capabilities of traditional search engines and real people. Some travel services have already begun to use elements of artificial intelligence, which help to analyze large volumes of data and learn from their own and other people’s experience of fulfilling customer orders. Currently, the main goal for travel brands is to “learn” using personalized customer experience. Personalized services that are most suitable for a particular client are a strong competitive advantage. It is AI that helps choose such services, since it allows processing a lot of data and creating a personalized product much faster than traditional search technologies
This paper, provided by the authors, focuses on the prospects for the development of wine tourism in the southern regions of the Russian Federation. Unlike a beach holiday, for obvious reasons unpopular in winter, wine tourism is a year-round phenomenon. The most important factor in the successful development of this type of business is familiarizing tourists with the latest technologies in winemaking and viticulture. These technologies are based on digitization of agriculture, the introduction of elements of precision farming, digital mapping and yield planning, parallel driving systems, Internet of Things (IoT), the use of unmanned tractors (combines) and robotics.
This paper presents an analysis of modern methods used to determine antioxidant activity. According to research by the World Health Organization, the deficiency of such important nutrients as antioxidants leads to a decrease in body resistance and the development of chronic diseases. When it comes to diet, the inclusion of foods with a high content of antioxidants helps to increase life expectancy. As a result of this research, the mass concentration of phenolic substances and the antioxidant activity of phenolic antioxidants in young white and red table wine materials were determined using amperometric and chemiluminescent methods in order to determine antioxidant activity. Regression equations reflecting the relationship between the indicator of antioxidant activity and the value of the mass concentration of phenolic substances in young table wine materials were derived. The conversion coefficient for determining the mass concentration of phenolic substances when using Trolox-C and gallic acid as standards was established, which was—3.75. Based on a multiple linear regression model, the total antioxidant activity of the samples (F9.5 = 19.10 and p = 0.0023) can be fairly accurately predicted with an R2 of 0.921 for the calibration data set. A neural network regression model (NNRM) was chosen for the machine-learning regression analysis of the antioxidant activity of the wine samples due to its effectiveness in predicting outcomes in various applications. The implementation was performed using the fitrnet function provided in the Statistics and Machine Learning Toolbox in MATLAB R2021b. The MSE of the calibration model was 0.056; however, the MSE for the three validation samples was much higher, at 0.272.
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