Approximation model based on LSTM for predicting the next prime number in an infinite sequence

Prime numbers are a special set of natural numbers that have captured the attention of mathematicians since ancient times. As prime numbers are a fundamental component in many areas of mathematics, they have naturally found wide applications in various fields of knowledge, such as cryptography. The goal of all researchers is to discover the distributional relationships within this infinite set of numbers or, at the very least, to create a mathematical model for predicting the next prime number in a diverging sequence. This article is dedicated to an attempt at solving this problem based on a deep learning model -Long Short-Term Memory (LSTM) neural network

ORGANIZING OF MEASURES FOR PREVENTING THE DELIVERY AND SPREADING OF SEVERE ACUTE RESPIRATORY SYNDROME AT THE TERRITORY OF YAKUTIA

Materials concerning the sanitary protection of the territory of Sakha (Yakutia) from «atypical pneumonia» (SARS) delivery during the epidemic complication in the world are presented in the article. The activity of the region on preventing of SARS delivery by taking of legal and normative documents is shown. Mobilization capacity of the organs and institutions for expanding the measures in case of SARS delivery is reflected, the algorithm of operations is developed

The Latent Dirichlet Allocation (LDA) generative model for automating process of rendering judicial decisions

The Latent Dirichlet Allocation (LDA) generative model is widely used in statistical analysis and machine learning due to its ability to model the probabilities of multidimensional categorical data, such as the frequencies of different categories or the probability distribution across multiple categories. This article explores the potential application of the LDA model for the practical task of topic separation in documents related to judicial proceedings

Ensembling two deep learning algorithms to efficiently solve the problem of predicting volatility in applied finance

Volatility is one of the most commonly used terms in the trading platform. In financial markets, volatility reflects the magnitude of price fluctuations. High volatility is associated with periods of market turbulence and sharp price fluctuations, while low volatility characterizes more relaxed pricing. When trading options, it is especially important for trading firms to accurately predict volatility values, since the price of options is directly related to the profit of a trading firm. A proactive artificial intelligence model that allows predicting volatility for future periods of time will be presented in this article

A mathematical modification of the WNTM associative algorithm for cognitive analysis and efficient detection of dualisms in text and video streaming data

We report on a modification of the WNTM (Word Network Topic Model) algorithm for efficient modelling of bitermes – pairs of words that frequently occur together in texts of different topics. The modified algorithm is an extension of the classical topic model and allows efficient detection and extraction of semantic relations between pairs of words. The paper presents formalized mathematical equations describing the process of modelling biterms, and also presents the results of experiments on real text data, confirming the effectiveness of the proposed approach

Development of the architecture of a transformer-based neural network model to automate delivering judgments in bankruptcy cases

Delivering judgments is one of the brightest examples of solving a creative problem, which implies not only the analysis of data presented in natural language, but also the verification of the compliance of the input information with legal norms and rules. Automation of this process requires the creation of such a language model of machine learning that would allow processing natural language and delivering judgments based on the legal framework, thereby completely replacing the position of a judge. Serious functional requirements are imposed on such an intelligent system, which describe the system of constraints for the architecture of a machine learning model in a formalized mathematical language. This article is devoted to defining the rules for building an applied artificial intelligence model that would automate the process of delivering judgments in bankruptcy cases