ADVANCED TECHNOLOGIES AND COMPUTER SCIENCE

Artificial Intelligence for Complexity Theory

Mirzakhmet Syzdykov — 2023-09-27

In this continued series of work, we present the theoretical and practical results towards reasoning with modern methods of Artificial Intelligence (AI). We justify our methodology with help of illustrative examples from Computer Science relying on the regular expression matching algorithm and application of the proposed solution for the task of identifying files consistency according to the unknown format. We will also give several notable proofs to the classical theorems which in some sense are coherent to the terms like AI and algorithmic complexity, however, or at least, nowadays they’re solved involving the huge amount of hardware resources and together constitute the new formation in the modern age with help of specifically crafter hardware modules – we’re still about to represent the model in more classical understanding from the point of view of computational complexity, concise reasoning and computer logic within the classical models, theorems and proofs as the base approach of estimating the costs needed to build Artificial Neural Networks (ANN) or Machine Learning (ML) data.

ПРИМЕНЕНИЕ ТЕХНОЛОГИЙ МАШИННОГО ОБУЧЕНИЯ ПРИ ДИАГНОСТИКЕ ГЛАУКОМЫ

Кымбат Момынжанова — 2023-09-27

Glaucoma is a progressive eye disease that, if left untreated, can lead to permanent vision loss or total blindness. Early detection and treatment of glaucoma is critical to preventing vision loss. However, diagnosing and treating glaucoma requires extensive testing and regular monitoring, which can be time consuming and costly. Diagnosis of glaucoma is a complex process that requires high accuracy and efficiency in identifying the symptoms of the disease. In recent years, the application of machine learning technologies has become increasingly popular in the field of glaucoma diagnostics. This article provides an overview of recent research that has applied machine learning algorithms to diagnose glaucoma, including classifying glaucoma and predicting its progression. The article highlights the benefits of applying modern machine learning technologies, such as improving accuracy, efficiency and objectivity in the diagnosis of glaucoma. It also describes some of the problems and limitations of these technologies and suggests potential solutions. Overall, the article highlights the potential of deep learning technologies in diagnosing glaucoma and their role in improving patient outcomes.

the METHOD OF CONSTRUCTING MODELS OF INFORMATION ATTACKS

Aliya Barakova — 2023-09-27

The existing approaches to the construction of formal models of information attacks are considered and analyzed. The basic requirements for information attack models are formulated. A method is proposed for constructing models of information attacks based on control E-network transitions and multi-agent management, taking into account the formulated requirements. The considered approaches to modeling allow describing the process of an information attack with varying degrees of detail. The models use different mathematical bases, but most of them are based on finite automata and represent the attack as

a sequence of automaton states. None of the models makes it possible to take into account the vulnerability activated by such a complex, the method of its implementation and possible consequences. The models allow us to more accurately determine the effectiveness of existing

and developed means of protection against simulated information attacks

МАРКОВСКИЕ ИДЕИ В БАЙЕСОВСКИХ СЕТЯХ

Assel Abdildayeva — 2023-09-27

The article deals with Bayesian networks (hereinafter BS) with additional limitations due to the ideas of Markov networks (hereinafter MS). Graph models described by such networks will be called Bayesian Markov networks (BMS). These models describe many real-world problems with different types of uncertainties having different cause-and-effect relationships. The limitations imposed by the Markov property in many cases make it possible to significantly simplify calculations in Bayesian networks in the presence of evidence. At the same time, practice shows that most models reflecting real processes and built on the apparatus of Bayesian networks actually have the Markov property.

The article describes the ideas of calculations in Bayesian networks with restrictions imposed by the Markov property. The differences in calculations in Bayesian networks without Markov constraints and with Markov constraints are described.

The work was written within the framework of grant funding AP19679142 "Search for optimal solutions in Bayesian networks in models with linear constraints and linear functionals. Development of algorithms and programs" (2023-2025) MONV RK.

Operational Calculus of Modified Subset Construction

Mirzakhmet Syzdykov — 2023-09-27

We present the continuation of studying Extended Regular Expression (ERE) on the view of modified subset construction within the overridden operators like intersection, subtraction, and re-written complement. As before we have stated that in this case the complexity has a decreasing nature and tendency. We will give the strict definition of the operational part of this modified subset construction which is due to Rabin and Scott. The complexity of algorithm remains a magnitude less than NP-hard problems for which we have given the strict proof of equivalence in the prior work, so this work continues the studying of the comparable proof for a variety of problems to be computationally complex, however, explainable in terms of unified approach like operational calculus. In this calculus the general points of research are given to the representation of modified subset construction with at least two operands which are to be computed by subset construction and in terms of complexity of the effective algorithm they are computed using modified subset construction.