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• ANALYSIS OF ARTIFICIAL INTELLIGENCE METHODS APPLIED TO SOLVING PSYCHIATRY PROBLEMS

  E.S. Podoplelova

  2022-05-26

  Abstract ▼

  The use of artificial intelligence methods in the field of medicine has become widespread,
  helping to diagnose, analyze and make recommendations for treatment. Psychiatry is a branch of
  medicine that studies mental disorders, methods for their diagnosis and treatment. Her range of
  tasks includes not only diagnosis and treatment, but also observation, monitoring and subsequent
  rehabilitation of patients. This subject area has significant problems, such as objectivity, inconsistency
  in the diagnosis, the complexity of the classification of diseases, and the unpredictability
  of the course of the disease. With a number of these problems, the use of machine learning methods
  and artificial intelligence algorithms helps to cope. This paper is devoted to a review of research
  on artificial intelligence methods used to solve problems in the field of psychiatry.
  The relevance of the topic is due to the high need for improvements in this subject area. Specific
  issues are presented in this article. Among them, the main directions were identified: data deidentification,
  classification of symptom severity, accuracy of condition prediction. To solve them,
  the authors used such methods as latent semantic analysis for natural language processing, classification
  methods, convolutional neural networks for prediction, and cognitive modeling. Separately,
  the effectiveness of hybrid systems, including the implementation of several machine learning
  methods at once, is noted. The aim of the study was to highlight the main directions of development
  of research in the scientific community, which demonstrate the successful integration of artificial intelligence into psychiatry, as well as to compare them with each other according to the
  obtained estimates of the accuracy of the models. Which, in turn, implies the analysis and analysis
  of specific algorithms, their performance for specific tasks

• CONSTRUCTION OF AN OPTIMAL CONTROL TRAJECTORY IN AN INTELLIGENT SYSTEM IN THE ABSENCE OF OBSERVABLE VARIABLES

  А.N. Tselykh , V. S. Vasilev , L.А. Tselykh , Е.S. Podoplelova

  224-233

  2025-07-24

  Abstract ▼

  Constructing optimal control in the complete absence of data on the system dynamics is a pressing problem. In this paper, we propose a solution to a finite-horizon linear quadratic problem (LCP) for a time-invariant system with a graph dynamics matrix. Unlike the control problem, stability and complete controllability of the system are not assumed. The construction of the control trajectory is controlled by the direction of increase in the change in the state of variables over a small number of steps, which is determined by the conditional principal eigenvector of the adjacency matrix of the graph model. The solution of classical optimal control is carried out in an autonomous mode and requires complete knowledge of the system dynamics. In the absence of complete knowledge of the system dynamics, solving optimal control problems for systems with uncertainty, including discrete linear systems, has attracted considerable interest in recent years. The main approach when complete information about the system is unavailable is the design of optimal control, in which the system parameters are initially determined, and then an algebraic equation in the dual space is solved. An important difference from the standard discrete control problem is that the control model was modified to estimate changes in the state of variables under controls transmitted through the dynamics matrix. The proposed algorithm using a graph matrix implements recurrent calculations of dynamic and adjoint equations, as well as the Powell method for solving a system of linear algebraic equations (SLAE). The authors introduced a new interpretation of the mathematical construction of the system dynamics matrix in a standard discrete control problem on a finite time interval, which can be used to design any controlled dynamic system with unobservable parameters.

• FAILURE PREDICTION USING FACTOR ANALYSIS METHODS

  Е.S. Podoplelova

  213-223

  2025-07-24

  Abstract ▼

  This article discusses the application of a risk assessment method based on the combination of the FMEA (failure mode and effect analysis) methodology and the MCDM (Multiple Criteria Decision Making) methods. This approach allows taking into account both expert knowledge and historical data on the operation of the equipment. MCDM methods process the assessment more flexibly in comparison with the standard method of calculating the priority number of risks (PRN), which helps to better assess the risks by three criteria: the probability of occurrence, the complexity of detection and the severity of the consequences. One of the criteria can be obtained not only through an expert assessment, but also on the basis of data recording the operation of the equipment. This approach was tested using the example of synthetic open-source data on the operating modes of production equipment. The task was to predict both the failure itself and its type, as well as to identify the factors that have the greatest impact on the failure. For this purpose, data preprocessing was carried out, during which it was necessary to eliminate the imbalance of classes. There are several approaches to solving this problem, aimed at reducing the dominant class or generating instances of poorly represented classes. In this example, random reduction of the number of records without errors was used. Then, AdaBoost, Random Forest and LinearSVC were compared as classification algorithms. Since multi-class classification was required, it was decided to use the one-vs-the-rest strategy. As a result, it was possible to achieve 86% forecasting accuracy by F-measure using the AdaBoost and Random Forest algorithms. LinearSVC turned out to be ineffective. Thus, the resulting forecasting model recognizes different types of errors, but there is room for improvement, which requires a larger sample, including more examples with different types of failure. Based on this, this approach as an alternative to expert assessment is promising, improving objectivity, and also making it possible to foresee risks and prevent a real failure or risk-related incident.

• MODIFICATION OF THE FMEA METHOD USING MACHINE LEARNING ALGORITHMS

  Е.S. Podoplelova, I.I. Knyazev

  2024-01-05

  Abstract ▼

  Risk assessment is an important task in any field, from manufacturing to medicine. Risks accompany
  a project, product or process throughout its life, from the moment of planning until its
  complete termination. Each of them has its own approaches. These include FMEA (Failure Mode
  and Effects Analysis) - analysis of the types and consequences of failures. The proposed model is
  based on the FMEA method, which is based on risk assessment according to three criteria: the
  severity of the consequences when a threat is realized and the complexity of identifying a failure,
  the probability of occurrence. The first two criteria are based on expert assessment obtained in
  accordance with artificial intelligence methods. The authors proposed a modification of the third
  criterion. In our work, we replaced the expert assessment of the “probability of occurrence” criterion
  with a machine learning model capable of predicting this indicator based on statistical data.
  We carried out the first stage of research into the task at hand on NASA’s open dataset about engine
  operating cycles before failure. Initially, the task was set to predict the remaining number of
  cycles before failure, then we moved to the classification task, determining whether the equipment
  is at risk, depending on its potential remaining life. The best result was obtained by the support
  vector machine (SVM), with a classification accuracy of 80%. The goal of the work is to create a
  risk assessment model based on the FMEA methodology, which allows to improve the quality of
  assessment, reduce subjectivity in decision making, making a forecast based on historical data,
  and not just the subjective experience of an expert.

• SELECTION OF MULTI-CRITERIA ANALYSIS METHODS ON THE EXAMPLE OF THE PROBLEM OF RANKING

  Е.S. Podoplelova

  2023-08-14

  Abstract ▼

  This work is devoted to the selection and comparison of popular traditional methods of multi-
  criteria decision making. The article presents an overview of the existing works of recent years
  on the topic of their comparison, highlights the main criteria, as well as the most significant results.
  Further, an example of the implementation of a DSS (decision support system) was considered
  on the recommendation of such a method to the user, which includes a description of not only
  the main methods, but also their modifications, highlighting an exhaustive taxonomy of multicriteria
  analysis methods in general. For the selection of methods in this article, international
  databases of scientific publications were used: Science Direct, Google Scholar and IEEE Xplore.
  Certain search settings have been made to retrieve jobs that match the query. The next step describes the task of ranking alternatives to demonstrate the results of applying the selected methods.
  As a method for distributing the weights of the criteria, the method of analysis of hierarchies
  (AHP) was used. The calculation results are presented in tables and graphically. The evaluation
  metric was considered to be the stability of the method to the number of alternatives and criteria,
  as well as sensitivity to the weights of the criteria. At the current stage of the study, the following
  methods were selected: TOPSIS, WASPAS, VIKOR, PROMETHEE and ELECTRE. As a result of
  the study, optimal methods were determined (in terms of the ratio of computational complexity to
  stability) for their further use in the development of DSS, the ELECTRE method was used as an
  additional tool with a large number of alternatives to screen out the least attractive ones.
  PROMETHEE showed high sensitivity to changes in weights and complexity of calculations, therefore
  it was excluded from further development stages. VIKOR and TOPSIS showed the best stability
  with the simplicity of calculations.