A. E. Yankovskaya

Investigation and development of an intelligent system for the diagnostics and intervention of organizational stress

The development of an intelligent system for the diagnostics and intervention of organizational stress (DIOS) is devoted in this paper. This system is described and the results of its investigation are given. The DIOS system is based on recognition of the description of a subject under investigation (a respondent) by applying threshold logic and soft computing techniques, as well as means of cognitive visualization of information structures and justification results for diagnostic and intervention decisions. This paper also presents an algorithm for decisions-making on the diagnostics and intervention of organizational stress. This algorithm is implemented in the DIOS intelligent system. Further ways to improve the DIOS intelligent system are discussed in this paper.

Parallel algorithm for constructing k-valued fault-tolerant diagnostic tests in intelligent systems

A parallel algorithm for constructing k-valued fault-tolerant diagnostic tests is described. This algorithm combines two algorithms, viz. a parallel algorithm for constructing an irredundant implication matrix designed to distinguish objects from different patterns and a parallel algorithm for constructing irredundant h-fold column coverings. The IMSLOG intelligent instrumental software (IIS), on the basis of which we construct intelligent systems for various disciplines is described. A sufficient condition for constructing diagnostic tests tolerant to the given number of measurement (entry) errors of values of characteristic features of the object under investigation is applied to ensure fault-tolerance. Suggestions for further research are given.

Knowledge and data acquisition and processing in solving biomedical problems

We describe an original method for knowledge and data acquisition and processing in solving biomedical problems, including, in particular, diagnosis of depression. This method underlies for an intelligent system that is developed for diagnosis and prevention of depression based on logical—combinatorial test methods for pattern recognition. The intelligent system is distributed and includes two subsystems. The first subsystem is implemented as a web application and incorporated into a complex of intelligent systems for prevention of mental and behavioral disorders. The second subsystem is implemented as a dynamic plug-in to the intelligent instrumental software IMSLOG based on the revealing of different kinds of regularities, logical—combinatorial and logical—combinatorial—probabilistic methods for pattern recognition, and cognitive tools for decisionmaking support and its justification. Applied intelligent systems are being developed on the base of the intelligent instrumental software IMSLOG. We also present a description of our proposed intelligent system.

Application of information systems in road-climatic zoning

The article is devoted to the problem of organization and integration of information streams when making territorial road-climatic zoning taking into account geocomplexes of zonal and intrazonal character. The scheme of data acquisition and processing at the stages of allocation of zones, subzones, and road districts is shown. The relevance of the article is due to insufficient consideration of peculiarities of climate conditions while designing of highways in newly reclaimed regions of Russia, for example, Siberia and the Far East.

Structurization of Data and Knowledge for the Information Technology of Road-Climatic Zoning

The article is devoted to structurization of data and knowledge for the information technology of road-climatic zoning (IT RCZ). The IT includes an intelligent system based on the extended matrix model of data and knowledge representation on the investigated area of the West Siberian region and the matrix model of knowledge representation preassigned by experts in the field of RCZ without specification of particular territories. Structurization is conditioned upon the extended matrix model.

Mixed diagnostic tests building from an optimal unconditional test subset in intelligent pattern recognition

In intelligent pattern recognition, it is proposed that mixed diagnostic test (MDT) building be conducted from an optimal subset (OS) of unconditional irredundant diagnostic tests (UIDT). Decision making on each MDT is performed in the course of MDT tree building. The criteria of considering the sequence for UIDT as well as features involved in each of them are stated, which results in enumeration reduction and, as a rule, allows MDT building from not all UIDT in an UIDT OS. A matrix model of data and knowledge representation, a method and an algorithm of the MDT tree building, an illustrative example, and the ways of algorithm development are cited.

Construction of hybrid intelligent system of express-diagnostics of information security attackers based on the synergy of several sciences and scientific directions

This paper is devoted to construction of a hybrid intelligent system of express-diagnostics of possible information security attackers (HIS DIVNAR) based on a synergy of several sciences and scientific directions: test pattern recognition; discrete mathematics; threshold and fuzzy logic; artificial intelligence; finite state machines (FSM) theory; reliability; theory of separating systems; theory of probability and mathematical statistics; and cognitive means. The proposed approach and basis of the mathematical apparatus are fragmentarily given for constructing HIS DIVNAR; that consists of four components: the first component, called IS DIOS, is designed for the express-diagnostics of organizational stress of the subject; the second (IS DIAPROD) is for the express-diagnostics and prevention of depression; the third (DIDEV) is for the expressdiagnostics and prevention of deviant behavior; and the fourth, intelligent system of express-diagnostics of information security attackers (IS DINARLOG2) is for making and justification of decisions with the use of cognitive means based on earlier revealed different regularities, including fault-tolerant irredundant unconditional diagnostic tests, fault-tolerant mixed diagnostic tests, regularities, and decision rules, which are built by using the applied IS DINARLOG1 constructed on the basis of intelligent instrumental software IMSLOG. Further development of this approach is proposed.

Data and Knowledge Base on the Basis of the Expanded Matrix Model of Their Representation for the Intelligent System of Road-Climatic Zoning of Territories

An extended matrix model of data and knowledge representation on the investigated area, as well as a matrix model of data representation on the territory under investigation, are proposed for the intelligent system of road-climatic zoning of territories (RCZT) - the main information technology of RCZT. A part of the West Siberian region has been selected as the investigated territory. The extended matrix model of knowledge representation is filled out by knowledge engineers with participation of highly qualified experts in the field of RCZT. The matrix model of data representation on the territory under investigation is filled out by persons concerned in RCZT of the motor-roads management system.

Construction of fault-tolerant signal feature subsets

New kinds of regularities in knowledge, viz., fault-tolerant signal-feature subsets, are proposed for use in intelligent test-pattern-recognition systems; the change in the values of these features indicates the transition of objects from one pattern to another. The algorithms for finding fault-tolerant signal-feature subsets are described and examples of their work are given. The algorithms for finding new regularities are implemented in a subsystem incorporated into the IMSLOG intelligent software tool based on test methods of pattern recognition. Examples from various problem domains are given to find subsets of signal features that are tolerant to measurement errors. The usability of these subsets is substantiated.

Tradeoff search methods between interpretability and accuracy of the identification fuzzy systems based on rules

This paper starts a brief historical overview of occurrence and development of fuzzy systems and their applications. Integration methods are proposed to construct a fuzzy system using other AI methods, achieving synergy effect. Accuracy and interpretability are selected as main properties of rule-based fuzzy systems. The tradeoff between interpretability and accuracy is considered to be the actual problem. The purpose of this paper is the in-depth study of the methods and tools to achieve a tradeoff for accuracy and interpretability in rule-based fuzzy systems and to describe our interpretability indexes. A comparison of the existing ways of interpretability estimation has been made We also propose the new way to construct heuristic interpretability indexes as a quantitative measure of interpretability. In the main part of this paper we describe previously used approaches, the current state and original authors’ methods for achieving tradeoff between accuracy and complexity.