scholarly journals Linguistic Ontologies: Designing and Using in the Educational Intellectual Systems

2021 ◽  
Vol 4 (1) ◽  
pp. 97-111
Author(s):  
Olha Tkachenko ◽  
Kostiantyn Tkachenko ◽  
Oleksandr Tkachenko
Keyword(s):  
Computer Networks ◽  
Intelligent Systems ◽  
Semantic Analysis ◽  
Intelligent System ◽  
Subject Area ◽  
Intellectual System ◽  
Proposed Model ◽  
Small Set ◽  
Ontological Model ◽  
Linguistic Ontology

The purpose of the article is to investigate and consider the general trends, problems and prospects of designing and using linguistic ontologies in educational intellectual systems. The research methodology consists in semantic analysis methods of the basic concepts in the considered subject area (linguistic ontologies in the educational intellectual systems). The article discusses approaches to the use of linguistic models in modern educational intelligent systems. The novelty of the research is the analysis of the linguistic ontologies use in the educational intellectual systems. Conclusions. A model of linguistic ontology for the domain (disciplines “Computer Networks” and “Modelling Systems”) is presented. This model is used in the development of an educational intellectual system that supports online learning in these disciplines. The proposed model describes a set of relations of linguistic ontology, specially selected to describe the analyzed domain. To ensure these properties, it was proposed to use a small set of relationships. The proposed linguistic ontological model is implemented in an educational intelligent system that supports such disciplines as “Computer Networks” and “Modelling Systems”.

scholarly journals Ontological Modelling of the Decision-making Support Intellectual System in Risks Analysis in the Innovation and Investment Sphere

2021 ◽  
Vol 4 (1) ◽  
pp. 66-78
Author(s):  
Kostiantyn Tkachenko ◽  
Andrii Baidak
Keyword(s):  
Decision Making ◽  
Intelligent Systems ◽  
Semantic Analysis ◽  
Subject Area ◽  
Investment Projects ◽  
Problem Situations ◽  
Intellectual System ◽  
The Status ◽  
Develop Software ◽  
Decision Making Processes

The purpose of the article is to study, analyze and consider general problems and prospects of using ontologies when modelling decision-making processes in intelligent systems when analyzing the risks of the innovation and investment sphere. The research methodology lies in methods of the basic concepts semantic analysis of a given subject area (innovation and investment projects and management processes for the implementation of these projects). The article discusses the existing approaches to modelling decision-making processes in the analysis of risks in the innovation and investment sphere. The novelty of the research is the intellectualization problems solving of processes in the innovation and investment sphere on the basis of formal ontological models. Conclusions. The practical value of the presented results lies in the development and use of knowledge management system components for identifying and predicting problem situations in innovation and investment projects. The proposed integrated ontology can be used in the management of innovation and investment projects in various subject areas since it contains classes of concepts that have the status of a project activity standard. The developed ontological model makes it possible to develop software architecture for an intelligent decision support system, develop metadata and build a set of interrelated thesauri to support the semantics of end-user requests.

Artificial intelligence as a moving force of improvement and innovative development in education and pedagogic

2019 ◽  
pp. 21-30
Author(s):  
M. G. Koliada ◽  
T. I. Bugayova
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Interaction Analysis ◽  
Educational Data Mining ◽  
Main Task ◽  
Intellectual System ◽  
Will Force ◽  
To Come ◽  
Artificial Intelligence Systems

The article discusses the history of the development of the problem of using artificial intelligence systems in education and pedagogic. Two directions of its development are shown: “Computational Pedagogic” and “Educational Data Mining”, in which poorly studied aspects of the internal mechanisms of functioning of artificial intelligence systems in this field of activity are revealed. The main task is a problem of interface of a kernel of the system with blocks of pedagogical and thematic databases, as well as with the blocks of pedagogical diagnostics of a student and a teacher. The role of the pedagogical diagnosis as evident reflection of the complex influence of factors and reasons is shown. It provides the intelligent system with operative and reliable information on how various reasons intertwine in the interaction, which of them are dangerous at present, where recession of characteristics of efficiency is planned. All components of the teaching and educational system are subject to diagnosis; without it, it is impossible to own any pedagogical situation optimum. The means in obtaining information about students, as well as the “mechanisms” of work of intelligent systems based on innovative ideas of advanced pedagogical experience in diagnostics of the professionalism of a teacher, are considered. Ways of realization of skill of the teacher on the basis of the ideas developed by the American scientists are shown. Among them, the approaches of researchers D. Rajonz and U. Bronfenbrenner who put at the forefront the teacher’s attitude towards students, their views, intellectual and emotional characteristics are allocated. An assessment of the teacher’s work according to N. Flanders’s system, in the form of the so-called “The Interaction Analysis”, through the mechanism of fixing such elements as: the verbal behavior of the teacher, events at the lesson and their sequence is also proposed. A system for assessing the professionalism of a teacher according to B. O. Smith and M. O. Meux is examined — through the study of the logic of teaching, using logical operations at the lesson. Samples of forms of external communication of the intellectual system with the learning environment are given. It is indicated that the conclusion of the found productive solutions can have the most acceptable and comfortable form both for students and for the teacher in the form of three approaches. The first shows that artificial intelligence in this area can be represented in the form of robotized being in the shape of a person; the second indicates that it is enough to confine oneself only to specially organized input-output systems for targeted transmission of effective methodological recommendations and instructions to both students and teachers; the third demonstrates that life will force one to come up with completely new hybrid forms of interaction between both sides in the form of interactive educational environments, to some extent resembling the educational spaces of virtual reality.

scholarly journals ПРИНЦИП ЦІЛІСНОЇ ОРГАНІЗАЦІЇ ІНТЕЛЕКТУАЛЬНИХ СИСТЕМ

2019 ◽  
pp. 4-16
Author(s):  
Серій Ілліч Доценко
Keyword(s):  
Control System ◽  
Intelligent Systems ◽  
Systems Approach ◽  
Intelligent System ◽  
Control Object ◽  
The Other ◽  
Methodological Basis ◽  
Other Hand ◽  
Intellectual System ◽  
The Impact

The antinomy of the division of the intellectual system into parts has been formed, namely: the intellectual system is an organized whole, which is formed from at least two parts; for an intelligent system, as an organized whole, it is impossible to divide into a controlling part (control system) and a part of which is controlled. It has been established that the antinomy of dividing an intelligent system into parts is generated by the fact that, traditionally, the control system and the control object are considered separately. Therefore, it is considered the system, and not an organized whole. The role of the theory of functional systems in the development of cybernetic systems as intellectual systems is defined. This theory is the basis for the development of intelligent systems A. V. Chechkinim, K. A. Pupkov, and other authors. On the other hand, M. I. Meltzer develops the theory of dialogue systems for managing production enterprises, the basis of which is the mathematical theory of systems. It is shown that the functional representation architectures for these systems are similar. The similarity is determined on the basis of the task approach. On the one hand, there is a mutual non-recognition of the results of scientific schools of physical and technical cybernetics, and on the other hand, there is a similarity of the results obtained. It has been established that the methodological basis of the holistic approach is the task approach to the formation of a solving system, developed in the theory of dialogue management of production. To do this, it is necessary to include the “Activity to get the result” block in the solving system in order to turn it into an intellectual system. The methodological basis of a systems approach is a functional approach to the formation of systems. The main lesson of the classical cybernetics crisis, regarding the organizational principle for two parts of an organized whole, is to establish a dialectical unity of concepts in the form of a “general” concept and a “concrete” concept for problem-solving results in the control system and control object. Thus, a dialectically organized whole is formed. The article also analyzes the impact of the study of intelligent systems on the development of the methodological foundations of the Industry 4.0 platform. The next task that needs to be solved is the formation of the principle of functional self-organization, which is the basis for the formation of a mechanism for ensuring consistency between the results of solving problems in parts of a dialectically organized whole

scholarly journals Analysis of approaches for identification the ontological model components of the searching system

2021 ◽  
Vol 6 (2(62)) ◽  
pp. 10-14
Author(s):  
Victoria Kostenko ◽  
Olga Bulgakova ◽  
Barbara Stelyuk
Keyword(s):  
Intelligent System ◽  
Subject Area ◽  
System Model ◽  
Search Process ◽  
Search System ◽  
Automated Processing ◽  
Ontological Model ◽  
Model Components ◽  
Functional Components

The object of research is the components of an intelligent system for searching information in electronic repositories of unstructured documents, which based on the ontologies of the subject area. One of the most problematic areas is the processing and analysis of information contained in electronic repositories of unstructured documents. There are considered the some possibilities of increasing the efficiency of information processing. In the course of the study, using the method in which ontologies comprise sets of terms presented in it. In addition, the ontological set also includes information about subject areas, areas of definitions, etc. There are obtained the sequence of defining the conceptual representation of an intelligent search system based on ontological components. There are presented the composition of the ontological system model. There are described the main functional components of the system for intelligent processing of information about electronic documents. The proposed approaches for identifying the component components of the ontological model of the search system have a lot of features. This is due to the fact that the search system model must have a set of properties: integrity, coherence, organization, integrability, mobility. Ontologies which representing the basic concepts of the domain in a format available for automated processing in the form of a hierarchy of classes and relationships between them allow automated processing. The using of ontologies in the role of an intermediary between the user and the search process, between the search process and the search system that can facilitate the solution of a number of complex and non-standard tasks of information retrieval (for example, the automation of the search process). It is possible to solve the problem of knowledge representation for displaying information relevant to user requests, as well as to solve the problems of filtering and classifying information. Compared to similar well-known search systems, this provides such advantages as creating a common terminology for software agents and users, protecting the information store from total overflow and errors, as well as solving the issue of information aging.

scholarly journals ПРИНЦИП ФУНКЦІОНАЛЬНОЇ САМООРГАНІЗАЦІЇ ДІЯЛЬНОСТІ ІНТЕЛЕКТУАЛЬНИХ СИСТЕМ

2019 ◽  
pp. 18-28
Author(s):  
Серій Ілліч Доценко
Keyword(s):  
Control Systems ◽  
Intelligent Systems ◽  
Feedback Loop ◽  
Intelligent System ◽  
Functional System ◽  
Self Organization ◽  
Main Attention ◽  
Functional Systems ◽  
Intellectual System ◽  
Smart Things

The five principles of self-organization of cybernetic systems are formed in classical cybernetics in the form of two hypotheses of N. Wiener and three hypotheses of W. R. Ashby. The main attention in the development of the theory of a functional system is given to its analysis as an integral unit, and the formation on its basis of the theories of intelligent systems. At the same time, no attention was left to the study of the principle of the mechanism for ensuring compliance with the result obtained and the project established for it. The conformity mechanism, which is formed as part of a functional system, is implemented on the basis of the principle of self-organization of the functional system’s activity at the stage of a future result project’s implementation through double sequential feedback through the “Action Results Acceptor” mechanism. Based on this principle, it is possible to formulate the law of self-organization of an intellectual system in the following form. For functional self-organization of an intelligent system based on a mechanism to ensure compliance with the result of an activity and its project, it is necessary to include an “Acceptor of an action result” in the feedback loop to match the result of an action, a project of a future result of an action, and a management team. The principles of self-organization formed in classical cybernetics turned out to be elements of the clarified single principle of the self-organization of functional systems activity. In this work, it was realized that the meaning of knowledge about the functional systems in the theory and the theory of dialogue control systems of two successive feedback loops and the mechanism of their combination in the “Acceptor of the results of action” was realized. It is thanks to these contours that the principle of functional self-organization of activities is implemented, the founders of classical cybernetics so stubbornly sought and from which they abandoned technical cybernetics. The task of the formation of the goal of the activity can be solved by knowing the mechanism of the formation of the project of a future result based on heuristic self-organization for physiological and cybernetic systems. The solution to this problem will ensure the formation of "smart things" in Industry 5.0. After all, “smart things” should be “intelligent”

Diesel Engine Working Cycle Utilizing Artificial Intelligent Systems

2014 ◽  
Vol 490-491 ◽  
pp. 1057-1062
Author(s):  
Marwa Ahmed Abd-El Hamied
Keyword(s):  
Diesel Engine ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Cylinder Pressure ◽  
Artificial Intelligent ◽  
Engine Model ◽  
Working Cycle ◽  
Proposed Model ◽  
A New Technique ◽  
Artificial Neural Network Ann

In this paper a new technique is designed to build a model concerning cylinder pressure in diesel engine. The concept is to build a working cycle model utilizing artificial intelligent system. Artificial Immunity System (AIS) and Artificial Neural Network (ANN) are proposed as a new approach that can be used to simulate cylinder pressure for different crankshaft speed and loads. Experimental test results of diesel engine model (AD3.152 UR) are used to train AIS system and ANN. The proposed model succeeded to provide reliable result and prove to be useful in evaluating the quality of working cycle in diesel engine.

scholarly journals COUNTERFACTUAL TEMPORAL MODEL OF CAUSAL RELATIONSHIPS FOR CONSTRUCTING EXPLANATIONS IN INTELLIGENT SYSTEMS

2021 ◽  
pp. 41-46
Author(s):  
Serhii Chalyi ◽  
Volodymyr Leshchynskyi ◽  
Irina Leshchynska
Keyword(s):  
Information System ◽  
Intelligent Systems ◽  
Intelligent System ◽  
System Development ◽  
Causal Relationships ◽  
Temporal Properties ◽  
Intellectual System ◽  
Transitivity Property ◽  
Temporal Model ◽  
Intelligent Information

The subject of the research is the processes of constructing explanations based on causal relationships between states or actions of an intellectualsystem. An explanation is knowledge about the sequence of causes and effects that determine the process and result of an intelligent informationsystem. The aim of the work is to develop a counterfactual temporal model of cause-and-effect relationships as part of an explanation of the process offunctioning of an intelligent system in order to ensure the identification of causal dependencies based on the analysis of the logs of the behavior ofsuch a system. To achieve the stated goals, the following tasks are solved: determination of the temporal properties of the counterfactual description ofcause-and-effect relationships between actions or states of an intelligent information system; development of a temporal model of causal connections,taking into account both the facts of occurrence of events in the intellectual system, and the possibility of occurrence of events that do not affect theformation of the current decision. Conclusions. The structuring of the temporal properties of causal links for pairs of events that occur sequentially intime or have intermediate events is performed. Such relationships are represented by alternative causal relationships using the temporal operators"Next" and "Future", which allows realizing a counterfactual approach to the representation of causality. A counterfactual temporal model of causalrelationships is proposed, which determines deterministic causal relationships for pairs of consecutive events and pairs of events between which thereare other events, which determines the transitivity property of such dependencies and, accordingly, creates conditions for describing the sequence ofcauses and effects as part of the explanation in intelligent system with a given degree of detail The model provides the ability to determine cause-andeffect relationships, between which there are intermediate events that do not affect the final result of the intelligent information system.

scholarly journals Intellectual analysis of spectral images of plants

2020 ◽  
Vol 17 ◽  
pp. 00142
Author(s):  
Eugeny V. Lutsenko ◽  
Valery E. Korzhakov ◽  
Valery I. Loiko
Keyword(s):  
Intelligent System ◽  
Real Data ◽  
Subject Area ◽  
Leaf Damage ◽  
Automated System ◽  
Intellectual System ◽  
Reliable Model ◽  
Original Presentation ◽  
The Subject

Traditionally, the assessment of plants for different diseases is carried out by visual determination of leaf damage with the help of an expert – phytopathologist. This method has a number of disadvantages that are proposed to be overcome with the use of the automated system-cognitive analysis (ASC-analysis) of the spectra of images plants in the intelligent system called “Eidos”. For this purpose, we solve the following tasks: Task 1: formulating the idea and concept of the solution of the problem; Task 2: justifying the choice of the method and the tool to solve problems; Task 3: applying the selected method and the tool to solve the problems, i.e. to perform the following steps: – cognitive structuring of the subject area; – formalization of the subject area; – synthesis and verification of models; – improving the quality of the model and the choice of the most reliable models – solution in the most reliable model of diagnostic tasks (classification, recognition, identification), decision support and research of the modeled subject area by studying its model. Task 4: describing the effectiveness of the proposed solution. Task 5: examining the limitations and disadvantages of the proposed solutions for the problems and prospects of its development by overcoming those limitations and drawbacks. We also provide a detailed numerical example intellectual analysis of spectral images of plants with real data by applying the ASC-analysis and “Eidos” intellectual system. However, students and scientists still do not notice that open, scalable, interactive, intelligent online environment for learning and researches already exists and operates, based on automated systemcognitive analysis (ASC-analysis) and its programmatic Toolkit – intellectual “Eidos” and the author’s website. This article is an original presentation and it is designed to familiarize potential users with the capabilities of this environment.

scholarly journals Weather Modeling Using Data-Driven Adaptive Rough-Neuro-Fuzzy Approach

2017 ◽  
Vol 12 (2) ◽  
pp. 429-435
Author(s):  
M. Sudha
Keyword(s):  
Intelligent Systems ◽  
Prediction Accuracy ◽  
Intelligent System ◽  
Data Driven ◽  
Fuzzy Approach ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Hybrid Data ◽  
Weather Modeling ◽  
Using Data

Recently, hybrid data-driven models have become appropriate predictive patterns in various hydrological forecast scenarios. Especially, meteorology has witnessed that there is a need for a much better approach to handle weather-related parameters intelligently. To handle this challenging issue, this research intends to apply the fuzzy and ANN theories for developing hybridized adaptive rough-neuro-fuzzy intelligent system. . Assimilating the features of ANN and FIS has attracted the rising attention of researchers due to the growing requisite of adaptive intelligent systems to solve the real world requirements. The proposed model is capable of handling soft rule boundaries and linguistic variables to improve the prediction accuracy. The adaptive rough-neuro-fuzzy approach attained an enhanced prediction accuracy of 95.49 % and outperformed the existing techniques.

scholarly journals User interfaces ontology in the cybernetic model of intelligent sys-tems

2021 ◽  
Vol 11 (1) ◽  
pp. 89-103
Author(s):  
K.I. Kostenko ◽  
◽  
V.Yu. Belkin
Keyword(s):  
User Interfaces ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Three Dimensional ◽  
Subject Area ◽  
Knowledge Flows ◽  
Professional Activity ◽  
Information Structures ◽  
Knowledge Processing ◽  
Abstract Knowledge

A model of an intelligent system is proposed for implementing the control and professional use of a knowledge collec-tions related to subject area of software interfaces with external users. The basis of formalization is an abstract model of an intelligent system. It is based on coordinated and balanced invariants of the knowledge representation formalism model. Invariants set includes classes of morphisms for abstract knowledge processing modelling with morphisms' do-mains, invariants of the multidimensional architecture of intelligent systems components, which includes inter-component knowledge flows and processes of knowledge synthesis within components, as well as invariants of control agents for abstract knowledge flows and processes within intelligent systems. The intelligent system knowledge base is presented as subject area ontology by non-ordered series of simple knowledge. Fragments of such ontology are distrib-uted between components of intelligent systems three-dimensional architecture. The basis of such structure based on the dimensions of abstraction, structuring and the level of knowledge addressed to and processed within intelligent sys-tem's separate components. The ontologies reflect the ideas about the structures of memory and the processes of think-ing used to model the schemes of the professional activity of a specialist. The basis for the formalization of such con-cepts is the fundamental principles of philosophy, linguistics, cognitive psychology, mathematics, and system engineer-ing. This allows deploying a comprehensive system of classes of information structures and processes for complex knowledge synthesizing that support achievement of various cognitive goals implemented by specialists within profes-sional tasks implementation processes. The goals system includes extraction, analysis and application of knowledge about user interfaces. The variety of such goals is modeled by a high-level implementation pattern system. They are composed of basic types of goals and are implemented using knowledge processing cognitive operations. Knowledge structures in the format of semantic hierarchies are used as a unified representation of knowledge. Cognitive goals are realized by combinations of operations on structured knowledge, adapted to special classes of structures.

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