Artificial intelligence using for medical diagnosis via implementation of expert systems

Modern biomedical technologies development affords to provide the doctor with colossal amount of information about patients organism condition. However, the opportunity of using this data for medical diagnosis fully now is a distantive perspective only. The reason is a humans limited ability in assessment and interpretation this data arrays. The solution seems in artificial intelligence and expert systems wide introduction to medicine. Currently, almost all authors consider various options for constructing artificial neural networks as a way to implement artificial intelligence. This approach, which goes back to the fundamental theorem of A.N. Kolmogorov, the works of V.I. Arnold and Hecht-Nielsen [3], demonstrates excellent capabilities in a number of pattern recognition problems, which are reduced to revealing hidden details against the background of input noises. Much less often is mentioned such a method of modeling formal thinking as expert systems, which arose in the 1960s and then went into the shadows. Since the inception of cybernetics, computer programmers have tried to reproduce the mechanism of human thinking, that is, the task was to teach the computer to "think". The first known results in the field of creating and using intelligent systems were laid by the work of Norbert Wiener and G.S. Altshuller. At the same time, the creation of intelligent systems was reduced to the development of programs that solve problems using a variety of heuristic methods based on the property of human thinking to generalize.

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Fundamentals of creating MIVAR expert systems

10.12737/1513119 ◽

2021 ◽

Author(s):

Oleg Varlamov

Keyword(s):

Artificial Intelligence ◽

Decision Support ◽

Expert System ◽

Expert Systems ◽

Decision Support Systems ◽

Intelligent Systems ◽

Autonomous Robots ◽

Support Systems ◽

Knowledge Bases ◽

Computer Engineering

Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

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Fuzzy classifiers in cardiovascular disease diagnostics: Review

The Siberian Journal of Clinical and Experimental Medicine ◽

10.29001/2073-8552-2020-35-4-22-31 ◽

2020 ◽

Vol 35 (4) ◽

pp. 22-31

Author(s):

I. A. Hodashinsky

Keyword(s):

Artificial Intelligence ◽

Medical Diagnosis ◽

Classification Problem ◽

Medical Diagnostics ◽

Medical Systems ◽

Classification Result ◽

Biological Objects ◽

Disease Diagnostics ◽

Fuzzy Classifiers ◽

Human Thinking

The complexity of biological objects makes the development of computerized medical systems a difficult algorithmic decision due to the natural uncertainty inherent in these objects. Human thinking is based on vague and approximate data that can be analyzed to form clear decisions. An exact mathematical model of biological objects may not exist in practice, or such a model may be too complex to implement. In this case, fuzzy logic is a suitable tool for solving the specified problem. The problem of medical diagnosis can be viewed as a classification problem. The article presents a literature review of the use of fuzzy classifiers in diagnostics of cardiovascular diseases. The main advantage of fuzzy classifiers in comparison with other artificial intelligence methods is the ability to interpret the resulting classification result. The review aims to expand the knowledge of various researchers working in the field of medical diagnostics.

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A General Medical Diagnosis System Formed by Artificial Neural Networks and Swarm Intelligence Techniques

Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-5225-4769-3.ch006 ◽

2018 ◽

pp. 130-145 ◽

Cited By ~ 1

Author(s):

Pandian Vasant

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Artificial Neural Networks ◽

Swarm Intelligence ◽

Medical Diagnosis ◽

Intelligent Systems ◽

General Framework ◽

Diagnosis System ◽

Artificial Neural ◽

Medical Diagnosis System

One of the most popular applications of artificial intelligence within the medical field is developing medical diagnosis systems. Because artificial-intelligence-based techniques are able to use pre-data and instant data flow for making predictions, it is an easy task to design intelligent systems that can give advice to people or perform diagnosis-based decision making. So, it has been an important research interest to design and develop intelligent systems, which are able to make diagnoses for medical purposes. In this sense, the objective of this chapter is to introduce a general medical diagnosis system that can be used for detecting diseases. In detail, the system employs artificial neural networks and swarm-intelligence-based techniques to form a general framework of intelligent diagnosis. The chapter briefly focuses on the infrastructure of the system and discusses its diagnosis potential.

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A General Medical Diagnosis System Formed by Artificial Neural Networks and Swarm Intelligence Techniques

Biotechnology ◽

10.4018/978-1-5225-8903-7.ch031 ◽

2019 ◽

pp. 788-803

Author(s):

Pandian Vasant

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Artificial Neural Networks ◽

Swarm Intelligence ◽

Medical Diagnosis ◽

Intelligent Systems ◽

General Framework ◽

Diagnosis System ◽

Artificial Neural ◽

Medical Diagnosis System

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Integrating Information Visualization and Dimensionality Reduction: A pathway to Bridge the Gap between Natural and Artificial Intelligence

TecnoLógicas ◽

10.22430/22565337.2108 ◽

2021 ◽

Vol 24 (51) ◽

pp. e2108

Author(s):

Diego H. Peluffo-Ordóñez

Keyword(s):

Artificial Intelligence ◽

Dimensionality Reduction ◽

Information Visualization ◽

Intelligent Systems ◽

Computerized Decision Support ◽

Suitable Alternative ◽

Human Thinking ◽

Machine Communication ◽

To Receive ◽

Computerized Decision Support Systems

By importing some natural abilities from human thinking into the design of computerized decision support systems, a cross-cutting trend of intelligent systems has emerged, namely, the synergetic integration between natural and artificial intelligence. While natural intelligence provides creative, parallel, and holistic thinking, its artificial counterpart is logical, accurate, able to perform complex and extensive calculations, and tireless. In the light of such integration, two concepts are important: controllability and interpretability. The former is defined as the ability of computerized systems to receive feedback and follow users’ instructions, while the latter refers to human-machine communication. A suitable alternative to simultaneously involve these two concepts—and then bridging the gap between natural and artificial intelligence—is bringing together the fields of dimensionality reduction (DimRed) and information visualization (InfoVis).

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Artificial Intelligence in Chemistry

Collection of Czechoslovak Chemical Communications ◽

10.1135/cccc19922413 ◽

1992 ◽

Vol 57 (12) ◽

pp. 2413-2451 ◽

Cited By ~ 6

Author(s):

Vladimír Jakuš

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Problem Solving ◽

Expert Systems ◽

Intelligent Systems ◽

Structure Elucidation ◽

Chromatographic Retention ◽

Natural Languages ◽

Synthesis Planning ◽

Intelligent Instrumentation

The definition of artificial intelligence and the associated tasks of this branch of science are discussed. The tasks include pattern recognition, adaptation and learning, problem solving by means of expert systems or neural networks, and understanding the natural language and communication with a machine in it. The principles of problem solving are analyzed. It is demonstrated how artificial intelligence-based computer programs in which chemical expertise is encoded assist in structure elucidation, in the investigation of relations between structure and biological activity or chromatographic retention, etc.; problems emerging in the synthesis planning with a retrosynthetic analysis, or in the planning of experiments and intelligent consultations are dealt with. Several models used for structure elucidation and synthesis planning are evaluated. An overview is presented of additional expert systems which, along with artificial intelligence-based robotics, are used in intelligent instrumentation. Also discussed is the role of neural networks, which begin to be successfully employed in structure elucidation, synthesis planning, in intelligent instrumentation and in the treatment of natural languages. They are expected to be an important tool in the implementation of intelligent systems for the classification of chemical databases and prediction of properties of molecules.

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

Informatics and Education ◽

10.32517/0234-0453-2018-33-10-21-30 ◽

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.

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SISTEM PAKAR MENDIAGNOSA VIRUS MAYORA DENGAN METODE VARIABLE CENTERED INTELLIGENT RULE SYSTEM (VCIRS)

KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) ◽

10.30865/komik.v2i1.916 ◽

2018 ◽

Vol 2 (1) ◽

Author(s):

Siti Nurhena ◽

Nelly Astuti Hasibuan ◽

Kurnia Ulfa

Keyword(s):

Artificial Intelligence ◽

Immune System ◽

Expert System ◽

Expert Systems ◽

Dengue Hemorrhagic Fever ◽

Hemorrhagic Fever ◽

Visual Basic ◽

Artificial Intelligence Techniques ◽

Individual Needs ◽

Rule System

The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms. Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms. With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms.Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms.With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)

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INTERDISCIPLINARY LINKS, INTEGRATIVE APPROACH,HEURISTIC METHODS AND ARTIFICIAL INTELLIGENCE IN THE MANAGEMENT OF INNOVATION - PART 1

Knowledge International Journal ◽

10.35120/kij28051527h ◽

2018 ◽

Vol 28 (5) ◽

pp. 1527-1532

Author(s):

Hristo Patev

Keyword(s):

Artificial Intelligence ◽

Production Systems ◽

Innovation Management ◽

Integrative Approach ◽

Heuristic Methods ◽

Organizational Renewal ◽

Innovation Activities ◽

Management Of Innovation ◽

Inventive Activity ◽

High Degree

In this first work, out of the total of twenty-four, are considered: Integrative approach, interdisciplinary relations and transnational language in the technical and economic fundament of engineering and management, for the purpose of competitive innovation and successful business. Approaches to develop the innovation with a high degree of complexity. Interactive heuristic methods and algorithms for inventive activity, for inspiring and developing new industrial products and services for households and production systems. Implementing an effective business vocabulary for organizational renewal. Introduction of gaming and "art" methods in innovation management. Intensifying innovation activities through an attempt to introduce artificial intelligence into teamwork, with simultaneous implementation of an engineering and non-engineering approach.

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Group Agency and Artificial Intelligence

Philosophy & Technology ◽

10.1007/s13347-021-00454-7 ◽

2021 ◽

Author(s):

Christian List

Keyword(s):

Artificial Intelligence ◽

Moral Status ◽

Intelligent Systems ◽

Group Agency ◽

High Stakes ◽

Moral Agents ◽

Legal Personhood ◽

The Social ◽

Intentional Agents ◽

Regulatory Challenges

AbstractThe aim of this exploratory paper is to review an under-appreciated parallel between group agency and artificial intelligence. As both phenomena involve non-human goal-directed agents that can make a difference to the social world, they raise some similar moral and regulatory challenges, which require us to rethink some of our anthropocentric moral assumptions. Are humans always responsible for those entities’ actions, or could the entities bear responsibility themselves? Could the entities engage in normative reasoning? Could they even have rights and a moral status? I will tentatively defend the (increasingly widely held) view that, under certain conditions, artificial intelligent systems, like corporate entities, might qualify as responsible moral agents and as holders of limited rights and legal personhood. I will further suggest that regulators should permit the use of autonomous artificial systems in high-stakes settings only if they are engineered to function as moral (not just intentional) agents and/or there is some liability-transfer arrangement in place. I will finally raise the possibility that if artificial systems ever became phenomenally conscious, there might be a case for extending a stronger moral status to them, but argue that, as of now, this remains very hypothetical.

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