Intelligent Expert Decision Support Systems

2017 ◽  
pp. 215-242 ◽  
Author(s):  
Abdel-Badeeh M. Salem ◽  
Tetiana Shmelova
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support Systems ◽  
Knowledge Engineering ◽  
Support Systems ◽  
Case Based Reasoning ◽  
Web Based ◽  
Engineering Research ◽  
New Generation ◽  
Application Developers

In this chapter, the authors present Intelligent Expert Decision Support Systems (IEDSSs) technology and conceptual models of Expert systems(ES) for Human-Operator (H-O) of different areas and Air Navigation System (ANS) too. The authors demonstrate some interesting applications of IEDSS. Intelligent Expert Decision Support Systems technology is a challenging field that has witnessed great advances in the last few years. Artificial intelligence (AI) theories and approaches receive increasing attention within this emerging technology .Researchers have been used the AI concepts and theories to develop a robust generation of IEDSSs. Moreover, the convergence of AI technologies and web technologies (WT) is enabling the creation of a new generation of web-based IEDSSs for all domains and tasks. This chapter discusses the AI methodologies and techniques for developing the IEDSSs. Two most popular paradigms are discussed namely; case-based reasoning and ontological engineering. Moreover, the chapter addresses the challenges faced by the application developers and knowledge engineers in developing and deploying AI-based expert decision support systems. In addition, the chapter presents some examples of ES by the author and colleagues at National Aviation University, Ukraine and some cases of IEDSSs developed by the author and his colleagues at Artificial intelligence and Knowledge Engineering Research Labs, Ain Shams University, AIKE Labs-ASU, Cairo, Egypt.

Intelligent Expert Decision Support Systems

2021 ◽  
pp. 510-531
Author(s):  
Abdel-Badeeh M. Salem ◽  
Tetiana Shmelova
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support Systems ◽  
Knowledge Engineering ◽  
Support Systems ◽  
Case Based Reasoning ◽  
Web Based ◽  
Engineering Research ◽  
New Generation ◽  
Application Developers

In this chapter, the authors present Intelligent Expert Decision Support Systems (IEDSSs) technology and conceptual models of Expert systems(ES) for Human-Operator (H-O) of different areas and Air Navigation System (ANS) too. The authors demonstrate some interesting applications of IEDSS. Intelligent Expert Decision Support Systems technology is a challenging field that has witnessed great advances in the last few years. Artificial intelligence (AI) theories and approaches receive increasing attention within this emerging technology .Researchers have been used the AI concepts and theories to develop a robust generation of IEDSSs. Moreover, the convergence of AI technologies and web technologies (WT) is enabling the creation of a new generation of web-based IEDSSs for all domains and tasks. This chapter discusses the AI methodologies and techniques for developing the IEDSSs. Two most popular paradigms are discussed namely; case-based reasoning and ontological engineering. Moreover, the chapter addresses the challenges faced by the application developers and knowledge engineers in developing and deploying AI-based expert decision support systems. In addition, the chapter presents some examples of ES by the author and colleagues at National Aviation University, Ukraine and some cases of IEDSSs developed by the author and his colleagues at Artificial intelligence and Knowledge Engineering Research Labs, Ain Shams University, AIKE Labs-ASU, Cairo, Egypt.

scholarly journals THE WEB–BASED REAL ESTATE MULTIPLE CRITERIA NEGOTIATION DECISION SUPPORT SYSTEM: A NEW GENERATION OF DECISION SUPPORT SYSTEMS

2009 ◽  
Vol 13 (3) ◽  
pp. 267-286 ◽  
Author(s):  
Vita Urbanavičienė ◽  
Artūras Kaklauskas ◽  
Edmundas K. Zavadskas ◽  
Mark Seniut
Keyword(s):  
Decision Support ◽  
Real Estate ◽  
Decision Support System ◽  
Stress Analysis ◽  
Decision Support Systems ◽  
Support System ◽  
Support Systems ◽  
Web Based ◽  
New Generation ◽  
Voice Stress

The negotiations are an inseparable part of the real estate buying and selling process. Currently real estate are characterized by the intensive creation and use of information, knowledge and automation (software, knowledge and decision support systems, neural networks, etc.) applications. It is commonly agreed that a better integration of information, knowledge and automation applications, as well use of voice stress analysis (one of biometric technologies) might be an efficient mean for decision making in real estate negotiations. Voice stress analysis can help the negotiators to distinguish between truth and lies, improve the value of decisions made, significantly speed up real estate sector processes, help to reach a better real estate sales and purchase agreement terms and decrease the overall cost of real estate search and negotiation processes. The authors’ objective is to improve the quality and efficiency of decision support systems. The article analyses scientific research related to negotiations and presents the developed Web–based Real Estate Multiple Criteria Negotiation Decision Support System with integrated voice stress analysis– a new generation of Decision Support Systems. Santruka Derybos yra neatskiriama nekilnojamojo turto pirkimo ir pardavimo proceso dalis. Dabartiniam nekilnojamojo turto sektoriui būdingas intensyvus informacijos, žiniu ir automatizavimo naudojimas bei kūrimas (programine iranga, žiniu ir sprendimu paramos sistemos, neuroniniai tinklai ir pan.). Sutariama, kad geresnis informacijos, žiniu, automatizavimo, taip pat balso streso analizes (biometrines technologijos) integravimas pagreitina nekilnojamojo turto sektoriaus veikla. Balso streso analize gali padeti derybininkams atskirti, kada sakoma tiesa, o kada meluojama, padidina priimamu sprendimu naudinguma, paspartina nekilnojamojo turto paieškos ir derybu procesus, padeda pasiekti naudingesniu pirkimo ir pardavimo sutarties salygu bei sumažina nekilnojamojo turto paieškos ir derybu proceso kaina. Straipsnio autoriai, siekdami pagerinti sprendimu paramos sistemu kokybe ir efektyvuma, analizuoja mokslininku atliktu derybu srities tyrimu rezultatus ir pristato sukurta nekilnojamojo turto derybu internetine sprendimu paramos sistema su integruota balso streso analizes technologija kaip naujos kartos sprendimu paramos sistema.

scholarly journals The acceptance of clinical decision support systems among clinicians in the treatment of neck and/or back pain

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S M Jansen-Kosterink ◽  
M Cabrita ◽  
I Flierman
Keyword(s):  
Back Pain ◽  
Decision Support ◽  
Focus Groups ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Case Based Reasoning ◽  
Online Questionnaire

Abstract Background Clinical Decision Support Systems (CDSSs) are computerized systems using case-based reasoning to assist clinicians in making clinical decisions. Despite the proven added value to public health, the implementation of CDSS clinical practice is scarce. Particularly, little is known about the acceptance of CDSS among clinicians. Within the Back-UP project (Project Number: H2020-SC1-2017-CNECT-2-777090) a CDSS is developed with prognostic models to improve the management of Neck and/or Low Back Pain (NLBP). Therefore, the aim of this study is to present the factors involved in the acceptance of CDSSs among clinicians. Methods To assess the acceptance of CDSSs among clinicians we conducted a mixed method analysis of questionnaires and focus groups. An online questionnaire with a low-fidelity prototype of a CDSS (TRL3) was sent to Dutch clinicians aimed to identify the factors influencing the acceptance of CDSSs (intention to use, perceived threat to professional autonomy, trusting believes and perceived usefulness). Next to this, two focus groups were conducted with clinicians addressing the general attitudes towards CDSSs, the factors determining the level of acceptance, and the conditions to facilitate use of CDSSs. Results A pilot-study of the online questionnaire is completed and the results of the large evaluation are expected spring 2020. Eight clinicians participated in two focus groups. After being introduced to various types of CDSSs, participants were positive about the value of CDSS in the care of NLBP. The clinicians agreed that the human touch in NLBP care must be preserved and that CDSSs must remain a supporting tool, and not a replacement of their role as professionals. Conclusions By identifying the factors hindering the acceptance of CDSSs we can draw implications for implementation of CDSSs in the treatment of NLBP.

Fundamentals of creating MIVAR expert systems

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.

Sign in / Sign up

Export Citation Format

Share Document