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Relevant bibliographies by topics / Decision Support System and Expert System

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Academic literature on the topic 'Decision Support System and Expert System'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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Journal articles on the topic "Decision Support System and Expert System"

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Greer, Marianne L. "Rxpert: A Prototype Expert System for Formulary Decision Making." Annals of Pharmacotherapy 26, no. 2 (February 1992): 244–50. http://dx.doi.org/10.1177/106002809202600221.

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OBJECTIVE: RXPERT, a prototype, computer-based, expert system that models the decision-making processes for an ambulatory (non-hospital) formulary, is described as an example of how expert systems may be used to support pharmacy decision making. Basic information about expert-system technology is provided through this example. BACKGROUND: Computer-assisted decision making is becoming an important and accepted aspect of complex, health-related decisions. Because expert-system support may become an integral component of future, complex, pharmacy decision making, it is important for pharmacists to become familiar with this technology and its possibilities for supporting pharmacy decisions. METHOD: Expert systems offer the potential advantages of making the human decision-making process explicit, more consistent, easily duplicated in many locations simultaneously, and easy to update and document. Although an expert system is seldom intended to replace human decision makers, it can provide valuable support for complex, multivariable decisions. Typical knowledge-acquisition and knowledge-engineering techniques, as well as the characteristics and structure of expert systems, are described, relative to the development of the RXPERT prototype. CONCLUSIONS: Although RXPERT is not yet in use, the process for using an expert system to support an individual committee member's personal assessment of a drug product is described. Decision-support expert systems are potentially useful to pharmacists in complex decision-making tasks.

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Lee, R. M. "On information system semantics: Expert vs. decision support systems." Social Science Information Studies 5, no. 1 (January 1985): 3–10. http://dx.doi.org/10.1016/0143-6236(85)90002-x.

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Megawaty, Megawaty, and Maria Ulfa. "Decision Support System Methods: A Review." Journal of Information Systems and Informatics 2, no. 1 (March 17, 2020): 192–201. http://dx.doi.org/10.33557/journalisi.v2i1.63.

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The current information system is the backbone of most companies or agencies. Currently there are various types of information systems including "transaction processing systems (TPS), office automation systems (OAS), knowledge work systems (KWS), management information systems (MIS), decision support systems (DSS), expert systems, groups decision support systems (GDSS), computer support collaborative work systems (CSCW), and executive support systems (ESS) ". Of the various types of information systems that are currently available decision support systems are widely used as aids in making decisions. For this reason, in this study, a study was conducted with a method that can be used in a decision support system. From the study conducted there are three decision support system methods that are often used, namely simple additive weighting (SAW), analytic hierarchy process (AHP), and simple multi attribute rating technique (SMART). The third method is a method that has a weighting value and criteria in the calculation process

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Kersten, Gregory E. "Expert system technology and strategic decision support." Mathematical and Computer Modelling 12, no. 10-11 (1989): 1321–33. http://dx.doi.org/10.1016/0895-7177(89)90371-3.

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Doukidis, Georgios I. "Decision support system concepts in expert systems: An empirical study." Decision Support Systems 4, no. 3 (September 1988): 345–54. http://dx.doi.org/10.1016/0167-9236(88)90021-8.

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Lee, Daniel T. "Expert Decision-support Systems for Decision-making." Journal of Information Technology 3, no. 2 (June 1988): 85–94. http://dx.doi.org/10.1177/026839628800300204.

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Computers have made tremendous contributions towards transactional processing. However, the highest pay-off the computer can make is not in transactional processing but in decision-making. Recently, expert systems have just begun to be used in the decision-making process. Individual technologies alone are inadequate for an effective decision support. The purpose of this paper is to investigate the related issues in decision support and to develop an expert decision support system (EDSS) for combining decision support systems and expert systems into a unified whole for decision support. The emphasis will be on developing a DSS/ES model which can be used to integrate the traditional DSS database and ES knowledge-base for building a user-friendly EDSS.

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Gutenson, J. L., A. N. S. Ernest, J. R. Fattic, L. E. Ormsbee, A. A. Oubeidillah, and X. Zhang. "Water Expert: a conceptualized framework for development of a rule-based decision support system for distribution system decontamination." Drinking Water Engineering and Science 8, no. 2 (August 28, 2015): 9–24. http://dx.doi.org/10.5194/dwes-8-9-2015.

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Abstract. Significant drinking water contamination events pose a serious threat to public and environmental health. Water utilities often must make timely, critical decisions without evaluating all facets of the incident. The data needed to enact informed decisions are inevitably dispersant and disparate, originating from policy, science, and heuristic contributors. Water Expert is a functioning hybrid decision support system (DSS) and expert system framework that emphasizes the meshing of parallel data structures in order to expedite and optimize the decision pathway. Delivered as a thin-client application through the user's web browser, Water Expert's extensive knowledgebase is a product of inter-university collaboration that methodically pieced together system decontamination procedures. Decontamination procedures are investigated through consultation with subject matter experts, literature review, and prototyping with stakeholders. This paper discusses the development of Water Expert, analyzing the development process underlying the DSS and the system's existing architecture specifications. Water Expert constitutes the first system to employ a combination of deterministic and heuristic models which provide decontamination solutions for water distribution systems. Results indicate that the decision making process following a contamination event is a multi-disciplinary effort. This contortion of multiple inputs and objectives limit the ability of the decision maker to find optimum solutions without technological intervention.

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Gutenson, J. L., A. N. S. Ernest, J. R. Fattic, L. E. Ormsbee, A. A. Oubeidillah, and X. Zhang. "Water Expert: a conceptualized framework for development of a rule-based decision support system for distribution system decontamination." Drinking Water Engineering and Science Discussions 7, no. 1 (May 27, 2014): 169–211. http://dx.doi.org/10.5194/dwesd-7-169-2014.

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Abstract. Significant drinking water contamination events pose a serious threat to public and environmental health. Water utilities often must make timely, critical decisions without evaluating all facets of the incident, as the data needed to enact informed decisions are inevitably dispersant and disparate, originating from policy, science, and heuristic contributors. Water Expert is a functioning hybrid decision support system (DSS) and expert system framework, with emphases on meshing parallel data structures to expedite and optimize the decision pathway. Delivered as a thin-client application through the user's web browser, Water Expert's extensive knowledgebase is a product of inter-university collaboration that methodically pieced together system decontamination procedures through consultation with subject matter experts, literature review, and prototyping with stakeholders. This paper discusses development of Water Expert, analyzing the development process underlying the DSS and the system's existing architecture specifications.

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CHAN, CHRISTINE WAICHI, WEERAPONG KRITPIPHAT, and PAITOON TONTIWACHWUTHIKUL. "KNOWLEDGE ENGINEERING OF A MONITORING AND CONTROL DECISION SUPPORT SYSTEM." International Journal of Software Engineering and Knowledge Engineering 10, no. 03 (June 2000): 301–18. http://dx.doi.org/10.1142/s0218194000000183.

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This paper presents the Object-Oriented Knowledge Engineering (OOKE) methodology and its application in developing an expert system. OOKE is an expert system development methodology which incorporates the conceptual modelling tool of Inferential Modelling Technique into the analysis model of the Object-Oriented Software Engineering methodology. It was applied to develop a supervisory and decision support system for monitor and control of a water distribution system called the Water Advisor. The expertise, heuristics and reasoning knowledge of experts were acquired and then formulated in a model building process using the OOKE into a conceptual model which became the basis for a prototype expert system.

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Mulya, Dimmas, Dian Pratiwi, and Is Mardianto. "Decision Support System for Diagnose Hepatitis Type using Expert System Method." Journal of Information Technology and Computer Science 6, no. 1 (April 30, 2021): 66–79. http://dx.doi.org/10.25126/jitecs.202161231.

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In the medical world, there are five types of Hepatitis, namely Hepatitis A, B, C, D, and E. However, the five types of hepatitis have similar symptoms, including yellowing of the skin color, yellowing of the eyeball, loss of appetite, etc. Thus, many of the Medical Personnel often misdiagnose the patient for the type of hepatitis or not suffer from hepatitis.Therefore, previous diagnostic data ware collected from Medical Specialists which will be processed and developed into the Java-based Decision Support System Application with Expert System method with the percentage output of the likelihood of patients from each type of hepatitis along with the possibility of patients not suffering from hepatitis.With the output of this application, the percentage of the possibility of each type of hepatitis or the possibility of not suffering from hepatitis can help Medical Personnel to make diagnostic decisions based on alternatives provided by the application.

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Dissertations / Theses on the topic "Decision Support System and Expert System"

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Al-Mohamdi, Granim Al Hamaidi. "An intelligent decision support system for project management." Thesis, University of Nottingham, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287199.

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Froukh, Mohammed Lu’ay Jamal. "Decision-support system for domestic water demand forecasting and management." Thesis, University of Newcastle Upon Tyne, 1997. http://hdl.handle.net/10443/413.

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A generic but flexible decision-support system for domestic water demand forecasting and management (DFMS) has been developed as part of a highlyintegrated decision-support system for river-basin management. Its purpose is to provide water-resources planners with the facilities for estimating future water demand for any demand region and time period, having regard to the possibility of introducing demand-management measures. The system has the capability of predicting domestic-water demand by various methods according to the data availability, computing conservation effectiveness due to the implementation of various demand-management measures, forecasting the number of customers for different consumption units (person, household, water connection) and facilitating the development of demand-scenarios for eveluating various options. The system is designed in such a way that makes it easy to use for both novice and experienced users since it is driven by a menu system which relies on a mouse rather than the keyboard. Moreover, the communication between user and the system is by means of a user-friendly interface which makes extensive use of hypertext and colour graphics in presenting the results. Briefly, DFMS comprises the following components: a GIS that stores, displays and analyses all geo-coded information such as satellite imagery, urban areas, cities and towns, etc.; • a database which provides access to non-spatial data such as demand-area location and characteristics including top-level descriptors such as population, total demand, per-capita consumption, etc.; • an expert system which uses the rule-based inference for data entry and predicting values (quantitative or qualitative) of variables from the knowledgebase; . four methods of demand forecasting ranging from superficial to detailed, namely time extrapolation, econometric variables, end-uses variables and households classification; a multi-objective decision component which helps the user to determine the most appropriate forecasting method and conservation measures; • a set of mathematical models to provide the analytical capability for quantifying descriptors, producing multiple outputs etc.; • a user-interface with access to the various functional components of the system and the various help/explain files; • a set of pre- and post-processors which support editing of the inputs data and the visualisation or analysis of model output, in addition to handling scenarios for each of the models or variables; • a set of help files which are used to provide the user with the necessary assistance if for any reason, a more detailed explanation is required, based on a hypertext; In order to demonstrate the system capability, DFMS has been applied to the Swindon demand area of Thames Water Utilities Ltd.

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Spencer, Malik. "CHRISTINE: A Flexible Web-Based Clinical Decision Support System." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1282052336.

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Liou, Yihwa Irene. "The use of a group decision support system environment for knowledge acquisition." Diss., The University of Arizona, 1989. http://hdl.handle.net/10150/184717.

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Knowledge acquisition is not only the most important but also most difficult task knowledge engineers face when they begin to develop expert systems. One of the first problems they encounter is the need to identify at least one individual with appropriate expertise who is able and willing to participate in the development project. They must also be able to use a variety of techniques to elicit the knowledge that they require. These include such traditional knowledge acquisition methods as interviewing, thinking-aloud protocol analysis, on-site observation, and repertory grid analysis. As expert system applications have become more complex, knowledge engineers have found that they must work with and tap the domain knowledge of not one but several individuals. They have also discovered that the traditional methods do not work well in eliciting the knowledge residing in a group of individuals. The complexity of the systems, the difficulties inherent in working with multiple experts, and the lack of appropriate tools have combined to make the knowledge acquisition task even more arduous and time consuming. Group Decision Support Systems (GDSS) have been proven to be useful tools for improving the efficiency and effectiveness of a multiplicity of group activities. It would appear that by bringing experts together in a GDSS environment and using computer-based tools to facilitate group interaction and information exchange, a knowledge engineer could eliminate many of these problems. This research was designed to explore the possibility of using a GDSS environment to facilitate knowledge acquisition from multiple experts. The primary research question was "Does A GDSS environment facilitate the acquisition of knowledge from multiple experts?" The principle contributions of this research are (1) demonstration of the first use of a GDSS environment to elicit knowledge from multiple experts; (2) establishment of a methodology for knowledge acquisition in a GDSS environment; (3) development of process models for acquiring knowledge; (4) development of guidelines for designing and evaluating group support tools; and (5) recognition of some implications of using a computer-supported cooperative approach to extract knowledge from a group of experts. (Abstract shortened with permission of author.)

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Galipalli, Ashwin Kumar, and Haritha Jyothi Madyala. "Process to Build an Efficient Decision Support System." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-16405.

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Decision support systems will be an asset dealing with the complexity involved in many decision situations for companies, organizations and societies by integrating different aspects into a holistic pattern. That creates a close relationship to systems science since systems thinking promote holism as a profitable way to handle complexity. The ideal decision support system should not be used to make automatic decisions but to assist a human being in the decision process. That process is sometimes described as a model consisting of the phases, intelligence, design and choice. Intelligence is needed to understand the situation and find the information that is needed to continue the process. Design means designing different alternatives and in the last phase, choice, the alternatives are evaluated and the best alternative is chosen. A good decision support system should give the user assistance through the whole process. The main purpose of our research is identifying the process of building an efficient Decision Support System. The target groups are the people who are working with multinational companies that are specialized in constructing and delivering decision support systems to end users. The number of target companies involved in this study is only two and is limited Indian Multinational companies. The theoretical study helps in identifying the basic characteristics of a decision support system, exploring the types of decision support systems used in current organizations, resulting if there is any particular standard for constructing DSS today and signifying approach for constructing a user friendly decision support system by analyzing the existing literature related to DSS. At the same time, empirical study advances the research problem from a practical angle. The conclusion for this research is a comprehensive report in relation to the varieties of Decision Support Systems used in today’s organizations, qualities that a decision support system ought to possess and suggested process to be implemented for building an efficient decision support system.
Program: Magisterutbildning i informatik

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Wei, Peng. "Web and knowledge-based decision support system for measurement uncertainty evaluation." Thesis, Brunel University, 2009. http://bura.brunel.ac.uk/handle/2438/10114.

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In metrology, measurement uncertainty is understood as a range in which the true value of the measurement is likely to fall in. The recent years have seen a rapid development in evaluation of measurement uncertainty. ISO Guide to the Expression of Uncertainty in Measurement (GUM 1995) is the primary guiding document for measurement uncertainty. More recently, the Supplement 1 to the "Guide to the expression of uncertainty in measurement" – Propagation of distributions using a Monte Carlo method (GUM SP1) was published in November 2008. A number of software tools for measurement uncertainty have been developed and made available based on these two documents. The current software tools are mainly desktop applications utilising numeric computation with limited mathematical model handling capacity. A novel and generic web-based application, web-based Knowledge-Based Decision Support System (KB-DSS), has been proposed and developed in this research for measurement uncertainty evaluation. A Model-View-Controller architecture pattern is used for the proposed system. Under this general architecture, a web-based KB-DSS is developed based on an integration of the Expert System and Decision Support System approach. In the proposed uncertainty evaluation system, three knowledge bases as sub-systems are developed to implement the evaluation for measurement uncertainty. The first sub-system, the Measurement Modelling Knowledge Base (MMKB), assists the user in establishing the appropriate mathematical model for the measurand, a critical process for uncertainty evaluation. The second sub-system, GUM Framework Knowledge Base, carries out the uncertainty evaluation process based on the GUM Uncertainty Framework using symbolic computation, whilst the third sub-system, GUM SP1 MCM Framework Knowledge Base, conducts the uncertainty calculation according to the GUM SP1 Framework numerically based on Monte Carlo Method. The design and implementation of the proposed system and sub-systems are discussed in the thesis, supported by elaboration of the implementation steps and examples. Discussions and justifications on the technologies and approaches used for the sub-systems and their components are also presented. These include Drools, Oracle database, Java, JSP, Java Transfer Object, AJAX and Matlab. The proposed web-based KB-DSS has been evaluated through case studies and the performance of the system has been validated by the example results. As an established methodology and practical tool, the research will make valuable contributions to the field of measurement uncertainty evaluation.

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Al, Assaf Safwan. "An expert decision support system for strategic housing management in local authorities." Thesis, Cardiff University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305094.

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O'Connor, Rory V. "An architecture for an intelligent assistant system for use in software project planning." Thesis, City University London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340375.

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Yoberd, Belmond. "An energy expert advisor and decision support system for aluminium melting and casting." Thesis, Kingston University, 1994. http://eprints.kingston.ac.uk/20580/.

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The aim of this project was to develop and implement an expert advisor system to provide information for selecting and scheduling several items of small foundry plants using electric resistance bale-out furnaces, to optimise metal use and reduce energy costs. This involved study in formulating the procedures and developing a “foundry user friendly” expert system for giving advice to unskilled operatives in what was a complex multi- variable process. This system (FOES) included investigation and development of an advising system on the casting of a large numbers of different objects cast under different operating conditions and electricity tariffs. Knowledge elicitation techniques were developed and used during the complicated knowledge election process. Since this research programme intended to look at the complete process of melting, holding and pouring of the aluminium alloy, complex electricity tariffs were incorporated into the expert system in order to accurately calculate the energy cost of each process. A sub-section of the FOES system (DAD) could advise the unskilled foundry operative identify and eliminate the seven most common aluminium alloy casting defect by using a novel technique of incorporating actual defect photographs which were digitally scanned into the system.

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Aggarwal, Ajay K. "On developing an expert system : a knowledge base for GP formulation and analysis /." Diss., This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-07132007-143148/.

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Books on the topic "Decision Support System and Expert System"

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Pulat, P. Simin. A decision support system for tolerance allocation. Norman, Okla: School of Industrial Engineering, University of Oklahoma, 1993.

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Porter, Olen D. A decision support system for the diagnosis of aircraft emergencies. Monterey, Calif: Naval Postgraduate School, 1986.

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Johnston, Michael. An expert system to support prescribed burning decisions. Vancouver, B.C: Forest Economics and Policy Analysis Research Unit, 1990.

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L, Cats-Baril William, and Alemi Farrokh, eds. Systems to support health policy analysis: Theory, models, and uses. Ann Arbor, Mich: Health Administration Press, 1992.

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Richardson, H. J. Developing an expert system to provide decision support for women workers seeking unfair dismissal on the grounds of pregnancy. Manchester: UMIST, 1996.

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Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.

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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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Varlamov, Oleg. 18 examples of mivar expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1248446.

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Many years of research on mivar technologies of logical artificial intelligence have allowed us to create a new powerful, versatile and fast tool, which is called "multidimensional open gnoseological active net" — "multidimensional open gnoseological active net: MOGAN". This tool allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format, and it can be used to model cause-and-effect relationships in different subject areas and create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with "Big Knowledge". The reader, after studying this tutorial, you will be able to create mivar expert system with the help of CASMI Wi!Mi. Designed for students, bachelors, masters and postgraduate students studying artificial intelligence methods, as well as for users, experts and specialists, creating a system of information processing and management, mivar models, expert systems, automated control systems, systems of decision support and Recommender systems.

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Turban, Efraim. Decision support and expert systems: Management support systems. 3rd ed. New York: Macmillan, 1993.

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Turban, Efraim. Decision support and expert systems: Management support systems. 4th ed. London: Prentice-Hall International, 1995.

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Turban, Efraim. Decision support and expert systems: Management support systems. 2nd ed. New York: Macmillan, 1990.

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Book chapters on the topic "Decision Support System and Expert System"

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Wigertz, Ove B., Paul D. Clayton, Peter J. Haug, and T. Allan Pryor. "Expert System Knowledge Transfer." In Expert Systems and Decision Support in Medicine, 371–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-48706-4_60.

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Peters, John W., and Adedeji B. Badiru. "Heuristic decision support system database structure for diagnostic expert systems." In Manufacturing Decision Support Systems, 111–43. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4613-1189-8_6.

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Ireland, A. M., and M. A. H. Dempster. "A Financial Expert Decision Support System." In Mathematical Models for Decision Support, 415–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83555-1_25.

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Seuchter, S. A., H. U. Prokosch, D. Keir, and M. H. Skolnick. "An Expert System for Genetic Counseling." In Expert Systems and Decision Support in Medicine, 196–201. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-48706-4_30.

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Nagy, L. L., B. Christensen, and T. J. Nagy. "An Expert System for Medical Evacuations." In Expert Systems and Decision Support in Medicine, 270–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-48706-4_41.

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Kuratani, Yoshiro. "The Intelligent Decision Support System: Synthesis of a Decision Support System and an Expert System." In Lecture Notes in Economics and Mathematical Systems, 65–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-46609-0_7.

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Schaaf, R., R. Engelbrecht, and W. Scholz. "SMA Scholz — MEDIS — Drug Information System." In Expert Systems and Decision Support in Medicine, 573. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-48706-4_104.

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Eckardt, M., U. Ruch, and M. Schnabel. "Knowledge Based System for Diagnosing Anemia." In Expert Systems and Decision Support in Medicine, 562. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-48706-4_93.

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Lam, David C. L., and David A. Swayne. "Integrating Database, Spreadsheet, Graphics, GIS, Statistics, Simulation Models and Expert Systems: Experiences with the RAISON System on Microcomputers." In Decision Support Systems, 429–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76048-8_20.

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Li, Wenjian, and Ruiqing Zhao. "Shock Trauma Manual Computer Consultation Expert System." In Expert Systems and Decision Support in Medicine, 264–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-48706-4_40.

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Conference papers on the topic "Decision Support System and Expert System"

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Tammineni, S. V., J. P. Scanlan, and P. A. S. Reed. "Expert Decision Support System for Costing of Aircraft Gas Turbine Engine." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27798.

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This paper presents the role of performance parameters for an aircraft gas turbine engine in influencing the configuration of systems/sub-systems and related costs at the preliminary design phase. An expert decision support system is provided that employs a hybrid decision tree inference mechanism to provide cost estimates for configurations of a system/sub-system based on the specifications of the performance parameters by the user. The inference mechanism provides a way of dealing with certain and uncertain knowledge extracted from experts for decision making. This assists the designer in the preliminary phase of design in evaluating decisions on the specification of performance parameters by considering their cost influence. The designer can optimize the system/sub-system configuration and performance parameters based on cost. A case study demonstrating the research applied to an exemplar turbojet engine compressor sub-system is presented.

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Pethe, V. P., C. P. Rippey, and L. V. Kale. "A specialized expert system for judicial decision support." In the second international conference. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/74014.74038.

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Kupershtein, Leonid M., Tatiana B. Martyniuk, Myhail D. Krencin, Andriy V. Kozhemiako, Yurii Bezsmertnyi, Halyna Bezsmertna, Maksat Kolimoldayev, Andrzej Smolarz, Róża Weryńska-Bieniasz, and Svetlana Uvaysova. "Neural expert decision support system for stroke diagnosis." In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2017, edited by Ryszard S. Romaniuk and Maciej Linczuk. SPIE, 2017. http://dx.doi.org/10.1117/12.2280956.

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Yazici, H., K. Muthuswamy, and J. Vila. "An expert system approach for interface management in decision support systems." In Proceedings of the Twenty-Fifth Hawaii International Conference on System Sciences. IEEE, 1992. http://dx.doi.org/10.1109/hicss.1992.183469.

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Urrea, R. A., J. M. Restrepo, J. C. Valencia, and J. M. Ortiz. "Mobility Expert System Decision Support System for Medellin's Traffic Control Center." In MOVICI-MOYCOT 2018: Joint Conference for Urban Mobility in the Smart City. Institution of Engineering and Technology, 2018. http://dx.doi.org/10.1049/ic.2018.0008.

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Hajiha, Ali, Javad J. Jassbi, and Sohrab Khanmohammadi. "A Fuzzy Expert Decision Support System for Job Assignment." In 2007 IEEE International Fuzzy Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/fuzzy.2007.4295550.

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"SOFTWARE MAINTENANCE EXPERT SYSTEM (SMxpert) - A Decision Support Instrument." In 7th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0002526601420148.

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de la Torre, Francisco Jose, Laura Garcia Munoz, Roberto Gonzalez Barahona, Maite Avelino Carmona, and Manuel Gil Perez. "Decision Support Console for System Administration Based on an Expert System Approach." In 2009 Second International Conference on Dependability (DEPEND). IEEE, 2009. http://dx.doi.org/10.1109/depend.2009.29.

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Peden, J. M., and J. J. Tovar. "Sand Prediction and Exclusion Decision Support Using an Expert System." In Offshore Europe. Society of Petroleum Engineers, 1991. http://dx.doi.org/10.2118/23165-ms.

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Beecham, Sarah, Noel Carroll, and John Noll. "A Decision Support System for Global Team Management: Expert Evaluation." In 2012 Seventh IEEE International Conference on Global Software Engineering Workshop (ICGSEW). IEEE, 2012. http://dx.doi.org/10.1109/icgsew.2012.14.

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Reports on the topic "Decision Support System and Expert System"

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Fuerst, Michael J., Donald K. Hicks, and Robert D. Neathammer. Framework for Developing an Expert System-Based Decision Support System for Managing U.S. Army Directorates of Engineering and Housing Equipment Fleets. Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada230722.

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Luqi. System Engineering and Evolution Decision Support. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada395539.

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Luqi. System Engineering and Evolution Decision Support. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada384684.

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Meador, Christopher. Burn Resuscitation Decision Support System (BRDSS). Fort Belvoir, VA: Defense Technical Information Center, November 2014. http://dx.doi.org/10.21236/ada608762.

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Heacox, N. J., M. L. Quinn, R. T. Kelly, J. W. Gwynne, and R. J. Smille. Decision Support System for Coalition Operations. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada406338.

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Kappes, Sandra F., Simon S. Kim, Patrick J. Tanner, Roddy J. Williams, and Louis F. Cohn. Employing Expert System Technologies to Real Property Management Decision making. Fort Belvoir, VA: Defense Technical Information Center, July 1990. http://dx.doi.org/10.21236/ada226176.

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Kwasniak, Andrzej, Bogdan Chivoiu, and Andrzej Tarko. Expert System to Support Site Investigation for Safety Improvement. West Lafayette, IN: Purdue University, 2006. http://dx.doi.org/10.5703/1288284313411.

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Hibbard, Carol, and Theresa O'Brien. The Source Selection Evaluation Decision Support System. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada275676.

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Bostick, K. V. Decision support system to select cover systems. Office of Scientific and Technical Information (OSTI), February 1995. http://dx.doi.org/10.2172/10116819.

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Wan, Hung-da, Fengshan F. Chen, and Glenn W. Kuriger. An Intelligent Decision Support System for Workforce Forecast. Fort Belvoir, VA: Defense Technical Information Center, January 2011. http://dx.doi.org/10.21236/ada537920.

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