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Journal articles on the topic 'Computer decision support systems'

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Published: 4 June 2021
Last updated: 15 February 2022

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1

Payne, Thomas H. "Computer Decision Support Systems." Chest 118, no. 2 (August 2000): 47S—52S. http://dx.doi.org/10.1378/chest.118.2_suppl.47s.

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2

Marković-Blagojević, Marija, Bratislav Mikarić, and Dušan Trajković. "Computer systems as decision support." Trendovi u poslovanju 2, no. 2 (2014): 33–42. http://dx.doi.org/10.5937/trendpos1402033m.

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3

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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4

French, Simon, and Murray Turoff. "Decision support systems." Communications of the ACM 50, no. 3 (March 2007): 39–40. http://dx.doi.org/10.1145/1226736.1226762.

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5

Finlay, Paul N. "Decision support systems." Data Processing 28, no. 8 (October 1986): 434–37. http://dx.doi.org/10.1016/0011-684x(86)90427-2.

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6

McDonald, Clement J. "Analyzing Computer-based Decision Support Systems." Annals of Internal Medicine 121, no. 6 (September 15, 1994): 469. http://dx.doi.org/10.7326/0003-4819-121-6-199409150-00017.

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7

Haynes, R. Brian. "Analyzing Computer-based Decision Support Systems." Annals of Internal Medicine 121, no. 6 (September 15, 1994): 469. http://dx.doi.org/10.7326/0003-4819-121-6-199409150-00032.

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8

Thornett, Andrew Martyn. "Computer decision support systems in general practice." International Journal of Information Management 21, no. 1 (February 2001): 39–47. http://dx.doi.org/10.1016/s0268-4012(00)00049-9.

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9

Chin, Jerry M. "Using computer algebra in decision support systems." International Journal of Computer Mathematics 45, no. 3-4 (January 1992): 133–43. http://dx.doi.org/10.1080/00207169208804124.

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10

Ballantine, Malcolm. "Computer-assisted careers guidance systems as decision support systems." British Journal of Guidance and Counselling 14, no. 1 (January 1, 1986): 21–32. http://dx.doi.org/10.1080/03069888600760031.

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11

Ballantine, Malcolm. "Computer-Assisted Careers Guidance Systems as Decision Support Systems." British Journal of Guidance & Counselling 14, no. 1 (January 1986): 21–32. http://dx.doi.org/10.1080/03069888608253494.

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12

De, Suranjan, Shimon Y. Nof, and Andrew B. Whinston. "Decision support in computer-integrated manufacturing." Decision Support Systems 1, no. 1 (January 1985): 37–55. http://dx.doi.org/10.1016/0167-9236(85)90196-4.

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13

Nolan, Andrew M., Carol A. Norquay, Jonathan G. A. Dartnell, and Ken J. Harvey. "Electronic prescribing and computer‐assisted decision support systems." Medical Journal of Australia 171, no. 10 (November 1999): 541–43. http://dx.doi.org/10.5694/j.1326-5377.1999.tb123791.x.

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14

Klein, Joseph, and Herman Ronen. "The Contribution of a Decision Support System to Educational Decision-Making Processes." Journal of Educational Computing Research 28, no. 3 (April 2003): 273–90. http://dx.doi.org/10.2190/3u4e-xqr0-mk52-agam.

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In the light of reports of bias, the present study investigated the hypothesis that administrative educational decisions assisted by Decision Support Systems (DSS) are characterized by different pedagogical and organizational orientation than decisions made without computer assistance. One hundred and ten high school teachers were asked to suggest solutions to problems that arose in two school incidents. After reaching decisions, respondents were asked to rank, in order of importance, the considerations that guided them. The data were processed by DSS software, which calculated the highest-scoring option for each incident. This option was compared with the one chosen by the respondents, taking into account variables of seniority, educational level, specialization, and self-efficacy. DSS-aided decisions promoted greater moderation and enhanced cooperation among interested parties in comparison to decisions made without computer aid. The implications of these findings for future division of decision-making tasks between people and computers is discussed.
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15

Knight, Jonathan D., and John D. Mumford. "Decision Support Systems in Crop Protection." Outlook on Agriculture 23, no. 4 (December 1994): 281–85. http://dx.doi.org/10.1177/003072709402300408.

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All farmers and growers have at some time faced the decision of whether to control a pest in their crop. In order to make the correct decision the farmer needs access to, and an understanding of, sufficient information relevant to such pest problems. Decision support systems are able to help farmers make these difficult decisions by providing information in an easily understandable and quickly accessed form. The increasing use of computers by farmers for record-keeping and business management is putting the hardware necessary for the implementation of these systems onto more and more farms. The scarcity of expert advice, increasingly complex decisions and reduced economic margins all increase the importance of making the right pest management decision at the right time. It is against this background that decision support systems have an important role to play in the fight against losses caused by pests and diseases.
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16

Sauter, Vicki L., Srikanth Mudigonda, Ashok Subramanian, and Ray Creely. "Visualization-Based Decision Support Systems." International Journal of Decision Support System Technology 3, no. 1 (January 2011): 1–20. http://dx.doi.org/10.4018/jdsst.2011010101.

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Increasingly, decision makers are incorporating large quantities of interrelated data in their decision making. Decision support systems need to provide visualization tools to help decision makers glean trends and patterns that will help them design and evaluate alternative actions. While visualization software that might be incorporated into decision support systems is available, the literature does not provide sufficient guidelines for selecting among possible visualizations or their attributes. This paper describes a case study of the development of a visualization component to represent regional relationship data. It addresses the specific information goals of the target organization, various constraints that needed to be satisfied, and how the goals were achieved via a suitable choice of visualization technology and visualization algorithms. The development process highlighted the need for specific visualizations to be driven by the specific problem characteristics as much as general rules of visualization. Lessons learned during the process and how these lessons may be generalized to address similar requirements is presented.
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17

Cassie, Claire. "Marketing decision support systems." Industrial Management & Data Systems 97, no. 8 (December 1997): 293–96. http://dx.doi.org/10.1108/02635579710195000.

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18

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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19

Adams, Roy. "Decision support systems: an overview." VINE 26, no. 2 (February 1996): 3–7. http://dx.doi.org/10.1108/eb040599.

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20

O'Keefe, Robert M., and Tim McEachern. "Web-based customer decision support systems." Communications of the ACM 41, no. 3 (March 1998): 71–78. http://dx.doi.org/10.1145/272287.272300.

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21

Blaha, M. "Data warehouses and decision support systems." Computer 34, no. 12 (December 2001): 38–39. http://dx.doi.org/10.1109/mc.2001.970555.

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22

Meystel, A. M. "Multiresolutional hierarchical decision support systems." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 33, no. 1 (February 2003): 86–101. http://dx.doi.org/10.1109/tsmcc.2003.809866.

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23

Klimeš, Cyril, and Radim Farana. "Model of the Decision Support System under Condition of Non-Determination for Manufacturing Process Support." Applied Mechanics and Materials 611 (August 2014): 416–23. http://dx.doi.org/10.4028/www.scientific.net/amm.611.416.

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Decision support systems mean interactive computer systems, which assist to decision making subjects to utilize both data and models to solve non-structured issues. These systems were established mainly on the basis of a risk analysis, utilizing the experience/skills, conclusion making and intuition, enabling very fast and flexible analysis with a good response, enabling the application of manager intuition and judgment this way. However, such decisions are often based on uncertain information. This fact requires the establishment of other decision support models.
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24

Hersh, M. A. "Sustainable decision making and decision support systems." Computing & Control Engineering Journal 9, no. 6 (December 1, 1998): 289–95. http://dx.doi.org/10.1049/cce:19980610.

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25

Dujmović, Jozo. "Graded logic for decision support systems." International Journal of Intelligent Systems 34, no. 11 (August 25, 2019): 2900–2919. http://dx.doi.org/10.1002/int.22177.

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26

Jones, Matthew R. "Interactive modelling in decision support systems." Interacting with Computers 3, no. 2 (August 1991): 167–86. http://dx.doi.org/10.1016/0953-5438(91)90010-y.

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27

Rao, V. Kenkata. "Flexible Access to Models and Data through Decision Support Systems." Vikalpa: The Journal for Decision Makers 12, no. 1 (January 1987): 51–56. http://dx.doi.org/10.1177/0256090919870107.

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Although computers have entered the Indian scene in a big way, their applications are either mundane or characterized by rigidity. Decision Support System (DSS) is a new departure: it is a computer application that supports decision making in managers by providing them with flexible access to models and data. Its potential in India for application is vast, but largely untapped, argues V Venkata Rao. The author goes on to explain the reasons why organization have been slow in adopting the DSS and gives guidelines for designing one.
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28

Jiang, J. J., and G. Klein. "Side effects of decision guidance in decision support systems." Interacting with Computers 12, no. 5 (April 2000): 469–81. http://dx.doi.org/10.1016/s0953-5438(99)00018-1.

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29

MANHEIM, MARVIN L. "Creativity-Support Systems for Planning, Design and Decision support*." Computer-Aided Civil and Infrastructure Engineering 1, no. 1 (November 6, 2008): 14–31. http://dx.doi.org/10.1111/j.1467-8667.1986.tb00110.x.

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30

García-Manotas, Ignacio, Eduardo Lupiani, Francisco García-Sánchez, and Rafael Valencia-García. "Populating Knowledge Based Decision Support Systems." International Journal of Decision Support System Technology 2, no. 1 (January 2010): 1–20. http://dx.doi.org/10.4018/jdsst.2010101601.

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Knowledge-based decision support systems (KBDSS) hold up business and organizational decision-making activities on the basis of the knowledge available concerning the domain under question. One of the main problems with knowledge bases is that their construction is a time-consuming task. A number of methodologies have been proposed in the context of the Semantic Web to assist in the development of ontology-based knowledge bases. In this paper, we present a technique for populating knowledge bases from semi-structured text which take advantage of the semantic underpinnings provided by ontologies. This technique has been tested and evaluated in the financial domain
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31

Corradino, Claudia, Gaetana Ganci, Giuseppe Bilotta, Annalisa Cappello, Ciro Negro, and Luigi Fortuna. "Smart Decision Support Systems for Volcanic Applications." Energies 12, no. 7 (March 28, 2019): 1216. http://dx.doi.org/10.3390/en12071216.

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The huge amount of information coming from remote sensors on satellites has allowed monitoring changes in the planetary environment from about 50 years. These instruments are widely adopted to observe extreme thermal events such as eruptive phenomena in volcanic areas. Although the availability of so many different infrared sensors makes these instruments suitable to observe different kind of thermal phenomena, choosing the right infrared sensor to monitor each thermal event is not straightforward. In fact, the decision should take into account both the main features of the phenomena under investigation, e.g., its size and temperatures, that are often not known a priori, and the instruments specifications, e.g., spatial resolution. Here, a smart decision support system (SDSS) is proposed to address this task. In particular, we used a SDSS to simulate remote sensors responses, collect data coming from three different classes of remote sensors, retrieve information about the main features of the observed thermal event and, consequently, select the most suitable infrared remote sensor for the specific observed phenomena. Results obtained for a real case of study at Etna volcano is shown.
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32

Argiolas, Michele, Maurizio Atzori, Nicoletta Dessì, and Barbara Pes. "Dataspaces Enhancing Decision Support Systems in Clouds." International Journal of Web Portals 4, no. 2 (April 2012): 35–55. http://dx.doi.org/10.4018/jwp.2012040103.

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Business Organizations increasingly rely on publicly accessible communication infrastructures such as the Internet and Cloud computing, in order to manage their internal processes and offer their services to financial actors, businesses and ordinary people worldwide. However, little attention has been paid to embracing Software-as-a-Service (SaaS) philosophy for supporting business analysts in evaluating effectiveness and relevance of business information or finding patterns to direct them on more sources of data. This paper proposes a flexible architectural framework for modelling application services useful in supporting business decision processes. This architecture goes beyond the static schemas offered by data warehouse systems as it supports including data gathered from heterogeneous sources and added-value services offered in Internet or provided by Cloud infrastructures. Central to this architecture is the concept of dataspace as a reference model for structuring information relevant to a particular organization, regardless of its format and location. A case study of a real world implementation is presented dealing with supporting decisions in the real estate domain.
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33

Chenthamarakshan, V., K. Dixit, M. Gattani, M. Goyal, P. Gupta, N. Kambhatla, R. M. Lotlikar, et al. "Effective decision support systems for workforce deployment." IBM Journal of Research and Development 54, no. 6 (November 2010): 5:1–5:15. http://dx.doi.org/10.1147/jrd.2010.2074310.

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34

Tran, Cong, Ajith Abraham, and Lakhmi Jain. "Decision support systems using hybrid neurocomputing." Neurocomputing 61 (October 2004): 85–97. http://dx.doi.org/10.1016/j.neucom.2004.03.006.

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35

Löf, Staffan, and Björn Möller. "Knowledge systems and management decision support." Expert Systems with Applications 3, no. 2 (January 1991): 187–94. http://dx.doi.org/10.1016/0957-4174(91)90147-7.

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36

Beynon, Meurig, Suwanna Rasmequan, and Steve Russ. "A new paradigm for computer-based decision support." Decision Support Systems 33, no. 2 (June 2002): 127–42. http://dx.doi.org/10.1016/s0167-9236(01)00140-3.

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37

Sharmi, Meenakshi, and Himanshu Aggarwal. "Methodologies of Legacy Clinical Decision Support System." International Journal of Computers in Clinical Practice 2, no. 2 (July 2017): 20–37. http://dx.doi.org/10.4018/ijccp.2017070102.

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Information technology playing a prominent role in the field of medical by incorporating the clinical decision support system (CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at right time. Nowadays, clinical decision support systems are a dynamic research area in the field of computers, but the lack of understanding, as well as functions of the system, make adoption slow by physicians and patients. The literature review of this article focuses on the overview of legacy CDSS, the kind of methodologies and classifiers employed to prepare such a decision support system using a non-technical approach to the physician and the strategy-makers. This article provides understanding of the clinical decision support along with the gateway to physician, and to policy-makers to develop and deploy decision support systems as a healthcare service to make the quick, agile and right decision. Future directions to handle the uncertainties along with the challenges of clinical decision support systems are also enlightened in this study.
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38

Krzywicki, D., Ł. Faber, A. Byrski, and M. Kisiel-Dorohinicki. "Computing agents for decision support systems." Future Generation Computer Systems 37 (July 2014): 390–400. http://dx.doi.org/10.1016/j.future.2014.02.002.

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39

Leslie, Gavin. "Decision support systems: learning to apply computers." Aslib Proceedings 38, no. 9 (September 1986): 275–83. http://dx.doi.org/10.1108/eb060048.

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40

Goggin, Leigh S., Robert H. Eikelboom, and Marcus D. Atlas. "Clinical decision support systems and computer-aided diagnosis in otology." Otolaryngology–Head and Neck Surgery 136, no. 4_suppl (April 2007): s21—s26. http://dx.doi.org/10.1016/j.otohns.2007.01.028.

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41

Calantone, Roger J., and Michael H. Morris. "The Utilisation of Computer‐Based Decision Support Systems in Transportation." International Journal of Physical Distribution & Materials Management 15, no. 7 (July 1985): 5–18. http://dx.doi.org/10.1108/eb014620.

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42

Rehg, William, Peter McBurney, and Simon Parsons. "Computer decision-support systems for public argumentation: assessing deliberative legitimacy." AI & SOCIETY 19, no. 3 (November 23, 2004): 203–28. http://dx.doi.org/10.1007/s00146-004-0313-2.

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43

Berlin, Amy, Marco Sorani, and Ida Sim. "A taxonomic description of computer-based clinical decision support systems." Journal of Biomedical Informatics 39, no. 6 (December 2006): 656–67. http://dx.doi.org/10.1016/j.jbi.2005.12.003.

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44

Belle, Ashwin, Mark A. Kon, and Kayvan Najarian. "Biomedical Informatics for Computer-Aided Decision Support Systems: A Survey." Scientific World Journal 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/769639.

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The volumes of current patient data as well as their complexity make clinical decision making more challenging than ever for physicians and other care givers. This situation calls for the use of biomedical informatics methods to process data and form recommendations and/or predictions to assist such decision makers. The design, implementation, and use of biomedical informatics systems in the form of computer-aided decision support have become essential and widely used over the last two decades. This paper provides a brief review of such systems, their application protocols and methodologies, and the future challenges and directions they suggest.
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45

Tata, Cyrus. "Introduction: Why Study Judicial Decision Support Systems?" International Review of Law, Computers & Technology 14, no. 3 (November 2000): 279–82. http://dx.doi.org/10.1080/713673365.

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46

Holsapple, Clyde W., and Mark P. Sena. "The Decision-Support Characteristics of ERP Systems." International Journal of Human-Computer Interaction 16, no. 1 (August 2003): 101–23. http://dx.doi.org/10.1207/s15327590ijhc1601_7.

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47

Rauscher, H. M., Keith Reynolds, and Harald Vacik. "Decision-support systems for forest management." Computers and Electronics in Agriculture 49, no. 1 (October 2005): 1–5. http://dx.doi.org/10.1016/j.compag.2005.02.001.

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48

Abraham, Ajith, and Crina Grosan. "Decision Support Systems Using Ensemble Genetic Programming." Journal of Information & Knowledge Management 05, no. 04 (December 2006): 303–13. http://dx.doi.org/10.1142/s0219649206001566.

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This paper proposes a decision support system for tactical air combat environment using a combination of unsupervised learning for clustering the data and an ensemble of three well-known genetic programming techniques to classify the different decision regions accurately. The genetic programming techniques used are: Linear Genetic programming (LGP), Multi-Expression Programming (MEP) and Gene Expression Programming (GEP). The clustered data are used as the inputs to the genetic programming algorithms. Some simulation results demonstrating the difference of these techniques are also performed. Test results reveal that the proposed ensemble method performed better than the individual GP approaches and that the method is efficient.
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49

Forgionne, Guisseppi A. "DECISION TECHNOLOGY SYSTEMS A Step Toward Complete Decision Support." Journal of Information Systems Management 8, no. 4 (January 1991): 34–43. http://dx.doi.org/10.1080/07399019108965009.

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50

Ullman, David G., and Bruce D'Ambrosio. "Taxonomy for classifying engineering decision problems and support systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 9, no. 5 (November 1995): 427–38. http://dx.doi.org/10.1017/s0890060400002742.

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AbstractThe design of even the simplest product requires thousands of decisions. Yet few of these decisions are supported with methods on paper or on computers. Is this because engineering design decisions do not need support or is it because techniques have yet to be developed that are usable on a wide basis? In considering this question a wide range of decision problem characteristics need to be addressed. In engineering design some decisions are made by individuals, others by teams – some are about the product and others about the processes that support the product – some are based on complete, consistent, quantitative data and others on sparse, conflicting, qualitative discussions. To address the reasons why so little support is used and the characteristics of potentially useful decision support tools, a taxonomy of decision characteristics is proposed. This taxonomy is used to classify current techniques and to define the requirements for an ideal engineering design decision support system.
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