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Статті в журналах з теми "Decision-support approach"

Автор: Grafiati

Опубліковано: 21 грудня 2024

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1

Patel, Alpa R., and Dr Jayesh M. Patel. "Integrated approach of OLAP and Data Mining for Decision support." International Journal of Scientific Research 2, no. 3 (June 1, 2012): 81–82. http://dx.doi.org/10.15373/22778179/mar2013/27.

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2

El Fazziki, Abdelaziz, Abderrahmane Sadiq, and Mohamed Sadgal. "An MDA based multi-agent approach for decision support systems development." International Journal of Academic Research 6, no. 1 (January 30, 2014): 221–31. http://dx.doi.org/10.7813/2075-4124.2014/6-1/a.29.

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3

Edwards, John S., Michel Klein, and Leif B. Methlie. "Expert Systems: A Decision Support Approach." Journal of the Operational Research Society 42, no. 5 (May 1991): 424. http://dx.doi.org/10.2307/2583757.

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4

Edwards, John S. "Expert Systems: A Decision Support Approach." Journal of the Operational Research Society 42, no. 5 (May 1991): 424. http://dx.doi.org/10.1057/jors.1991.86.

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5

Ligȩza, Antoni. "Expert systems approach to decision support." European Journal of Operational Research 37, no. 1 (October 1988): 100–110. http://dx.doi.org/10.1016/0377-2217(88)90284-6.

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6

Peter Jennergren, L. "Expert systems: A decision support approach." European Journal of Operational Research 48, no. 2 (September 1990): 285–86. http://dx.doi.org/10.1016/0377-2217(90)90386-p.

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7

Tontchev, Nikolay, and Zoran Čekerevac. "MULTICRITERIA DECISION SUPPORT - APPROACH AND APPLICATION IN THE FIELD OF MATERIALS SCIENCE." MEST Journal 2, no. 1 (January 15, 2014): 18–29. http://dx.doi.org/10.12709/mest.02.02.01.03.

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8

Yudin, V. N., and L. E. Karpov. "Hybrid approach to building decision support system." Proceedings of the Institute for System Programming of RAS 24 (2013): 447–56. http://dx.doi.org/10.15514/ispras-2013-24-20.

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9

Goodwin, Paul. "A psychological approach to decision support systems." International Journal of Forecasting 13, no. 1 (March 1997): 149–50. http://dx.doi.org/10.1016/s0169-2070(96)00722-4.

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10

Dekker, Andre. "Rapid Learning Approach for Decision Support Systems." Physica Medica 28, no. 4 (October 2012): 333. http://dx.doi.org/10.1016/j.ejmp.2012.06.009.

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11

Cosset, Jean-Claude, Yannis Siskos, and Constantin Zopounidis. "Evaluating country risk: A decision support approach." Global Finance Journal 3, no. 1 (March 1992): 79–95. http://dx.doi.org/10.1016/1044-0283(92)90006-7.

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12

Jankowski, Piotr, Grant Fraley, and Edzer Pebesma. "An exploratory approach to spatial decision support." Computers, Environment and Urban Systems 45 (May 2014): 101–13. http://dx.doi.org/10.1016/j.compenvurbsys.2014.02.008.

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13

Hoch, Stephen J., and David A. Schkade. "A Psychological Approach to Decision Support Systems." Management Science 42, no. 1 (January 1996): 51–64. http://dx.doi.org/10.1287/mnsc.42.1.51.

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14

Sen, Arun, and Gautam Biswas. "Decision support systems: An expert systems approach." Decision Support Systems 1, no. 3 (September 1985): 197–204. http://dx.doi.org/10.1016/0167-9236(85)90239-8.

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15

Agayev, F. B. "Analytical support of the procedural decision-making process." Mathematical machines and systems 4 (2020): 20–32. http://dx.doi.org/10.34121/1028-9763-2020-4-20-32.

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Анотація:

In the process of legal research, private scientific methods are developed and used to study legal reality, such as the method of comparative jurisprudence, the method of interpretation (explanation) and the formal legal method. However, at the present stage of legal research, it is impossible to be limited only by these methods. Even legal scholars who consistently defend the status of dogmatic jurisprudence recognize that the application of these methods, with all their merits, sets a limiting framework in un-derstanding the practical action of positive law and the originality of its theoretical vision. Nevertheless, the application of these methods in the study of legal reality allows us to draw a conclusion about the general trends in the development of evaluative concepts that are fundamental in civil procedural law. It is offered an approach to the formation of a system of information support of procedural decision-making based on the application of fuzzy inference mechanism implemented in the logical basis of the feedforward multilayer neural network. Under this approach, a method to overcome the semantic uncer-tainty in the evaluation terms of procedural law is developed by using appropriate terms (fuzzy sets) of corresponding linguistic variables. As an example it is selected the Articles on “Violation of copyright or neighboring rights” of the Criminal Code of the Azerbaijan Republic based on which has been proposed formalism for the evaluation concept of “significant harm” in relation to the sanction applied. For mak-ing an adequate to evaluation concept notion it is proposed grading scale of possible sanctions, obtained on the basis of the description of the relevant legal norms in terms of fuzzy implicative rules.

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16

Korhonen, Pekka J., and Subhash C. Narula. "An evolutionary approach to support decision making with linear decision models." Journal of Multi-Criteria Decision Analysis 2, no. 2 (August 1993): 111–19. http://dx.doi.org/10.1002/mcda.4020020206.

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17

Chen, Tianhua, Changjing Shang, Pan Su, Elpida Keravnou-Papailiou, Yitian Zhao, Grigoris Antoniou, and Qiang Shen. "A Decision Tree-Initialised Neuro-fuzzy Approach for Clinical Decision Support." Artificial Intelligence in Medicine 111 (January 2021): 101986. http://dx.doi.org/10.1016/j.artmed.2020.101986.

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18

Barach, P., V. Levashenko, and E. Zaitseva. "Fuzzy Decision Trees in Medical Decision Making Support Systems." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 8, no. 1 (September 2019): 37–42. http://dx.doi.org/10.1177/2327857919081009.

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Анотація:

Fuzzy decision trees represent classification knowledge more naturally to the way of human thinking and are more robust in tolerating imprecise, conflict, and missing information. Decision Making Support Systems are used widely in clinical medicine because decisions play an important role in diagnostic processes. Decision trees are a very suitable candidate for induction of simple decision-making models with the possibility of automatic learning. The goal of this paper is to demonstrate a new approach for predictive data mining models in clinical medicine. This approach is based on induction of fuzzy decision trees. This approach allows us to build decision-making modesl with different properties (ordered, stability etc.). Three new types of fuzzy decision trees (non-ordered, ordered and stable) are considered in the paper. Induction of these fuzzy decision trees is based on cumulative information estimates. Results of experimental investigation are presented. Predictive data mining is becoming an essential instrument for researchers and clinical practitioners in medicine. Using new approaches based on fuzzy decision trees allows to increase the prediction accuracy. Decision trees are a very suitable candidate for induction using simple decision-making models with the possibility of automatic and AI learning.

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19

Loeb, Gerald E. "A new approach to medical diagnostic decision support." Journal of Biomedical Informatics 116 (April 2021): 103723. http://dx.doi.org/10.1016/j.jbi.2021.103723.

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20

Bali, B., B. Y., and N. Nathan. "Intelligent Approach to Decision Support on Drug Abuse." International Journal of Computer Applications 156, no. 2 (December 15, 2016): 25–29. http://dx.doi.org/10.5120/ijca2016912372.

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21

Kwahk, Kee-Young, Hee-Woong Kim, and Hock Chuan Chan. "A Knowledge Integration Approach for Organizational Decision Support." Journal of Database Management 18, no. 2 (April 2007): 41–61. http://dx.doi.org/10.4018/jdm.2007040103.

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22

Maynard, Sean, Frada Burstein, and Dzvid Arnott. "A Multi-faceted Decision Support System Evaluation Approach." Journal of Decision System 10, no. 3-4 (December 30, 2001): 395–428. http://dx.doi.org/10.3166/jds.10.395-428.

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23

Gachet, Alexandre, and Pius Haettenschwiler. "A Decentralized Approach to Distributed Decision Support Systems." Journal of Decision Systems 12, no. 2 (January 2003): 141–58. http://dx.doi.org/10.3166/jds.12.141-158.

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24

Angehrn, Albert A., and Hans-Jakob Lüthi. "Intelligent Decision Support Systems: A Visual Interactive Approach." Interfaces 20, no. 6 (December 1990): 17–28. http://dx.doi.org/10.1287/inte.20.6.17.

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25

Kottemann, Jeffrey E., Kathleen M. Boyer-Wright, Joel F. Kincaid, and Fred D. Davis. "Understanding Decision-Support Effectiveness: A Computer Simulation Approach." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 39, no. 1 (January 2009): 57–65. http://dx.doi.org/10.1109/tsmca.2008.2007992.

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26

Espinasse, B., and D. Pascot. "Decision Support Systems (DSS): A Knowledge Oriented Approach." IFAC Proceedings Volumes 20, no. 10 (September 1987): 105–8. http://dx.doi.org/10.1016/s1474-6670(17)59042-4.

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27

Luo, Cuicui. "A comprehensive decision support approach for credit scoring." Industrial Management & Data Systems 120, no. 2 (October 3, 2019): 280–90. http://dx.doi.org/10.1108/imds-03-2019-0182.

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Анотація:

Purpose The purpose of this paper is to provide a comprehensive decision support approach in credit risk assessment. Design/methodology/approach A comprehensive decision support approach is proposed for credit scoring and prediction. The predictive performance of the new approach has been investigated by using data including number and text. Findings The results demonstrate that the proposed approach achieves better and more stable classification accuracy than the single classifiers in most cases. Meanwhile, the prediction accuracy of individual classifiers is also improved by the proposed approach. Originality/value This study provides a comprehensive model for credit risk scoring and provides valuable information to the existing literature on credit scoring by using artificial intelligence.

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28

Green, Nick. "Regional and Urban GIS: A Decision Support Approach." International Planning Studies 15, no. 4 (November 2010): 339–41. http://dx.doi.org/10.1080/13563475.2010.517293.

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29

ESPEJO, G., G. L'HUILLIER, and R. WEBER. "A game-theoretical approach for policing decision support." European Journal of Applied Mathematics 27, no. 3 (September 14, 2015): 338–56. http://dx.doi.org/10.1017/s0956792515000480.

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Recently, many security-related problems have gained increasing attention from a quantitative perspective. In this paper, we propose a game-theoretical approach to model the interaction between police forces and delinquents in public places. In the well-known Stackelberg game, a leader is faced with only one follower. However, in our application, the police are simultaneously faced with many offenders, who may be organized or act independently of each other. This application motivates the development of two games: a classical leader-follower interaction between police and organized criminals on the one hand and a novel approach between the leader and selfishly acting offenders on the other. It is of special interest that the effect of crime displacement under police surveillance be anticipated by the proposed models. Results using data from a simulated environment emphasise how these models can provide decision support for policing outperforming traditional strategies.

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30

Pawlak, Zdzisław. "Rough set approach to knowledge-based decision support." European Journal of Operational Research 99, no. 1 (May 1997): 48–57. http://dx.doi.org/10.1016/s0377-2217(96)00382-7.

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31

Murtaza, Mirza B., Deborah J. Fisher, and Miroslaw J. Skibniewski. "Knowledge‐Based Approach to Modular Construction Decision Support." Journal of Construction Engineering and Management 119, no. 1 (March 1993): 115–30. http://dx.doi.org/10.1061/(asce)0733-9364(1993)119:1(115).

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32

van der Enden, C. "Improving capital budgeting: A decision support system approach." European Journal of Operational Research 19, no. 1 (January 1985): 144–45. http://dx.doi.org/10.1016/0377-2217(85)90322-4.

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33

Carlson, Christopher K. "Information management approach and support to decision-making." Information & Management 15, no. 3 (January 1988): 135–49. http://dx.doi.org/10.1016/0378-7206(88)90069-9.

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34

Turban, Efraim, Duanning Zhou, and Jian Ma. "A group decision support approach to evaluating journals." Information & Management 42, no. 1 (December 2004): 31–44. http://dx.doi.org/10.1016/j.im.2003.12.003.

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35

Jin, Shangzhu, Jun Peng, Zuojin Li, and Qiang Shen. "Bidirectional approximate reasoning-based approach for decision support." Information Sciences 506 (January 2020): 99–112. http://dx.doi.org/10.1016/j.ins.2019.08.019.

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36

Deogun, Jitender S. "A conceptual approach to decision support system models." Information Processing & Management 24, no. 4 (January 1988): 429–48. http://dx.doi.org/10.1016/0306-4573(88)90046-5.

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37

Azaiez, Noura, Jalel Akaichi, and Jeffrey Hsu. "Moving Beyond Traditional Decision Support Systems." International Journal of Information Systems and Social Change 8, no. 2 (April 2017): 74–87. http://dx.doi.org/10.4018/ijissc.2017040104.

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Анотація:

Integrating the concept of mobility into the professional and organizational realm offers the possibility of reducing geographical disparities related to organization services. The advances made in technology, geographic information systems and pervasive systems equipped with global positioning (GPS) technologies have been able to bring about an evolution from classic data approaches towards the modeling of trajectory data resulting from moving activities of moving objects. As such, trajectory data needs first to be loaded into a Data Warehouse for analysis purposes. However, the traditional approaches used are poorly suited to handle spatio-temporal data features and also the decision making tasks related to mobility issues. Because of this mismatch, the authors propose to move beyond traditional approaches and propose a repository that is able to analyse trajectories of moving objects. Improving decision making and extracting pertinent knowledge with reduced costs and time expended are the main goals of this revised analysis approach. Thus, the authors propose an approach in which they employ the Bottom-up approach to modeling a Decision Support System which is designed to support Trajectory Data. As an example to illustrate this approach, the authors use a creamery and dairy milk mobile cistern application to demonstrate the effectiveness of their approach.

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38

Mohamed, Abdallah. "Interactive decision support for academic advising." Quality Assurance in Education 24, no. 3 (July 4, 2016): 349–68. http://dx.doi.org/10.1108/qae-03-2013-0011.

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Анотація:

Purpose This paper aims to support academic advising, which plays a crucial role in student success and retention. The paper focuses on one of the most challenging tasks involved in academic advising: individual course scheduling. This task includes not only careful planning for different courses over several semesters according to students’ preferences and goals but also must conform to many student constraints and administrative regulations, some of which may rely on student-specific cases.. Design/methodology/approach This paper introduces a novel approach that tries to provide meaningful support to decision makers involved in the course scheduling problem. The approach uses optimization algorithms to perform a pro-active analysis of the impact of different problem aspects and eventually suggests a balanced study plan that tries to satisfy both student preferences and advisor recommendations without violating any constraints. Findings An initial application of the proposed system is used to discuss its benefits. Originality/value The paper introduces a novel approach that uses optimization techniques to support making efficient decisions during the academic advising process.

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39

Sun, Yong Yong. "Decision Approach of Multi-Agent Intelligent Decision Support System Based on Blackboard." Advanced Materials Research 562-564 (August 2012): 1638–41. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.1638.

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Анотація:

Multi-Agent Intelligent Decision Support System based on blackboard is designed that uses of blackboard system and Agent, it describes the framework and gives the detail about the each Agent. At the same time, decision algorithm based on rough set of system is provided, and its application is described. The system can help to cooperate in Multi-Agent about complicated works.

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40

Cook, Gary J., and Monte R. Swain. "A Computerized Approach to Decision Process Tracing for Decision Support System Design." Decision Sciences 24, no. 5 (September 1993): 931–52. http://dx.doi.org/10.1111/j.1540-5915.1993.tb00497.x.

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41

Riedl, Leopold, Harald Vacik, and Robert Kalasek. "MapModels: a new approach for spatial decision support in silvicultural decision making." Computers and Electronics in Agriculture 27, no. 1-3 (June 2000): 407–12. http://dx.doi.org/10.1016/s0168-1699(00)00096-x.

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42

Gräßer, Felix, Hagen Malberg, and Sebastian Zaunseder. "Neighborhood Optimization for Therapy Decision Support." Current Directions in Biomedical Engineering 5, no. 1 (September 1, 2019): 1–4. http://dx.doi.org/10.1515/cdbme-2019-0001.

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Анотація:

AbstractThis work targets the development of a neighborhood-based Collaborative Filtering therapy recommender system for clinical decision support. The proposed algorithm estimates outcome of pharmaceutical therapy options in order to derive recommendations. Two approaches, namely a Relief-based algorithm and a metric learning approach are investigated. Both adapt similarity functions to the underlying data in order to determine the neighborhood incorporated into the filtering process. The implemented approaches are evaluated regarding the accuracy of the outcome estimations. The metric learning approach can outperform the Relief-based algorithms. It is, however, inferior regarding explainability of the generated recommendations.

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43

Radojevic, Gordana, and Milija Suknovic. "Scoring models: Towards the more realistic approach." Computer Science and Information Systems 6, no. 1 (2009): 45–69. http://dx.doi.org/10.2298/csis0901045r.

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Анотація:

Financial decision-making is one of the most current issues of modern financial management. Financial decision-making is an area where decision support systems, knowledge-based decision support systems, and intelligent decision support systems are successfully applied. In consequence of the importance and complexity of this problem area a large number of methods of support to financial decisionmaking was developed. This paper presents the most important features of two decision support systems, a classical system and a system based on fuzzy logic. The performances of these two models are compared and the advantages achieved through the introduction of fuzzy concepts into the classical decision support systems are determined.

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44

Pinsky, Michael R., Artur Dubrawski, and Gilles Clermont. "Intelligent Clinical Decision Support." Sensors 22, no. 4 (February 12, 2022): 1408. http://dx.doi.org/10.3390/s22041408.

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Анотація:

Early recognition of pathologic cardiorespiratory stress and forecasting cardiorespiratory decompensation in the critically ill is difficult even in highly monitored patients in the Intensive Care Unit (ICU). Instability can be intuitively defined as the overt manifestation of the failure of the host to adequately respond to cardiorespiratory stress. The enormous volume of patient data available in ICU environments, both of high-frequency numeric and waveform data accessible from bedside monitors, plus Electronic Health Record (EHR) data, presents a platform ripe for Artificial Intelligence (AI) approaches for the detection and forecasting of instability, and data-driven intelligent clinical decision support (CDS). Building unbiased, reliable, and usable AI-based systems across health care sites is rapidly becoming a high priority, specifically as these systems relate to diagnostics, forecasting, and bedside clinical decision support. The ICU environment is particularly well-positioned to demonstrate the value of AI in saving lives. The goal is to create AI models embedded in a real-time CDS for forecasting and mitigation of critical instability in ICU patients of sufficient readiness to be deployed at the bedside. Such a system must leverage multi-source patient data, machine learning, systems engineering, and human action expertise, the latter being key to successful CDS implementation in the clinical workflow and evaluation of bias. We present one approach to create an operationally relevant AI-based forecasting CDS system.

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45

Bareja, Krzysztof, and Włodzimierz Ogryczak. "Reference distribution based decision support platform." Journal of Telecommunications and Information Technology, no. 3 (June 26, 2023): 5–14. http://dx.doi.org/10.26636/jtit.2008.3.880.

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Анотація:

There many decision problems where numerous partial achievement functions are considered impartially which makes the distribution of achievements more important than the assignment of several achievements to the specific criteria. Such models are generally related to the evaluation and optimization of various systems which serve many users where quality of service for every individual user defines the criteria. This applies to various technical systems, like to telecommunication ones among others, as well as to social systems. An example arises in location theory, where the clients of a system are entitled to equal treatment according to some community regulations. This paper presents an implementation of decision support framework for such problems. This platform is designed for multiple criteria problems analyzed with the reference distribution approach. Reference distribution approach is an extension of the reference point method.

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46

Hamami, Dalila, Atmani Baghdad, and Carron Shankland. "Decision Support based on Bio-PEPA Modeling and Decision Tree Induction." International Journal of Information Systems in the Service Sector 9, no. 2 (April 2017): 71–101. http://dx.doi.org/10.4018/ijisss.2017040104.

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Анотація:

The problem of selecting determinant features generating appropriate model structure is a challenge in epidemiological modelling. Disease spread is highly complex, and experts develop their understanding of its dynamic over years. There is an increasing variety and volume of epidemiological data which adds to the potential confusion. The authors propose here to make use of that data to better understand disease systems. Decision tree techniques have been extensively used to extract pertinent information and improve decision making. In this paper, the authors propose an innovative structured approach combining decision tree induction with Bio-PEPA computational modelling, and illustrate the approach through application to tuberculosis. By using decision tree induction, the enhanced Bio-PEPA model shows considerable improvement over the initial model with regard to the simulated results matching observed data. The key finding is that the developer expresses a realistic predictive model using relevant features, thus considering this approach as decision support, empowers the epidemiologist in his policy decision making.

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47

Dias Junior, Luiz Dourado. "Integrating Software Repository Mining: A Decision Support Centered Approach." International Journal of Software Engineering & Applications 3, no. 6 (November 30, 2012): 57–75. http://dx.doi.org/10.5121/ijsea.2012.3605.

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48

Koteswara Rao and Shubhamoy Dey. "Decision Support for E-Governance: A Text Mining Approach." International Journal of Managing Information Technology 3, no. 3 (August 30, 2011): 73–91. http://dx.doi.org/10.5121/ijmit.2011.3307.

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49

Keller, Thomas, and Bastin Tony Roy Savarimuthu. "Facilitating Enhanced Decision Support Using a Social Norms Approach." Journal of Electronic Commerce in Organizations 15, no. 2 (April 2017): 1–15. http://dx.doi.org/10.4018/jeco.2017040101.

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Анотація:

Social norms constrain behavior of individuals either through obligating or prohibiting certain types of behavior. Norm-based mechanisms have only recently found applications in enhancing decisions of knowledge workers in an automated business process management context. The norms inferred in the context of business process executions are then recommended to users so as to enable them to make informed decisions. The previous work on prohibition norm inference focused on identifying failure cases, which is now complemented by first inferring norms from the successful process execution cases and then inferring prohibition norms. This approach based on considering social feedback (i.e. inferring what is obliged and prohibited from history logs of process execution) shows encouraging results under uncertain business environments. Using simulation results the paper demonstrates that using the norm based mechanism results in reduced failure rates in the decision making of a knowledge worker while still providing maximum flexibility for the user to choose from a range of actions to execute.

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Al-Issa, Yazan, Ali Mohammad Alqudah, Hiam Alquran, and Ahmed Al Issa. "Pulmonary Diseases Decision Support System Using Deep Learning Approach." Computers, Materials & Continua 73, no. 1 (2022): 311–26. http://dx.doi.org/10.32604/cmc.2022.025750.

Повний текст джерела

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