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Journal articles on the topic 'Intelligent decision support'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Kuhlmann, T., R. Lamping, and C. Massow. "Intelligent decision support." Journal of Materials Processing Technology 76, no. 1-3 (1998): 257–60. http://dx.doi.org/10.1016/s0924-0136(97)00357-9.

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2

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

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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 (A

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3

Gottinger, Hans W., and Peter Weimann. "Intelligent decision support systems." Decision Support Systems 8, no. 4 (1992): 317–32. http://dx.doi.org/10.1016/0167-9236(92)90053-r.

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4

Turayev, Khurshid Salim ugli. "INTELLIGENT DECISION SUPPORT SYSTEM." Chemical Technology, Control and Management 2023, no. 6 (2023): 75–79. http://dx.doi.org/10.59048/2181-1105.1528.

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5

Zhao, Xinyu, and Omer Saeed. "Intelligent Financial Processing Based on Artificial Intelligence-Assisted Decision Support System." Mobile Information Systems 2022 (August 8, 2022): 1–12. http://dx.doi.org/10.1155/2022/6974246.

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In order to improve the effect of intelligent financial processing and decision-making, this paper combines the artificial intelligence-assisted decision support system to construct an intelligent financial processing system. Based on the RBF neural network, this paper studies a fast decision-making algorithm based on short-term efficiency in an adaptive burst communication system. The trained RBF neural network can make quick decisions according to the parameters such as the electromagnetic environment information obtained by perception, and has good communication anti-interference ability, g

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Chelkovan, Viktor. "Use of Artificial Intelligence Methods in the Intelligent Decision Support System." Electronics and Control Systems 1, no. 79 (2024): 16–21. http://dx.doi.org/10.18372/1990-5548.79.18437.

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The article is devoted to the study of the use of artificial intelligence methods in decision-making support systems, particularly in military affairs. Examples of the application of artificial intelligence methods, such as expert systems and machine learning, which can be used to optimize management and strategic decisions in military operations, are considered. Special attention is paid to the use of decision trees in military scenarios as a tool for modeling possible options for the development of events and making optimal decisions. Decision trees allow a decision support system to analyze

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Gupta, Richa. "Decision Support Systems for IoT Based Infrastructures." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 9, no. 3 (2018): 1074–81. http://dx.doi.org/10.17762/turcomat.v9i3.13896.

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Computation offloading divides enormous computational tasks across numerous computer resources, circumventing hardware limits. Edge computing can employ vast quantities of data, individual preferences, and clever algorithms by offloading smart models to high-performance cloud servers. We propose a getaway-centric IoT system to enable intelligent and autonomous IoT devices at the computer infrastructure's edge. Edge computing manages IoT devices by selecting and applying the best control factor from a pool of intelligent services. The cloud-based intelligent service engine provides intelligent

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8

Dong, Andy, and Julie Jupp. "Intelligent decision support and modeling." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26, no. 4 (2012): 371–73. http://dx.doi.org/10.1017/s0890060412000248.

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9

Race, Patrick, Jason Tedor, and Kata L. Nance. "Intelligent Agents for Decision Support." IFAC Proceedings Volumes 31, no. 29 (1998): 94–97. http://dx.doi.org/10.1016/s1474-6670(17)38367-2.

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Race, Patrick, and Kara L. Nance. "Intelligent Agents for Decision Support." IFAC Proceedings Volumes 31, no. 29 (1998): 251–55. http://dx.doi.org/10.1016/s1474-6670(17)38953-x.

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11

Osipov, Vladimir Petrovich, Tatiana Vladimirovna Sivakova, Vladimir Anatolievich Sudakov, and Yury Ivanovich Nechaev. "Intelligent core decision support system." Keldysh Institute Preprints, no. 205 (2018): 1–23. http://dx.doi.org/10.20948/prepr-2018-205.

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12

Qin, Haoshu. "Research on Machine Learning and Intelligent Decision Support System Based on Risk Prediction." Advances in Engineering Technology Research 4, no. 1 (2023): 582. http://dx.doi.org/10.56028/aetr.4.1.582.2023.

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Decision Support System (DSS) is a new technology developed in recent years. Intelligent Decision Support System (IDSS) is the product of the combination of management decision science, operational research, computer science and artificial intelligence. For decision-makers, it takes a long time to accumulate experience to reach a rich enough level of decision-making. On the other hand, the decision-making process is also influenced to a great extent by the subjective consciousness of the decision-maker and the interference of external factors, which makes it easy to make analysis with large de

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13

Bivainis, Juozas, Daina Jansevičiūtė, and Jonas Dieninis. "Decisions of Intelligent Support for Supplier Selection." Business: Theory and Practice 13, no. (3) (2012): 264–74. https://doi.org/10.3846/btp.2012.28.

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This paper examines the problem of supplier selection. The supplier selection process has been expressed as decision cycle, which involves such phases: searching for suppliers, preliminary selection, complex evaluation, negotiations, preparing a contract, monitoring. The present situation in this field has been described in such a way: large interest to increase validity of decisions for supplier selection from side of business practice, diversity of methods and models for supplier selection suggested by researchers, the episodic character of putting into practice this methodical potential. In

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14

Fan Yang. "Application of Mobile Technology in Enterprise Management: From Mobile Office to Intelligent Decision Support." International Journal of Interactive Mobile Technologies (iJIM) 18, no. 18 (2024): 4–18. http://dx.doi.org/10.3991/ijim.v18i18.51493.

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With the rapid advancement of information technology, the application of mobile technology in enterprise management has become increasingly widespread. From mobile office solutions to intelligent decision support systems, the proliferation of mobile technology has not only enhanced operational efficiency but also provided more flexible and convenient management methods. Existing research indicates that the adoption of mobile office solutions enables enterprises to overcome limitations of time and space, improving work efficiency and employee satisfaction. Additionally, the implementation of in

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15

Srikanth Reddy Keshireddy. "Intelligent Decision Support Systems in Management Information Systems Using Hybrid AI Models." Research Briefs on Information and Communication Technology Evolution 10 (December 30, 2024): 211–30. https://doi.org/10.69978/rebicte.v10i.207.

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This study offers the framework of an Intelligent Decision Support System (IDSS) that leverages Hybrid Artificial Intelligence (AI) models consisting of rule-based, machine learning, and deep learning components for Management Information Systems (MIS). The system is designed to improve decision making in terms of accuracy, responsiveness, and adaptability through a direct interface to the enterprise MIS that processes real-time data for context sensitive feedback. An outcome based assessment approach was used to measure the performance of the hybrid IDSS across various business scenarios, and

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16

Pavlo, Zuiev, Zhyvotovskyi Ruslan, Zvieriev Oleksii, et al. "DEVELOPMENT OF COMPLEX METHODOLOGY OF PROCESSING HETEROGENEOUS DATA IN INTELLIGENT DECISION SUPPORT SYSTEMS." Eastern-European Journal of Enterprise Technologies 4, no. 9 (106) (2020): 14–23. https://doi.org/10.15587/1729-4061.2020.208554.

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The complex methodology for processing heterogeneous data in intelligent decision support systems is developed. This method is made to increase the efficiency of processing heterogeneous data in intelligent decision support systems. The complex methodology consists of the following interrelated procedures: heterogeneous data storing model; heterogeneous data synchronization algorithm; heterogeneous data separation algorithm; heterogeneous data indexing algorithm. The model of storing heterogeneous intelligence data, which is the basis of the methodology, differs in the presence of templates of

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17

Markovicheva, Elena V. "Intelligent Decision Support in Criminal Court." Proceedings of the Southwest State University. Series: History and Law 11, no. 6 (2021): 97–108. http://dx.doi.org/10.21869/2223-1501-2021-11-6-97-108.

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Relevance. The achievements of the digital revolution are actively penetrating all spheres of public life. Law en-forcement is also transforming, and modern technologies are increasingly being used in legal proceedings. The criminal process also feels this informational influence. But digitalization of the criminal process can go in different directions and is associated with certain risks. The greatest concern of scientists and practitioners is the use of intelligent systems when passing court decisions, since in this direction digitalization can also have a negative impact on ensuring the ri

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18

Искандеров, Ю. М., А. С. Свистунова, Д. С. Хасанов, and А. С. Чумак. "Intelligent decision support in logistics systems." MORSKIE INTELLEKTUAL`NYE TEHNOLOGII), no. 2(52) (June 5, 2021): 145–53. http://dx.doi.org/10.37220/mit.2021.52.2.021.

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В статье изложен подход, обеспечивающий реализацию комплексных логистических технологий на основе координации перевозок и процедур обработки грузов в интересах выполнения стратегии доставки «точно в срок» и «от двери до двери». Важнейшим фактором, обеспечивающим достижение высокого уровня качества управления транспортно-технологическими процессами, является формирование и использование релевантной системы интеллектуальной поддержки принятия решений, сформированной с учетом достижений новых информационных технологий. Представлены основные классы задач, решаемые при управлении логистическими сис

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19

Iassinovski, S. I., C. Raczy, and A. Artiba. "Intelligent simulation based decision support environment." Computers & Industrial Engineering 37, no. 1-2 (1999): 227–30. http://dx.doi.org/10.1016/s0360-8352(99)00061-3.

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20

Dufournet, J. C. "Intelligent Support to Decision Making Process." IFAC Proceedings Volumes 20, no. 10 (1987): 91–93. http://dx.doi.org/10.1016/s1474-6670(17)59039-4.

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21

Ahuja, Gaurav. "Decision Support System and Intelligent System." DELHI BUSINESS REVIEW 9, no. 1 (2008): 125. https://doi.org/10.51768/dbr.v9i1.91200811.

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22

Zhao, Ziming, and Jinyu Chen. "Application of artificial intelligence technology in the economic development of urban intelligent transportation system." PeerJ Computer Science 11 (March 20, 2025): e2728. https://doi.org/10.7717/peerj-cs.2728.

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With the rapid development of the social economy and the gradual improvement of residents’ living standards, the increasing number of urban cars has exacerbated urban traffic congestion. This article analyzed the application of artificial intelligence (AI) technology in five aspects of urban intelligent transportation systems. Artificial intelligence technology was used in traffic data collection and processing to provide accurate data support for traffic decision-making. A traffic flow prediction model was established for traffic flow prediction and optimized scheduling algorithms were used t

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23

Li, Yawen, Tiannan Zhang, Yushan Yang, and Yuchen Gao. "Artificial intelligence-aided decision support in paediatrics clinical diagnosis: development and future prospects." Journal of International Medical Research 48, no. 9 (2020): 030006052094514. http://dx.doi.org/10.1177/0300060520945141.

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Artificial intelligence (AI)-aided decision support has developed rapidly to meet the needs for effective analysis of substantial data sets from electronic medical records and medical images generated daily, and computer-assisted intelligent drug design. In clinical practice, paediatricians make medical decisions after obtaining a large amount of information about symptoms, physical examinations, laboratory test indicators, special examinations and treatments. This information is used in combination with paediatricians’ knowledge and experience to form the basis of clinical decisions. This dia

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24

MAKOIEDOVA, Valentyna. "INFORMATION TECHNOLOGIES IN DECISION SUPPORT SYSTEMS." Herald of Kyiv National University of Trade and Economics 133, no. 5 (2020): 18–26. http://dx.doi.org/10.31617/visnik.knute.2020(133)02.

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The article considers classification of decision support systems by the way of interaction with the user, by the method of support, by the level of data processed by the system. Types of DSS architecture are presented. Intelligent DSS are analyzed. The main areas of research in the field of artificial intelligence are identified. The advantages and disadvantages of using neural networks are revealed. Cloud technologies are highlighted as an important trend in the development of modern DSS. The properties of Big Data technology are determined.

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25

Matsatsinis, Nikolaos F., and Yannis Siskos. "MARKEX: An intelligent decision support system for product development decisions." European Journal of Operational Research 113, no. 2 (1999): 336–54. http://dx.doi.org/10.1016/s0377-2217(98)00220-3.

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26

Lindgaard, Gitte, Catherine Pyper, Monique Frize, et al. "Intelligent Decision Support in Medicine: back to Bayes?" JUCS - Journal of Universal Computer Science 14, no. (16) (2008): 2720–36. https://doi.org/10.3217/jucs-014-16-2720.

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Decision Support Systems are proliferating rapidly in many areas of human endeavour including clinical medicine and psychology. While these are typically based on rule-based systems, decision trees, or Artificial Neural Networks, this paper argues that Bayes' Theorem can be applied fruitfully to support expert decisions both in dynamically changing situations requiring the system progressively to adapt, and when this is not the case. One example of each of these two types is given. One provides diagnostic support for human decision makers; the other, an e-health mental intervention system prov

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27

Фасха, А. "INTELLECTUAL METHODS OF MANAGEMENT DECISION-MAKING SUPPORT." Organizer of Production 32, no. 2 (2024): 50–55. http://dx.doi.org/10.36622/1810-4894.2024.33.50.005.

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В современной динамичной и сложной бизнес-среде эффективное принятие решений имеет первостепенное значение для успеха организации. В этой статье представлен всесторонний обзор интеллектуальных методов, используемых для поддержки процессов принятия управленческих решений. Опираясь на широкий спектр литературы и эмпирических исследований, в статье рассматриваются различные интеллектуальные методы, включая искусственный интеллект, машинное обучение, анализ данных и экспертные системы. Методы исследования. Статья начинается с описания фундаментальных концепций, лежащих в основе систем поддержки пр

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28

Chechnev, Vasiliy Borisovich. "Using Decision Support Systems in Automation of Decision–Making Processes." Russian Digital Libraries Journal 28, no. 1 (2025): 163–83. https://doi.org/10.26907/1562-5419-2025-28-1-163-183.

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The increasing complexity of decision–making pushes organizations to automate this process. One of the key elements of this is decision support systems. This paper examines their theoretical and practical aspects. The formulated definition, as well as the provided list of the main attributes and functions of systems of this type, made it possible to identify one of the most promising areas in the use of artificial intelligence in this area – multi-agent systems. The conducted analysis of current decision support systems showed the main competitive advantages, common weaknesses of many of them,

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29

Samaras, Georgios D., and Nikolaos F. Matsatsinis. "Intelligent Investor: An intelligent decision support system for portfolio management." Operational Research 4, no. 3 (2004): 357–71. http://dx.doi.org/10.1007/bf02944152.

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30

Jiang, Wei, Tianyi Yang, Ang Li, Yiyu Lin, and Xinzhu Bai. "The Application of Generative Artificial Intelligence in Virtual Financial Advisor and Capital Market Analysis." Academic Journal of Sociology and Management 2, no. 3 (2024): 40–46. https://doi.org/10.5281/zenodo.11112424.

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This paper explores the application of Generating Artificial Intelligence (GAI) to virtual financial advisory and capital market analysis. This paper first introduces the basic principle of GAI and its importance in financial decision-making, and then analyzes the problems and limitations of traditional financial advisory model through literature review and actual data. Then, it discusses the development trend and advantages of intelligent financial advisor, and the difference compared with traditional financial advisor model. Then, it describes the application of generative AI in the financia

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31

Tiahunova, M. Yu, N. O. Ilina, and T. V. Holub. "INTELLIGENT DECISION SUPPORT SYSTEM FOR DRUG SELECTION." Problems of Modeling and Design Automatization 21, no. 1 (2019): 16–22. http://dx.doi.org/10.31474/2074-7888-2019-1-16-22.

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32

Sorokin, A. B. "CONCEPTUAL DESIGN OF INTELLIGENT DECISION SUPPORT SYSTEMS." Ontology of Designing 7, no. 3 (2017): 247–69. http://dx.doi.org/10.18287/2223-9537-2017-7-3-247-269.

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33

Kumar, Neelam Sanjeev, and P. Nirmalkumar. "An Intelligent Decision-Support System For Telemedicine." Applied Mathematics & Information Sciences 12, no. 5 (2018): 983–93. http://dx.doi.org/10.18576/amis/120511.

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34

García-Crespo, Ángel, Ricardo Colomo-Palacios, Juan Miguel Gómez-Berbís, Javier Chamizo, and Ismael Rivera. "Intelligent Decision-Support Systems for e-Tourism." International Journal of Decision Support System Technology 2, no. 1 (2010): 36–48. http://dx.doi.org/10.4018/jdsst.2010101603.

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The Internet has disrupted traditional tourism in which a promising landscape of intelligent service provision has erupted by applying a new lattice of cutting-edge technologies. Thus, the different actors of the tourist services are in a new environment and they should operate in a coordinated manner to increase the value of tourism, to keep current tourists and attract new ones. One of the major players of this technological disruption are Semantic Technologies, which have profited from the combined use of pervasive elements and recommender systems to bring added value to tourist actors. Bas

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35

Startseva, Elena Borisovna, Natalya Olegovna Nikulina, and Anna Ivanovna Malakhova. "KNOWLEDGE ORGANIZATION FOR INTELLIGENT DECISION SUPPORT SYSTEM." Systems Engineering and Information Technologies 3, no. 3 (2021): 17–22. http://dx.doi.org/10.54708/26585014_2021_33717.

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36

Zhang, Zhe, Lei Zou, Wenwen Li, Lynn Usery, Jochen Albrecht, and Marc Armstrong. "Cyberinfrastructure and intelligent spatial decision support systems." Transactions in GIS 25, no. 4 (2021): 1651–53. http://dx.doi.org/10.1111/tgis.12835.

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37

Moudani, Walid, Ahmad Shahin, Fadi Chakik, and Dima Rajab. "Intelligent Decision Support System for Osteoporosis Prediction." International Journal of Intelligent Information Technologies 8, no. 1 (2012): 26–45. http://dx.doi.org/10.4018/ijiit.2012010103.

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The healthcare environment is generally perceived as being information rich yet knowledge poor. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. The information technology may provide alternative approaches to Osteoporosis disease diagnosis. This study examines the potential use of classification techniques on a massive volume of healthcare data, particularly in prediction of patients that may have Osteop

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38

Mentzas, Gregory. "Intelligent process support for corporate decision making." Journal of Decision Systems 6, no. 2 (1997): 117–38. http://dx.doi.org/10.1080/12460125.1997.10511715.

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D. E. Kline, D. A. Bender, and B. A. McCarl. "FINDS: Farm-Level Intelligent Decision Support System." Applied Engineering in Agriculture 5, no. 2 (1989): 273–82. http://dx.doi.org/10.13031/2013.26513.

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40

Jurisica, I., P. Rogers, J. I. Glasgow, et al. "Intelligent decision support for protein crystal growth." IBM Systems Journal 40, no. 2 (2001): 394–409. http://dx.doi.org/10.1147/sj.402.0394.

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41

Moreira, Ailton, and Manuel Filipe Santos. "Multichannel Interaction for Healthcare Intelligent Decision Support." Procedia Computer Science 170 (2020): 1053–58. http://dx.doi.org/10.1016/j.procs.2020.03.074.

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Siskos, Y., and A. Spyridakos. "Intelligent multicriteria decision support: Overview and perspectives." European Journal of Operational Research 113, no. 2 (1999): 236–46. http://dx.doi.org/10.1016/s0377-2217(98)00213-6.

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Gudauskas, Renaldas, Saulė Jokubauskiene, Edmundas Kazimieras Zavadskas, et al. "Intelligent Decision Support System for Leadership Analysis." Procedia Engineering 122 (2015): 172–80. http://dx.doi.org/10.1016/j.proeng.2015.10.022.

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44

Fujita, Hamido, and Enrique Herrera-Viedma. "Guest Editorial: Intelligent Decision Making Support Tools." Knowledge-Based Systems 58 (March 2014): 1–2. http://dx.doi.org/10.1016/j.knosys.2013.11.013.

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Li, Tong, Shan Feng, and Ling Xia Li. "Information visualization for intelligent decision support systems." Knowledge-Based Systems 14, no. 5-6 (2001): 259–62. http://dx.doi.org/10.1016/s0950-7051(01)00104-6.

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Palma-dos-Reis, António, and Fatemeh “Mariam” Zahedi. "Designing personalized intelligent financial decision support systems." Decision Support Systems 26, no. 1 (1999): 31–47. http://dx.doi.org/10.1016/s0167-9236(99)00027-5.

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47

Dolšak, Bojan, and Marina Novak. "Intelligent decision support for structural design analysis." Advanced Engineering Informatics 25, no. 2 (2011): 330–40. http://dx.doi.org/10.1016/j.aei.2010.11.001.

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48

Michalewicz, Z., M. Schmidt, M. Michalewicz, and C. Chiriac. "Case Study: An Intelligent Decision-Support System." IEEE Intelligent Systems 20, no. 4 (2005): 44–49. http://dx.doi.org/10.1109/mis.2005.64.

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Hartanto, Michelle, Ditdit Nugeraha Utama, and Stefania Tomasiello. "Intelligent decision support model for recommending restaurant." Cogent Engineering 7, no. 1 (2020): 1763888. http://dx.doi.org/10.1080/23311916.2020.1763888.

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Sultanov, Murodjon, Gayrat Ishankhodjayev, Rano Parpiyeva, and Nafisa Norboyeva. "Creation of intelligent information decision support systems." E3S Web of Conferences 365 (2023): 04031. http://dx.doi.org/10.1051/e3sconf/202336504031.

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The use of intelligent information decision support systems implies considering the problem area's specifics. The object of study is characterized by the following set of features: - quality and efficiency of decision-making; - vagueness of goals and institutional boundaries; - the plurality of subjects involved in solving the problem; - randomness; - a plurality of mutually influencing factors; - weak formalizability, uniqueness of situations; - latency, concealment, the implicitness of information. For the efficient and reliable functioning of agricultural facilities and enterprises, it is n

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