Relevant bibliographies by topics / Intelligent decision support

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Intelligent decision support'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Intelligent decision support"

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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Intelligent decision support"

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Sandhu, Raghbir Singh. "Intelligent spatial decision support systems." Thesis, University College London (University of London), 1998. http://discovery.ucl.ac.uk/1317911/.

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This thesis investigates the conceptual and methodological issues for the development of Intelligent Spatial Decision Support Systems (ISDSS). These are spatial decision support systems (SDSS) integrating intelligent systems techniques (Genetic Algorithms, Neural Networks, Expert Systems, Fuzzy Logic and Nonlinear methods) with traditional modelling and statistical methods for the analysis of spatial problems. The principal aim of this work is to verify the feasibility of heterogeneous systems for spatial decision support derived from a combination of traditional numerical techniques and intel
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Almejalli, Khaled A., Keshav P. Dahal, and M. Alamgir Hossain. "Intelligent traffic control decision support system." Springer-Verlag, 2007. http://hdl.handle.net/10454/2554.

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When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be
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Hodgkin, Julie. "Provision of intelligent user support in decision support systems." Thesis, University of Strathclyde, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366908.

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Tzavaras, Aris. "Intelligent decision support systems in ventilation management." Thesis, City University London, 2009. http://openaccess.city.ac.uk/12084/.

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Introduction: Intensive Care Unit (ICU) medical personnel, in an ongoing process termed ventilation management, utilize patient physiology and pathology data to define ventilator apparatus settings. Aims: The aim of the research is to develop and evaluate in comparison hybrid ventilation advisor systems, that could support ventilation management process, specific to lung pathology for patients ventilated in control mode. Methodology: A questionnaire was designed and circulated to Intensivists. Patient data, as defined by the questionnaire analysis, were collected and categorized into three lun
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Jackson-Smale, Andrew David. "Intelligent decision support systems for optimised diabetes." Thesis, Oxford Brookes University, 1993. https://radar.brookes.ac.uk/radar/items/cf9b324c-c59f-4b12-b511-1dbe692725f2/1/.

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Computers now pervade the field of medicine extensively; one recent innovation is the development of intelligent decision support systems for inexperienced or non-specialist pbysicians, or in some cases for use by patients. In this thesis a critical review of computer systems in medicine, with special reference to decision support systems, is followed by a detailed description of the development and evaluation of two new, interacting, intelligent decision support systems in the domain of diabetes. Since the discovery of insulin in 1922, insulin replacement therapy for the treatment of diabetes
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BENVENUTI, FILIPPO. "Intelligent Decision Support Systems in Public Transport." Doctoral thesis, Università Politecnica delle Marche, 2018. http://hdl.handle.net/11566/252967.

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Nelle ultime decadi, l'incremento dell'efficienza di un Sistema di Trasporto Pubblico urbano (STP) sta assumendo un ruolo chiave nello sviluppo della società moderna. Ciononostante, la realtà è piuttosto lontana da un "mondo STP ideale", in cui i Trasporti Pubblici urbani non hanno particolari problemi nel fornire servizi efficaci ed efficienti nelle grandi citta. La realtà ci racconta che i gestori dei sistemi di trasporto urbano, nella maggior parte delle grandi città, stanno incontrando difficoltà sempre crescenti a mantenere alti livelli di efficienza. Per rispondere a questi problemi, in
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Edmiston, Marcia R., Darrell R. Gregg, and David G. Wirth. "Decision support for reconnaissance using intelligent software agents." Thesis, Monterey, California. Naval Postgraduate School, 1998. http://hdl.handle.net/10945/8192.

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Approved for public release; distribution is unlimited<br>Research in reconnaissance traditionally focuses on data detection and discrimination methods. Less emphasis is placed on transforming the collected data into useful information and presenting it to key command and control nodes. Information not presented in a timely manner is excluded from the decision process. This thesis proposes a conceptual model of intelligent software agents to support the human decision process and reconnaissance- related tasks. The Mobile Agent Reconnaissance Kit (MARK) suggests a hierarchy of software agents t
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Liang, Qixiong. "An intelligent strategy planning tool for decision support." Thesis, Queen's University Belfast, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300611.

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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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Backory, Jay K. "Intelligent monitoring, modelling and decision support in anaesthesia." Thesis, University of Sheffield, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301427.

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Books on the topic "Intelligent decision support"

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Słowiński, Roman, ed. Intelligent Decision Support. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-7975-9.

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Bortfeldt, Andreas, Jörg Homberger, Herbert Kopfer, Giselher Pankratz, and Reinhard Strangmeier, eds. Intelligent Decision Support. Gabler, 2008. http://dx.doi.org/10.1007/978-3-8349-9777-7.

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Sànchez-Marrè, Miquel. Intelligent Decision Support Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87790-3.

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Greco, Salvatore, Vincent Mousseau, Jerzy Stefanowski, and Constantin Zopounidis, eds. Intelligent Decision Support Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96318-7.

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Leung, Yee. Intelligent Spatial Decision Support Systems. Springer Berlin Heidelberg, 1997.

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Valencia-García, Rafael, Mario Andrés Paredes-Valverde, María del Pilar Salas-Zárate, and Giner Alor-Hernández, eds. Exploring Intelligent Decision Support Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74002-7.

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Leung, Yee. Intelligent Spatial Decision Support Systems. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60714-1.

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Leung, Yee. Intelligent spatial decision support systems. Springer Verlag, 1997.

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1935-, Lasker G. E., International Institute for Advanced Studies in Systems Research and Cybernetics., and International Conference on Systems Research, Informatics and Cybernetics (8th : 1996 : Baden-Baden, Germany), eds. Advances in support systems research: Intelligent management support systems, intelligent information systems, intelligent decision support systems, intelligent decision making, intelligent systems methodology. International Institute for Advanced Studies in Systems Research and Cybernetics, 1996.

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Tweedale, Jeffrey W., Rui Neves-Silva, Lakhmi C. Jain, Gloria Phillips-Wren, Junzo Watada, and Robert J. Howlett, eds. Intelligent Decision Technology Support in Practice. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-21209-8.

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Book chapters on the topic "Intelligent decision support"

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Bandyopadhyay, Susmita. "Intelligent Decision Support System." In Decision Support System. CRC Press, 2023. http://dx.doi.org/10.1201/9781003307655-14.

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Grzymala-Busse, Jerzy W. "LERS-A System for Learning from Examples Based on Rough Sets." In Intelligent Decision Support. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-7975-9_1.

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Hadjimichael, Michael, and Anita Wasilewska. "Rough Sets-Based Study of Voter Preference in 1988 U.S.A. Presidential Election." In Intelligent Decision Support. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-7975-9_10.

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Reinhard, Andrzej, Urszula Wybraniec-Skardowska, Boguslaw Stawski, and Tomasz Weber. "An Application of Rough Set Theory in the Control of Water Conditions on a Polder." In Intelligent Decision Support. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-7975-9_11.

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Teghem, Jacques, and Jean-Marie Charlet. "Use of “Rough Sets” Method to Draw Premonitory Factors for Earthquakes by Emphasing Gas Geochemistry: The Case of a Low Seismic Activity Context, in Belgium." In Intelligent Decision Support. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-7975-9_12.

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Łuba, Tadeusz, and Janusz Rybnik. "Rough Sets and Some Aspects of Logic Synthesis." In Intelligent Decision Support. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-7975-9_13.

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Dubois, Didier, and Henri Prade. "Putting Rough Sets and Fuzzy Sets Together." In Intelligent Decision Support. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-7975-9_14.

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Nakamura, Akira. "Applications of Fuzzy-Rough Classifications to Logics." In Intelligent Decision Support. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-7975-9_15.

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Krusińska, Ewa, Ankica Babic, Roman Słowiński, and Jerzy Stefanowski. "Comparison of the Rough Sets Approach and Probabilistic Data Analysis Techniques on a Common Set of Medical Data." In Intelligent Decision Support. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-7975-9_16.

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Teghem, Jacques, and Mohammed Benjelloun. "Some Experiments to Compare Rough Sets Theory and Ordinal Statistical Methods." In Intelligent Decision Support. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-7975-9_17.

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Conference papers on the topic "Intelligent decision support"

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Abdelshafy, Alaa Ezzat, Ahmed Aboelyazed Elsawy, and Shaimaa Talaat Amen. "Intelligent Decision Support System for Crime Investigation." In 2024 International Conference on Computer and Applications (ICCA). IEEE, 2024. https://doi.org/10.1109/icca62237.2024.10927988.

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Attiwi, Madi Hashem, J. O. Almass, Ayham Sameer Faaq Roukiah, et al. "Unremarkable AI: Intelligent Decision Support in Critical Clinical Decision-Making." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10923113.

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Bhatti, Uzair Aslam, Guo Wenlong, Mughair Aslam Bhatti, et al. "ABi-LT: Intelligent Medical Decision Model Clinical Decision Support System." In 2025 2nd International Conference on Electronic Engineering and Information Systems (EEISS). IEEE, 2025. https://doi.org/10.1109/eeiss65394.2025.11085933.

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Ahmed, Zeinab E., Aisha H. A. Hashim, Rania A. Mokhtar, and Mamoon M. Saeed. "Intelligent Decision Support Systems: Transforming Smart Cities Management." In 2024 1st International Conference on Emerging Technologies for Dependable Internet of Things (ICETI). IEEE, 2024. https://doi.org/10.1109/iceti63946.2024.10777112.

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Wu, Wenyuan, Jiaxin Lin, Bo Hu, Qiang Qin, and Hanye Huang. "Intelligent Decision Support Algorithm Based on Multimodal Data." In 2024 International Conference on Electronics and Devices, Computational Science (ICEDCS). IEEE, 2024. https://doi.org/10.1109/icedcs64328.2024.00201.

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Jadhav, Balasaheb, Vaidehi Ligde, Rushikesh Malpani, Rushikesh Lakhotiya, and Phalguni Savale. "CropOptimizer: Intelligent Crop Forecasting and Decision Support System." In 2024 Global Conference on Communications and Information Technologies (GCCIT). IEEE, 2024. https://doi.org/10.1109/gccit63234.2024.10862107.

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Zheng, Yongqing, Han Yu, Kun Zhang, Yuliang Shi, Cyril Leung, and Chunyan Miao. "Intelligent Decision Support for Improving Power Management." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/965.

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With the development and adoption of the electricity information tracking system in China, real-time electricity consumption big data have become available to enable artificial intelligence (AI) to help power companies and the urban management departments to make demand side management decisions. We demonstrate the Power Intelligent Decision Support (PIDS) platform, which can generate Orderly Power Utilization (OPU) decision recommendations and perform Demand Response (DR) implementation management based on a short-term load forecasting model. It can also provide different users with query and
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Hu, Jing. "Intelligent Financial Decision Support System Using Artificial Intelligence." In 2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC). IEEE, 2023. http://dx.doi.org/10.1109/icaisc58445.2023.10199365.

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Lindgaard, Gitte. "Intelligent decision support in medicine." In the 14th European conference. ACM Press, 2007. http://dx.doi.org/10.1145/1362550.1362554.

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"INTELLIGENT CLINICAL DECISION SUPPORT SYSTEMS." In International Conference on Health Informatics. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002740802820287.

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Reports on the topic "Intelligent decision support"

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

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Roth, Steven F., and Stephen F. Smith. Intelligent Support for Human Computer Interaction and Decision-Making in Distribution Planning and Scheduling Systems. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada263985.

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Danylchuk, Hanna B., and Serhiy O. Semerikov. Advances in machine learning for the innovation economy: in the shadow of war. Криворізький державний педагогічний університет, 2023. http://dx.doi.org/10.31812/123456789/7732.

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This preface introduces the selected and revised papers presented at the 10th International Conference on Monitoring, Modeling &amp; Management of Emergent Economy (M3E2 2022), held online in Ukraine, on November 17-18, 2022. The conference aimed to bring together researchers, practitioners, and students from various fields to exchange ideas, share experiences, and discuss challenges and opportunities in applying computational intelligence and data science for the innovation economy. The innovation economy is a term that describes the emerging paradigm of economic development that is driven by
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Rinaudo, Christina, William Leonard, Jaylen Hopson, Christopher Morey, Robert Hilborn, and Theresa Coumbe. Enabling understanding of artificial intelligence (AI) agent wargaming decisions through visualizations. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48418.

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The process to develop options for military planning course of action (COA) development and analysis relies on human subject matter expertise. Analyzing COAs requires examining several factors and understanding complex interactions and dependencies associated with actions, reactions, proposed counteractions, and multiple reasonable outcomes. In Fiscal Year 2021, the Institute for Systems Engineering Research team completed efforts resulting in a wargaming maritime framework capable of training an artificial intelligence (AI) agent with deep reinforcement learning (DRL) techniques within a mari
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Chernavskikh, Vladislav, and Jules Palayer. Impact of Military Artificial Intelligence on Nuclear Escalation Risk. Stockholm International Peace Research Institute, 2025. https://doi.org/10.55163/fziw8544.

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Increasing integration of artificial intelligence (AI) into military systems has the potential to influence nuclear escalation even when that integration occurs outside nuclear weapon systems. Non-nuclear applications of military AI may compress decision-making timelines, potentially increasing miscalculation risks during a crisis. Opaque recommendations from an AI-powered decision-support system can bias a decision-maker towards acting, while autonomy in a system with counterforce potential may undermine strategic stability by threatening the integrity of second-strike capabilities. Such uses
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Blanchard, Alexander, and Laura Bruun. Autonomous Weapon Systems and AI-enabled Decision Support Systems in Military Targeting: A Comparison and Recommended Policy Responses. Stockholm International Peace Research Institute, 2025. https://doi.org/10.55163/yqby3151.

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This report provides a comparative analysis of autonomous weapon systems (AWS) and artificial intelligence-enabled decision support systems (AI-DSS) used in in military targeting. It examines how both technologies shape human roles in decision making about the use of force, assesses their respective risks of unintended harm and analyses the key issues they raise under international law. By mapping the key similarities and differences between AWS and AI-DSS—particularly in their deployment across different phases of the targeting cycle—the report clarifies where existing policy frameworks apply
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Blanchard, Alexander, and Laura Bruun. Bias in Military Artificial Intelligence. Stockholm International Peace Research Institute, 2024. https://doi.org/10.55163/cjft9557.

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To support states involved in the policy debate on military artificial intelligence (AI), this background paper provides a deeper examination of the issue of bias in military AI. Three insights arise. First, policymakers could usefully develop an account of bias in military AI that captures shared concern around unfairness. If so, ‘bias in military AI’ might be taken to refer to the systemically skewed performance of a military AI system that leads to unjustifiably different behaviours—which may perpetuate or exacerbate harmful or discriminatory outcomes—depending on such social characteristic
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Perdigão, Rui A. P. Strengthening Multi-Hazard Resilience with Quantum Aerospace Systems Intelligence. Synergistic Manifolds, 2024. http://dx.doi.org/10.46337/240301.

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The present work further enhances and deploys our Quantum Aerospace Systems Intelligence technologies (DOI: 10.46337/quasi.230901) onto Multi-Hazard risk assessment and action, from sensing and prediction to modelling, decision support and active response, towards strengthening its fundamental knowledge, awareness and resilience in the face of multi-domain challenges. Moreover, it introduces our updated post-quantum aerospace engineering ecosystem for empowering active system dynamic capabilities to mitigate or even counter multi-hazard threats from space, leveraging our high energy technologi
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Christie, Lorna. Interpretable machine learning. Parliamentary Office of Science and Technology, 2020. http://dx.doi.org/10.58248/pn633.

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Machine learning (ML, a type of artificial intelligence) is increasingly being used to support decision making in a variety of applications including recruitment and clinical diagnoses. While ML has many advantages, there are concerns that in some cases it may not be possible to explain completely how its outputs have been produced. This POSTnote gives an overview of ML and its role in decision-making. It examines the challenges of understanding how a complex ML system has reached its output, and some of the technical approaches to making ML easier to interpret. It also gives a brief overview
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Perdigão, Rui A. P. Information Physical Artificial Intelligence in Complex System Dynamics: Breaking Frontiers in Nonlinear Analytics, Model Design and Socio-Environmental Decision Support in a Coevolutionary World. Meteoceanics, 2020. http://dx.doi.org/10.46337/200930.

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