Готові списки джерел за темами / Intelligence decision support system

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Добірка наукової літератури з теми "Intelligence decision support system"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "Intelligence decision support system"

Rábová, I., V. Konečný, and A. Matiášová. "Decision making with support of artificial intelligence." Agricultural Economics (Zemědělská ekonomika) 51, No. 9 (February 20, 2012): 385–88. http://dx.doi.org/10.17221/5124-agricecon.

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  Development of software modules for decision support is currently a basic trend in the creation of enterprise Information Systems (IS). The IS is basically a support system of the enterprise Decision System, therefore we can regard it as a very important factor of the competition ability and enterprise prosperity. Conventional IS modules provide the enterprise managers a lot of useful information. Nevertheless, own decision process in view of difficulty, complexity or creation disability of decision process model is very often problematic. This contribution is oriented by its content to appropriate choice realization of modules for support decision processes by using of artificial intelligence methods.      

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Varshney, Monika, and Dr Azad Kumar Srivastava. "Decision Support System in Corporate Intelligence." IJARCCE 6, no. 6 (June 30, 2017): 347–50. http://dx.doi.org/10.17148/ijarcce.2017.6661.

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Wang, Juan, Zhi Hong Qie, and Xin Miao Wu. "Bridge Management Intelligence Decision Support System." Applied Mechanics and Materials 361-363 (August 2013): 1182–86. http://dx.doi.org/10.4028/www.scientific.net/amm.361-363.1182.

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In this paper a “bridge management intelligence decision support system” (BMIDSS) will be introduced. This system is based on the theory of decision support, considering the need of bridge manager and decision maker, utilizing database, model base, graphics base, knowledge base, and inference engine to realize date management, damage identification, status evaluation, fault diagnosis, remedy guidance to existing bridge.

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Kultygin, Oleg P., and Irina Lokhtina. "Business intelligence as a decision support system tool." Journal of Applied Informatics 16, no. 91 (February 26, 2021): 52–58. http://dx.doi.org/10.37791/2687-0649-2021-16-1-52-58.

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The relevance of the topic considered in the article is to solve the problems of designing management decision support systems for enterprises based on business analytics technology. The research purpose is to analyze the applied methodologies during the design stage of the enterprise information system, to develop principles for using management decision support systems based on business intelligence. The problem statement is to analyze the technologies available on the market, which deal with business analyst systems, their potential use for decision support systems, and to identify the main stages of business analyst for enterprises. Business intelligence (BI) is information that can be obtained from data contained in the operational systems of a firm, enterprise, corporation, or from external sources. The BI can help the management of a company make the best decision in the chosen sphere of human activity faster, and, consequently, win the competition in the market for goods and services. A decision support system (DSS) which uses business intelligence, is an automated structure designed to assist professionals in making decisions in a complex environment and to objectively analyze a subject area. The decision support system is the result of the integration of management information systems and database management systems (DBMS). The internal development of BI is more cost-effective. The methods used are Structured Analysis and Design Technique and Object-oriented methods. The results of the research: the analysis of the possibilities was conducted and recommendations relating to the use of BI within DSS were given. Competition between BI software in business analysts reduces the cost of products created making them accessible to end-users – producers, traders and corporations.

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Li, Zhi Feng, and Li Yi Zhang. "Real Estate Intelligence System Based on Decision Support Technology." Applied Mechanics and Materials 416-417 (September 2013): 1475–78. http://dx.doi.org/10.4028/www.scientific.net/amm.416-417.1475.

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Aiming at the current situation that the sales decisions of real estate are difficult to be made by the decision maker due to the lack of information during the decision-making process, a decision support model based on the UML modeling language and decision support theories has been proposed in this paper, which designs the modules and system sequence in the decision support system according to the acquisition, operation and output of real estate information. Targeting at the new decision support model, specific implementation steps have been put forward, modeling with UML to provide system modeling mode for the future real estate enterprise.

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Lim, Chang-Gyoon, Kang-Chul Kim, Jae-Hung Yoo, and Jung-Ha Jhung. "Underachievers Realm Decision Support System using Computational Intelligence." Journal of Korean Institute of Intelligent Systems 16, no. 1 (February 1, 2006): 30–36. http://dx.doi.org/10.5391/jkiis.2006.16.1.030.

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Abu-Kabeer, Tasneem, Mohammad Alshraideh, and Ferial Hayajneh. "Intelligence Clinical Decision Support System for Diabetes Management." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 8 (May 20, 2020): 44–60. http://dx.doi.org/10.37394/232018.2020.8.8.

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Diabetes is the most common endocrine disease in all populations and all age groups. The diabetes patient should use correct therapy to live with this disease; there are several of important things to record about the patient and disease that help the doctors to make an optimal decision about the patient treatment. To improve the ability of the physicians, several tools have been proposed by the researchers for developing effective Clinical Decision Support System (CDSS), one of these tools is Artificial Neural Networks(ANN) that are computer paradigms that belong to the computational intelligence family. In this paper, a multilayer perceptron (MLP) feed-forward neural is used to develop a CDSS to determine the regimen type of diabetes management. The input layer of the system includes 25 input variables; the output layer contains one neuron that will produce a number that represents the treatment regimen. A Resilient backpropagation (Rprop) algorithm is used to train the system. In particular, a 10-fold cross-validation scheme was used, an 88.5% classification accuracy from the experiments made on data taken from 228 patient medical records suffering from diabetes (type II).

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Ashraf, Ather, Muhammad Akram, and Mansoor Sarwar. "Fuzzy decision support system for fertilizer." Neural Computing and Applications 25, no. 6 (June 19, 2014): 1495–505. http://dx.doi.org/10.1007/s00521-014-1639-4.

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Lai, Young-Jou, and Ching-Lai Hwang. "IFLP-II: A decision support system." Fuzzy Sets and Systems 54, no. 1 (February 1993): 47–56. http://dx.doi.org/10.1016/0165-0114(93)90359-p.

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Areej Fatima, Sagheer Abbas, and Muahmmad Asif. "Cloud Based Intelligent Decision Support System for Disaster Management Using Fuzzy Logic." Lahore Garrison University Research Journal of Computer Science and Information Technology 2, no. 4 (December 31, 2018): 31–40. http://dx.doi.org/10.54692/lgurjcsit.2018.020458.

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Field of cloud computing is an emerging field in computer science. Computational intelligence and Decisions Supports Systems (DSS) have to gained concerns as a computing solution to planned and unplanned problems of organizations in order to progress decision-making tasks in a better way. In today era, Disaster management is a big problem. To overcome this problem, a real time computation is required. Cloud computing is a tool to offer promising support to decision support system in a real time environment. In this paper, a fuzzy based decision support system is proposed to meet all the requirements using fuzzy logic inference system.

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Дисертації з теми "Intelligence decision support system"

Subramanian, Saravanan. "ANNTAX - an artificial intelligence based decision support system." Thesis, University of Surrey, 2005. http://epubs.surrey.ac.uk/2156/.

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Руденко, Максим Сергійович, Максим Сергеевич Руденко, and Maksym Serhiiovych Rudenko. "Intelligence decision support system for diagnostic oncological diseases." Thesis, Сумський державний університет, 2012. http://essuir.sumdu.edu.ua/handle/123456789/28793.

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Chin, Shou-fong. "Multi-agent as a decision support system." Thesis, Imperial College London, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287526.

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Forghani, Morteza Seyed. "The likelihood of success in management intelligence systems : building a consultant advisory system." Thesis, Loughborough University, 1989. https://dspace.lboro.ac.uk/2134/6845.

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Management Intelligence Systems are a class- of Decision Support Systems aimed at providing intelligence about an ill-structured decision to a decision-maker. The research objective was to build a 'Consultant Advisory System', a computerised model of success, to assist internal consultants in, assessing the likelihood of success for a Management Intelligence System (MINTS). The system would also be capable of allowing the consultant to identify reasons which might lead to a low likelihood of success, so that corrective action can be taken. The approach taken is different from many other studies which have concentrated on the success of a computer-based information system after implementation, rather than assessing success throughout the whole process of initiating, developing and implementing such systems. The research has been based on a detailed survey of the literature on Management Information systems (MIS), and Decision Support Systems (DSS) and 39 field investigations involving detailed interviews with the key actors involved in a MINTS project. Two phases of MINTS development were identified: (A) ensuring a right environment and (B) maintaining relationships. About 280 factors were distilled as significant for the successful development of a MINTS and these have been incorporated in a computerised advisor. Validation of MINTS in general and the advisor in particular is discussed in detail.

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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 intelligent techniques in order to provide superior performance and functionality to that achieved through the use of traditional methods alone. This thesis is composed of four distinct sections: (i) a taxonomy covering the employment of intelligent systems techniques in specific applications of geographical information systems and SDSS; (ii) the development of a prototype ISDSS; (iii) application of the prototype ISDSS to modelling the spatiotemporal dynamics of high technology industry in the South-East of England; and (iv) the development of ISDSS architectures utilising interapplication communication techniques. Existing approaches for implementing modelling tools within SDSS and GIS generally fall into one of two schemes - loose coupling or tight coupling - both of which involve a tradeoff between generality and speed of data interchange. In addition, these schemes offer little use of distributed processing resources. A prototype ISDSS was developed in collaboration with KPMG Peat Marwick's High Technology Practice as a general purpose spatiotemporal analysis tool with particular regard to modelling high technology industry. The GeoAnalyser system furnishes the user with animation and time plotting tools for observing spatiotemporal dynamics; such tools are typically not found in existing SDSS or GIS. Furthermore, GeoAnalyser employs the client/server model of distributed computing to link the front end client application with the back end modelling component contained within the server application. GeoAnalyser demonstrates a hybrid approach to spatial problem solving - the application utilises a nonlinear model for the temporal evolution of spatial variables and a genetic algorithm for calibrating the model in order to establish a good fit for the dataset under investigation. Several novel architectures are proposed for ISDSS based on existing distributed systems technologies. These architectures are assessed in terms of user interface, data and functional integration. Implementation issues are also discussed. The research contributions of this work are four-fold: (i) it lays the foundation for ISDSS as a distinct type of system for spatial decision support by examining the user interface, performance and methodological requirements of such systems; (ii) it explores a new approach for linking modelling techniques and SDSS; (iii) it investigates the possibility of modelling high technology industry; and (iv) it details novel architectures for ISDSS based on distributed systems.

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Weber, P. "Location intelligence : a decision support system for business site selection." Thesis, University College London (University of London), 2011. http://discovery.ucl.ac.uk/1302551/.

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As one of the leading ‘world cities’, London is home to a highly internationalised workforce and is particularly reliant on foreign direct investment (FDI) for its continued economic success. In the face of increasing global competition and a very difficult economic climate, the capital must compete effectively to encourage and support such investors. Given these pressures, the need for a coherent framework for data and methodologies to inform business location decisions is apparent. The research sets out to develop a decision support system to iteratively explore, compare and rank London’s business neighbourhoods. This is achieved through the development, integration and evaluation of spatial data and its manipulation to create an interactive framework to model business location decisions. The effectiveness of the resultant framework is subsequently assessed using a scenario based user evaluation. In this thesis, a geo-business classification for London is created, drawing upon the methods and practices common to geospatial neighbourhood classifications used for profiling consumers. The geo-business classification method encapsulates relevant location variables using Principal Components Analysis into a set of composite area characteristics. Next, the research investigates the implementation of an appropriate Multi-Criteria Decision Making methodology, in this case Analytical Hierarchy Process (AHP) allowing the aggregation of the geo-business classification and decision makers’ preferences into discrete decision alternatives. Lastly, the results of the integration of both data and model through the development of, and evaluation of a web-based prototype are presented. The development of this novel business location decision support framework enables not only improved location decision-making, but also the development of enhanced intelligence on the relative attractiveness of business neighbourhoods according to investor types.

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Zhang, Yan S. M. Program in Media Arts and Sciences (Massachusetts Institute of Technology). "CityMatrix : an urban decision support system augmented by artificial intelligence." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/114059.

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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 75-77).
Cities are our future. Ninety percent of the world's population growth is expected to take place in cities. Not only are cities becoming bigger, they are also becoming more complex and changing even more rapidly. The decision-making process in urban design and urban planning is outdated. Currently, urban decision-making is mostly a top-down process, with community participation only in its late stages. Furthermore, many design decisions are subjective, rather than based on quantifiable performance and data. Urban simulation and artificial intelligence techniques have become more mature and accessible. However, until now these techniques have not been integrated into the urban decision-making process. Current tools for urban planning do not allow both expert and non-expert stakeholders to explore a range of complex scenarios rapidly with real-time feedback. To address these challenges, a dynamic, evidence-based decision support system called CityMatrix was prototyped. The goals of CityMatrix were 1) Designing an intuitive Tangible User Interface (TUI) to improve the accessibility of the decision-making process for non-experts. 2) Creating real-time feedback of multi-objective urban performances to help users evaluate their decisions, thus to enable rapid, collaborative decision-making. 3) Constructing a suggestion-making system that frees stakeholders from excessive, quantitative considerations and allows them to focus on the qualitative aspects of the city, thus helping them define and achieve their goals more efficiently. CityMatrix was augmented by Artificial Intelligence (AI) techniques including Machine Learning simulation predictions and optimization search algorithms. The hypothesis explored in this work was that the decision quality could be improved by the organic combination of both strength of human intelligence and machine intelligence. The system was pilot-tested and evaluated by comparing the problem-solving results of volunteers, with or without Al suggestions. Both quantitative and qualitative analytic results showed that CityMatrix is a promising tool that helps both professional and nonprofessional users understand the city better to make more collaborative and better-informed decisions. CityMatrix was an effort towards evidence-based, democratic decisionmaking. Its contributions lie in the application of Machine Learning as a versatile, quick, accurate, and low-cost approach to enable real-time feedback of complex urban simulations and the implementation of the optimization searching algorithms to provide open-ended decision-making suggestions.
by Yan Zhang.
S.M.

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Mao, Yanwei. "Decision Support System : A study of strategic decision makings in banks." Thesis, Jönköping University, JIBS, Business Informatics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-12585.

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The main purpose of this research is to use Hermeneutic research approach to find out how Decision Support System (DSS) is used in banks and financial services. The research started from one stance, from which the further process could be extended to reach more complete picture of Decision Support System’s usage in strategic decision makings in banks. The research is also trying to find out the drawbacks and benefits of the DSS which have been used nowadays in banks. Furthermore, the future improvements of using DSS to make better decisions related with moral and different environments are also being discussed in the research findings.

During the primary data collection, resources from different channels have been used to support the research. The primary data sources include lectures and discussion in three banks’ visiting opportunities in Stockholm, Sweden, one interview with IT Vice president from Bank of America Merrill Lynch, New York, two interviews with a professor and a director respectively from Lund University and Financial Services Innovation Centre in University College Cork, Ireland.

Experiences from both academic and practical have been shared to strength the research’s validity and trustworthiness. Hermeneutic research approach addresses through the whole research process which needs to be open-minded and flexible.

Unawareness of DSS for people who are working in banks is one of the issues today. Different embedded models regarding various functions are not so clear to bank staff; thus there is a gap between human decisions and system decisions. There is a variation of requirements between central banks, retail banks, commercial banks, investment banks. Hence there should be a differentiation when implementing a system. Banking systems are widespread systems which are influenced by environment factors, political, economic, socio-cultural and technological variables.

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Xu, Xian Zhong. "Information systems for strategic intelligence support." Thesis, Open University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244368.

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West, Graeme Michael. "Computational intelligence methods for power system protection design and decision support." Thesis, University of Strathclyde, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400311.

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Книги з теми "Intelligence decision support system"

Computational intelligence for decision support. Boca Raton, FL: CRC Press, 2000.

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Decision support systems for business intelligence. 2nd ed. Hoboken, N.J: Wiley, 2010.

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Turban, Efraim. Decision support and business intelligence systems. 9th ed. Upper Saddle River, N.J: Pearson/Prentice Hall, 2011.

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Sauter, Vicki L. Decision Support Systems for Business Intelligence. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470634431.

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Brahnam, Sheryl. Advanced Computational Intelligence Paradigms in Healthcare 5: Intelligent Decision Support Systems. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Sol, Henk G., Cees A. Th Takkenberg, and Pieter F. De Vries Robbé, eds. Expert Systems and Artificial Intelligence in Decision Support Systems. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3805-2.

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Khan, Zeashan H., A. B. M. Shawkat Ali, and Zahid Riaz, eds. Computational Intelligence for Decision Support in Cyber-Physical Systems. Singapore: Springer Singapore, 2014. http://dx.doi.org/10.1007/978-981-4585-36-1.

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Dyczkowski, Krzysztof. Intelligent Medical Decision Support System Based on Imperfect Information. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67005-8.

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Intelligent sytems modeling and decision support in bioengineering. Boston, MA: Artech House, 2006.

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Business intelligence roadmap: The complete project lifecycle for decision-support applications. Boston, MA, USA: Addison-Wesley, 2003.

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Частини книг з теми "Intelligence decision support system"

Zaveri, Jigish S., and Ali F. Emdad. "Intelligent scheduling systems: an artificial-intelligence-based approach." In Manufacturing Decision Support Systems, 204–16. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4613-1189-8_10.

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Wittig, Hartmut. "Decision Support System." In Intelligent Media Agents, 103–10. Wiesbaden: Vieweg+Teubner Verlag, 1999. http://dx.doi.org/10.1007/978-3-322-86851-0_9.

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Kale, Vivek. "Decision Support Systems." In Enterprise Performance Intelligence and Decision Patterns, 35–56. New York : CRC Press, [2017]: Auerbach Publications, 2017. http://dx.doi.org/10.4324/9781351228428-4.

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Malheiro, Benedita, and Eugénio Oliveira. "Intelligent Distributed Environmental Decision Support System." In Advances in Artificial Intelligence, 171–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61859-7_18.

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Zeilinger, G., J. Mey, G. Gell, and G. Vrisk. "DECISion-support system for radiological diagnostic." In Artificial Intelligence in Medicine, 437–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60025-6_176.

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Sauter, Vicki L. "Competitive Intelligence Systems." In Handbook on Decision Support Systems 2, 195–210. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-48716-6_10.

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Phillips-Wren, Gloria. "Intelligent Decision Support Systems." In Multicriteria Decision Aid and Artificial Intelligence, 25–44. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118522516.ch2.

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Negash, Solomon, and Paul Gray. "Business Intelligence." In Handbook on Decision Support Systems 2, 175–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-48716-6_9.

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Grzymala-Busse, Jerzy W. "LERS-A System for Learning from Examples Based on Rough Sets." In Intelligent Decision Support, 3–18. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-015-7975-9_1.

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Riaño, David, and Susana Prado. "Improving HISYS1 with a Decision Support System." In Artificial Intelligence in Medicine, 140–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48229-6_20.

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Тези доповідей конференцій з теми "Intelligence decision support system"

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}. California: 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 application functions to facilitate explainable decision support.

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Eachus, Peter, and Ben Short. "Decision Support System for Intelligence Analysts." In 2011 European Intelligence and Security Informatics Conference (EISIC). IEEE, 2011. http://dx.doi.org/10.1109/eisic.2011.20.

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Ye, Xin, Yanzhang Wang, Hui Li, and Zailin Dai. "An Emergency Decision Support System Based on the General Decision Process." In 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2008. http://dx.doi.org/10.1109/wiiat.2008.173.

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Martínez, María Vanina. "Knowledge Engineering for Intelligent Decision Support." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/736.

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Knowledge can be seen as the collection of skills and information an individual (or group) has acquired through experience, while intelligence as the ability to apply such knowledge. In many areas of Artificial Intelligence, we have been focusing for the last 40 years on the formalization and development of automated ways of finding and collecting data, as well as on the construction of models to represent that data adequately in a way that an automated system can make sense of it. However, in order to achieve real artificial intelligence we need to go beyond data and knowledge representation, and deeper into how such a system could, and would, use available knowledge in order to empower and enhance the capabilities of humans in making decisions in real-world applications. From my point of view, an AI should be able to combine automatically acquired data and knowledge together with specific domain expertise from the users that the tool is expected to help.

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Zhang, Yongjin, Yiming Su, Wei Zhou, and Jiancang Xie. "Metasynthetic Decision Support System for Water Resource Management." In 2006 IEEE/WIC/ACM International Conference on Web Intelligence International Intelligence Agent Technology Workshops. IEEE, 2006. http://dx.doi.org/10.1109/wi-iatw.2006.89.

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Zhou, Harry. "Modeling Stock Analysts' Decision Making: An Intelligent Decision Support System." In 2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE, 2013. http://dx.doi.org/10.1109/snpd.2013.71.

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Lewis, Rory, and Michael Bihn. "Cerebral Vasospasm Decision Support System for Neurosurgeons." In 2017 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2017. http://dx.doi.org/10.1109/csci.2017.134.

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Wang, Chunyi, and Yutian Liu. "Group Intelligent Decision Support System for Power System Skeleton Restoration." In 2008 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2008. http://dx.doi.org/10.1109/ictai.2008.22.

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Jinbing, Ha, Wei Youna, and Jin Ying. "Logistics Decision-making Support System Based on Ontology." In 2008 International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2008. http://dx.doi.org/10.1109/iscid.2008.128.

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Yang, Yunsong. "Water Dispatching Intelligent Decision Support System." In 2010 International Conference on E-Product E-Service and E-Entertainment (ICEEE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iceee.2010.5661037.

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Звіти організацій з теми "Intelligence decision support system"

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

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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, September 2020. http://dx.doi.org/10.46337/200930.

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Perdigão, Rui A. P. New Horizons of Predictability in Complex Dynamical Systems: From Fundamental Physics to Climate and Society. Meteoceanics, October 2021. http://dx.doi.org/10.46337/211021.

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Discerning the dynamics of complex systems in a mathematically rigorous and physically consistent manner is as fascinating as intimidating of a challenge, stirring deeply and intrinsically with the most fundamental Physics, while at the same time percolating through the deepest meanders of quotidian life. The socio-natural coevolution in climate dynamics is an example of that, exhibiting a striking articulation between governing principles and free will, in a stochastic-dynamic resonance that goes way beyond a reductionist dichotomy between cosmos and chaos. Subjacent to the conceptual and operational interdisciplinarity of that challenge, lies the simple formal elegance of a lingua franca for communication with Nature. This emerges from the innermost mathematical core of the Physics of Coevolutionary Complex Systems, articulating the wealth of insights and flavours from frontier natural, social and technical sciences in a coherent, integrated manner. Communicating thus with Nature, we equip ourselves with formal tools to better appreciate and discern complexity, by deciphering a synergistic codex underlying its emergence and dynamics. Thereby opening new pathways to see the “invisible” and predict the “unpredictable” – including relative to emergent non-recurrent phenomena such as irreversible transformations and extreme geophysical events in a changing climate. Frontier advances will be shared pertaining a dynamic that translates not only the formal, aesthetical and functional beauty of the Physics of Coevolutionary Complex Systems, but also enables and capacitates the analysis, modelling and decision support in crucial matters for the environment and society. By taking our emerging Physics in an optic of operational empowerment, some of our pioneering advances will be addressed such as the intelligence system Earth System Dynamic Intelligence and the Meteoceanics QITES Constellation, at the interface between frontier non-linear dynamics and emerging quantum technologies, to take the pulse of our planet, including in the detection and early warning of extreme geophysical events from Space.

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

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

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

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

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

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

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

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