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Journal articles on the topic 'Intelligence decision support system'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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1

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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2

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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3

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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4

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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9

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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10

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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11

Krtalić, Andrija, and Milan Bajić. "Development of the TIRAMISU Advanced Intelligence Decision Support System." European Journal of Remote Sensing 52, no. 1 (December 4, 2018): 40–55. http://dx.doi.org/10.1080/22797254.2018.1550351.

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Ramaswamy, Vanaja, and Saswati Mukherjee. "An effective clinical decision support system using swarm intelligence." Journal of Supercomputing 76, no. 9 (June 28, 2019): 6599–618. http://dx.doi.org/10.1007/s11227-019-02888-5.

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13

Berbeka, Krzysztof, Sandjai Bhulai, and Ewa Magiera. "DECISION SUPPORT SYSTEM FOR WATER ADAPTING PRICING POLICY." Information System in Management 7, no. 2 (June 30, 2018): 97–107. http://dx.doi.org/10.22630/isim.2018.7.2.9.

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In the paper, the conception of Enterprise Information Portal (EIP) as an enduser interface of Simulation and Modeling System for Business (SMS-B) is presented. The system is a proposition of Business Intelligence education platform. EIP portals are also a base for Enterprise Integration Platform (EIP II) introduction in information and communication system in an institution.

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Kulik, А. "Artificial Intelligence-Based Aircraft Accident Threat Parrying Method." Proceedings of Telecommunication Universities 7, no. 4 (December 29, 2021): 110–17. http://dx.doi.org/10.31854/1813-324x2021-7-4-110-117.

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An anti-aircraft accident method is proposed, implemented in the decision support module, which is the main element of the flight safety control system and is a dynamic expert system. On the basis of the proposed method, recommendations are formed to the threat countering crew accidents using the information about its psychophysical state, the technical state an aircraft, external influencing factors, as well as a forecast of changes in flight conditions. The advantage of the proposed method is the ability to identify the immediate threat of an accident, as well as the development of management decisions to reduce the impact of the cause of the accident on flight safety. The peculiarity of the method of parrying the threat of an aircraft accident is the classification of management decisions depending on the flight conditions of the aircraft, which will reduce the computational costs for generating a threat parrying signal. Numerical modeling of the work using the assessment of a set of decision support rules made it possible to confirm its performance. The results can be used in systems development for safety an aircraft’s flight, the mathematical support of decision support systems.

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15

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

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

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16

Rosenthal-Sabroux, Camille, and Pascale Zaraté. "Artificial intelligence tools for decision support systems." European Journal of Operational Research 103, no. 2 (December 1997): 275–76. http://dx.doi.org/10.1016/s0377-2217(97)00119-7.

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17

Kahn, C. E. "Artificial intelligence in radiology: decision support systems." RadioGraphics 14, no. 4 (July 1994): 849–61. http://dx.doi.org/10.1148/radiographics.14.4.7938772.

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18

Aiken, Milam, Delvin Hawley, and John Seydel. "Competitive intelligence through group decision support systems." Competitive Intelligence Review 6, no. 2 (1995): 62–66. http://dx.doi.org/10.1002/cir.3880060212.

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19

Idrees, Amira M., Mohamed H. Ibrahim, and Ahmed I. El Seddawy. "Applying spatial intelligence for decision support systems." Future Computing and Informatics Journal 3, no. 2 (December 2018): 384–90. http://dx.doi.org/10.1016/j.fcij.2018.11.001.

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20

Galicic, Vlado, and Ljubica Pilepic. "The role of logistics information system in the business-decision process." Tourism and hospitality management 13, no. 3 (2007): 571–82. http://dx.doi.org/10.20867/thm.13.3.4.

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The development of logistics information systems that support decision-making, together with the use of business intelligence, provides assistance and support to logistics managers in the decision process, thereby impacting on the quality of business and productivity. Being better informed and having greater intelligence for decision-making can help to create new value and gain competitive advantage. Logistics business systems in a tourism destination appreciate the importance of information and communication technology in the decision process and seek to develop efficient logistics information systems that will make it possible to take better and more appropriate decisions directly aimed at improving business efficiency and productivity.

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21

Sandha, A., K. Agha, and R. Islam. "Artificial Intelligence…as a Decision Support System for Petroleum Engineers." Petroleum Science and Technology 23, no. 5-6 (May 2005): 555–71. http://dx.doi.org/10.1081/lft-200032841.

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22

Ponis, Stavros T., and Ioannis T. Christou. "Competitive intelligence for SMEs: a web-based decision support system." International Journal of Business Information Systems 12, no. 3 (2013): 243. http://dx.doi.org/10.1504/ijbis.2013.052449.

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23

MAKOIEDOVA, Valentyna. "INFORMATION TECHNOLOGIES IN DECISION SUPPORT SYSTEMS." Herald of Kyiv National University of Trade and Economics 133, no. 5 (October 20, 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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24

Hanratty, Timothy P., E. Allison Newcomb, Robert J. Hammell II, John T. Richardson, and Mark R. Mittrick. "A Fuzzy-Based Approach to Support Decision Making in Complex Military Environments." International Journal of Intelligent Information Technologies 12, no. 1 (January 2016): 1–30. http://dx.doi.org/10.4018/ijiit.2016010101.

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Data for military intelligence operations are increasing at astronomical rates. As a result, significant cognitive and temporal resources are required to determine which information is relevant to a particular situation. Soft computing techniques, such as fuzzy logic, have recently been applied toward decision support systems to support military intelligence analysts in selecting relevant and reliable data within the military decision making process. This article examines the development of one such system and its evaluation using a constructive simulation and human performance model to provided critical understanding of how this conceptual information system might interact with personnel, organizational, and system architectures. In addition, similarities between military intelligence analysts and cyber intelligence analysts are detailed along with a plan for transitioning the current fuzzy-based system to the cyber security domain.

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25

Al Eid, Ali Alhousain, and Uğur Yavuz. "The Effect of Using Decision Support Systems Applications and Business Intelligence Systems in Making Strategic Decisions: A Field Study in the City of Gaziantep." Global Journal of Economics and Business 12, no. 2 (April 2022): 256–73. http://dx.doi.org/10.31559/gjeb2022.12.2.8.

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This study aims at studying the importance of business intelligence systems and decision support systems for Syrian civil society organizations in addition to studying the impact of the dimensions of decision support systems and business intelligence in making strategic decisions. The data was collected through a survey conducted on the participants. 100 correct answers were used to analyze the data. SPSS and SmartPLS 3 software were used to analyze the study data. The results showed support for the seven hypotheses. That decision support systems and business intelligence are well available in Syrian civil society organizations in the city of Gaziantep and at the same time, it was found that there is a strong positive relation between business intelligence and decision support systems with the making strategic decisions.

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Salman, Ban, Nada M. Alhakkak, and Mustafa Musa Jaber. "Football Player Decision Support System Baghdad-City as a Case Study." International Journal of Engineering & Technology 7, no. 3.20 (September 1, 2018): 406. http://dx.doi.org/10.14419/ijet.v7i3.20.20582.

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Decision support system (DSS) is an area of information systems (IS) discipline which focuses on supporting decision-making. DSS includes personal decision support systems, executive information systems, online analytical processing systems, data warehousing, business intelligence, and group support systems. This paper introduced the implementation of a DSS related to football players with a case study.

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27

Tacuban, Tracy N. "Career Decision Support System For Graduating High School Students." Proceedings Journal of Interdisciplinary Research 2 (October 10, 2015): 1–7. http://dx.doi.org/10.21016/irrc.2015.ma15wf13o.

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This study aimed to create and evaluate an online decision support system (DSS) which allows students to take a multiple intelligence test, determine their most dominant intelligence, suggest the most suitable courses to take for college and provide an interpretation for the result of the evaluation. The system captures the knowledge of an expert in guidance and counseling and embodied it to its knowledge base using fuzzy logic, graph search, and depth-first search. The system was tested at the University of Iloilo–Basic Education Department, Iloilo City, and Tabugon National High School, Tabugon, Dingle, Iloilo by a conveniently chosen graduating high school students. The result of the system was evaluated by 39 guidance counselors from the different secondary schools from the province and city of Iloilo. The evaluation of the system’s functionality was assessed by 15 respondents from the Information and Communications Technology (ICT) sector using the International Organization for Standardization (ISO) 9126. The reliability of the system was evaluated by comparing the system output with the two other assessment tools from Western Visayas College of Science and Technology (Weber standard) and the University of Iloilo (NDDCTE standard). Using Cramer’s V, the result of the evaluation of the NDDCTE standard as compared to the Decision Support System of the data taken from the University of Iloilo and the data taken from Tabugon National High School has a significant relationship. It implies that there is no significant difference in the results of the two evaluations. The result of the evaluation using Cramer’s V of the Weber standard as compared to the Decision Support System of the data taken from the University of Iloilo and the data taken from Tabugon National High School has a significant relationship. It implies that the two results are highly related to each other. Using arithmetic mean (M) and standard deviation (SD), the result of the evaluation of the system’s output based on the perception of 39 guidance counselors resulted in a “Very Satisfactory” result. This implies that the system generates the dominant intelligence of the student, provide accurate course and study tips based on his/ her dominant intelligence. The evaluation of the respondents to the system’s software quality characteristics based on ISO 9126 resulted in a value described as “Very Effective”. It confirmed that the overall software characteristics of the system passed the ISO 9126 standards.

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Grabot, Bernard. "A decision support system for variable routings management in manufacturing systems." Fuzzy Sets and Systems 58, no. 1 (August 1993): 87–104. http://dx.doi.org/10.1016/0165-0114(93)90324-b.

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29

Shaw, Michael J., and Mark S. Fox. "Distributed artificial intelligence for group decision support." Decision Support Systems 9, no. 4 (June 1993): 349–67. http://dx.doi.org/10.1016/0167-9236(93)90046-6.

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30

Bellucci, Emilia, and John Zeleznikow. "Developing Negotiation Decision Support Systems that Support Mediators: A Case Study of the Family_Winner System." Artificial Intelligence and Law 13, no. 2 (June 2005): 233–71. http://dx.doi.org/10.1007/s10506-006-9013-1.

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31

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

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32

Gammack, J. G., T. C. Fogarty, S. A. Battle, N. S. Ireson, and J. Cui. "Human-centred decision support: The IDIOMS system." AI & Society 6, no. 4 (October 1992): 345–66. http://dx.doi.org/10.1007/bf02472787.

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33

Steiger, David M. "Decision Support as Knowledge Creation." International Journal of Business Intelligence Research 1, no. 1 (January 2010): 29–47. http://dx.doi.org/10.4018/jbir.2010071703.

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The primary purpose of decision support systems (DSS) is to improve the quality of decisions. Since decisions are based on an individual’s mental model, improving decision quality is a function of discovering the decision maker’s mental model, and updating and/or enhancing it with new knowledge; that is, the purpose of decision support is knowledge creation. This article suggests that BI techniques can be applied to knowledge creation as an enabling technology. Specifically, the authors propose a business intelligence design theory for DSS as knowledge creation, a prescriptive theory based on Nonaka’s knowledge spiral that indicates how BI can be focused internally on the decision maker to discover and enhance his/her mental model and improve the quality of decisions.

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Knapič, Samanta, Avleen Malhi, Rohit Saluja, and Kary Främling. "Explainable Artificial Intelligence for Human Decision Support System in the Medical Domain." Machine Learning and Knowledge Extraction 3, no. 3 (September 19, 2021): 740–70. http://dx.doi.org/10.3390/make3030037.

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In this paper, we present the potential of Explainable Artificial Intelligence methods for decision support in medical image analysis scenarios. Using three types of explainable methods applied to the same medical image data set, we aimed to improve the comprehensibility of the decisions provided by the Convolutional Neural Network (CNN). In vivo gastral images obtained by a video capsule endoscopy (VCE) were the subject of visual explanations, with the goal of increasing health professionals’ trust in black-box predictions. We implemented two post hoc interpretable machine learning methods, called Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), and an alternative explanation approach, the Contextual Importance and Utility (CIU) method. The produced explanations were assessed by human evaluation. We conducted three user studies based on explanations provided by LIME, SHAP and CIU. Users from different non-medical backgrounds carried out a series of tests in a web-based survey setting and stated their experience and understanding of the given explanations. Three user groups (n = 20, 20, 20) with three distinct forms of explanations were quantitatively analyzed. We found that, as hypothesized, the CIU-explainable method performed better than both LIME and SHAP methods in terms of improving support for human decision-making and being more transparent and thus understandable to users. Additionally, CIU outperformed LIME and SHAP by generating explanations more rapidly. Our findings suggest that there are notable differences in human decision-making between various explanation support settings. In line with that, we present three potential explainable methods that, with future improvements in implementation, can be generalized to different medical data sets and can provide effective decision support to medical experts.

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Chi, Chih-Lin, W. Nick Street, and David A. Katz. "A decision support system for cost-effective diagnosis." Artificial Intelligence in Medicine 50, no. 3 (November 2010): 149–61. http://dx.doi.org/10.1016/j.artmed.2010.08.001.

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Kulshrestha, S., and R. Khosa. "CLIPS based decision support system for Water Distribution Networks." Drinking Water Engineering and Science Discussions 4, no. 1 (March 7, 2011): 1–38. http://dx.doi.org/10.5194/dwesd-4-1-2011.

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Abstract. The Water Distribution Networks (WDN) are managed by experts, who, over the years of their association and responsibility, acquire an empirical knowledge of the system and, characteristically, this knowledge remains largely confined to their respective personal domains. In the event of any new information and/or emergence of a new problem, these experts apply simple heuristics to design corrective measures and cognitively seek to predict network performance. The human interference leads to inefficient utilization of resources and unfair distribution. Researchers over the past, have tried to address to the problem and they have applied Artificial Intelligence (AI) tool to automate the decision process and encode the heuristic rules. The application of AI tool in the field of WDN management is meager. This paper describes a component of an ongoing research initiative to investigate the potential application of artificial intelligence package CLIPS (short for C Language Integrated Production System, developed at NASA/Johnson Space Center) in the development of an expert decision support system for management of a water distribution network. The system aims to meet several concerns of modern water utility managers as it attempts to formalize operational and management experiences, and provides a frame work for assisting water utility managers even in the absence of expert personnel.

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Amran, M. D. Mohd, A. W. Mohamad Ikbar, S. Khairanum, A. B. Fairul Anwar, and B. Rahmat Roslan. "Development of Intelligent Decision Support System for Selection of Quality Tools and Techniques." International Journal of Machine Learning and Computing 9, no. 6 (December 2019): 893–98. http://dx.doi.org/10.18178/ijmlc.2019.9.6.889.

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King, Dave. "Intelligent decision support: Strategies for integrating decision support, database management, and expert system technologies." Expert Systems with Applications 1, no. 1 (January 1990): 23–38. http://dx.doi.org/10.1016/0957-4174(90)90066-4.

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39

Khedr, Ayman. "Business Intelligence framework to support Chronic Liver Disease Treatment." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (April 22, 2013): 307–12. http://dx.doi.org/10.24297/ijct.v4i2a2.3176.

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Business Intelligence (BI) framework designs the architecture of business intelligence information system which uses expert systems and Artificial Intelligence technology to support clinical decision and draw the strategy against chronic liver disease in Egypt. It makes integrated diagnostic and medical advice bases on the collected patientâ€™s information, providing reference for the clinical medical officers. This paper aims to support decision function and in particular utilization of historical data laboratory and outcome data processed through artificial intelligence tools. The combination of historical data and predictive tools provides valuable information in the hands of physicians as they develop a course of treatment for a patient

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40

Lee, Shao-Lun. "A decision support system for luggage typesetting." Expert Systems with Applications 35, no. 4 (November 2008): 1620–27. http://dx.doi.org/10.1016/j.eswa.2007.08.096.

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Park, Seon Gyu, Sungin Lee, Myeng-Ki Kim, and Hong-Gee Kim. "Shared decision support system on dental restoration." Expert Systems with Applications 39, no. 14 (October 2012): 11775–81. http://dx.doi.org/10.1016/j.eswa.2012.04.074.

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42

Teles, Gernmanno, Joel J. P. C. Rodrigues, Kashif Saleem, Sergei Kozlov, and Ricardo A. L. Rabêlo. "Machine learning and decision support system on credit scoring." Neural Computing and Applications 32, no. 14 (October 24, 2019): 9809–26. http://dx.doi.org/10.1007/s00521-019-04537-7.

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Simanjuntak, Ester, and Bosker Sinaga. "Decision Support System for Determining the Best Hospital Nurses Grandmed Method Using Simple Additive Weighting (SAW)." Journal Of Computer Networks, Architecture and High Performance Computing 2, no. 1 (January 1, 2020): 45–52. http://dx.doi.org/10.47709/cnapc.v2i1.357.

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Decision support system is a combination of sources of individual intelligence with the ability of components to improve the quality of decisions. Also decision support systems are computer-based information systems for management decision-making that deal with semi-structural problems. The purpose of this study is to the make the application of nurse employees in Grandmed Hospital Based on the results of research that has been Discussed items, namely the Decision Support System for Determining the Best Nurses in Grandmed Hospital by Using the Simple Additive Wieghting (SAW) Method with the benefit of being Able to Facilitate the processing of the data and Facilitate nurse in Determination of the best nurse for a promotion to a nurse at Grandmed Hospital

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Mishra, Manisha, Pujitha Mannaru, David Sidoti, Adam Bienkowski, Lingyi Zhang, and Krishna Pattipati. "Context-Driven Proactive Decision Support for Hybrid Teams." AI Magazine 40, no. 3 (July 9, 2019): 41–57. http://dx.doi.org/10.1609/aimag.v40i3.4810.

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A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances autonomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

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Lee, Kyounga, and Seon Heui Lee. "Artificial Intelligence-Driven Oncology Clinical Decision Support System for Multidisciplinary Teams." Sensors 20, no. 17 (August 20, 2020): 4693. http://dx.doi.org/10.3390/s20174693.

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Watson for Oncology (WfO) is a clinical decision support system driven by artificial intelligence. In Korea, WfO is used by multidisciplinary teams (MDTs) caring for cancer patients. This study aimed to investigate the effect of WfO use on hospital satisfaction and perception among patients cared for by MDTs. This was a descriptive study that used a written survey to gather information from cancer patients at a hospital in Korea. The rate of positive change in patient perception after treatment was 86.8% in the MDT-WfO group and 71.2% in the MDT group. In terms of easily understandable explanations, the MDT-WfO (9.53 points) group reported higher satisfaction than the MDT group (9.24 points). Younger patients in the MDT-WfO group showed high levels of satisfaction and reliability of treatment. When WfO was used, the probability of positive change in patient perception of the hospital was 2.53 times higher than when WfO was not used. With a one-point increase in overall satisfaction, the probability of positive change in patient perception of the hospital increased 1.97 times. Therefore, if WfO is used appropriately in the medical field, it may enhance patient satisfaction and change patient perception positively.

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Kilic, Kemal, and Onur Bozkurt. "Computational Intelligence Based Decision Support Tool for Personalized Advertisement Assignment System." International Journal of Computational Intelligence Systems 6, no. 3 (May 2013): 396–410. http://dx.doi.org/10.1080/18756891.2013.780725.

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Masciopinto, C., V. Palmisano, F. Tangorra, and M. Vurro. "A Decision Support System for Artificial Recharge Plant." Water Science and Technology 24, no. 9 (November 1, 1991): 331–42. http://dx.doi.org/10.2166/wst.1991.0262.

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The need for artificial recharge plants is the result of the qualitative and quantitative worsening of groundwater resources due to increased pumping and wastewater discharge. This paper described a system that uses artificial intelligence techniques for designing an artificial recharge plant. The system can be used as a training tool for new engineers, as well as an aid in the choices for expert engineers. The system is an application of an expert system shell running on a common p.c. machine. The model is made up of two knowledge bases, respectively denoted as Quantity artificial recharge and Quality artificial recharge. The former is related to the quantitative aspects, such as geology, climate and land availability, the latter to qualitative aspects, such as water use and treatment plant. Two case studies have been implemented in order to confirm the validity of this kind of systemic approach.

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Hoang, Dinh Tuyen, Ngoc Thanh Nguyen, Botambu Collins, and Dosam Hwang. "Decision Support System for Solving Reviewer Assignment Problem." Cybernetics and Systems 52, no. 5 (January 20, 2021): 379–97. http://dx.doi.org/10.1080/01969722.2020.1871227.

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Tyler, Nichole S., and Peter G. Jacobs. "Artificial Intelligence in Decision Support Systems for Type 1 Diabetes." Sensors 20, no. 11 (June 5, 2020): 3214. http://dx.doi.org/10.3390/s20113214.

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Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.

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Denaï, Mouloud A., Mahdi Mahfouf, and Jonathan J. Ross. "A hybrid hierarchical decision support system for cardiac surgical intensive care patients. Part I: Physiological modelling and decision support system design." Artificial Intelligence in Medicine 45, no. 1 (January 2009): 35–52. http://dx.doi.org/10.1016/j.artmed.2008.11.009.

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