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Showing results excluding:
  • pmc.ncbi.nlm.nih.gov

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  1. ncbi.nlm.nih.gov

    National Center for Biotechnology Information

    https://www.ncbi.nlm.nih.gov › pmc › articles › PMC8060082

    Therefore, harmonizing TQM and KM with the organizational goals and performance leads to the organization's greater capabilities. The current paper presents a decision support framework to evaluate the performance of organizations based on the success factors (SFs) of TQM and KM using the BCC model of data envelopment analysis (DEA).
    Publication:J Healthc Eng. 2021; 2021: 6654600.Author:Azin Karami, Hadi Shirouyehzad, Milad AsadpourPublished:2021
  2. sciencedirect.com

    Apr 15, 2023To illustrate and validate the proposed framework, a case study of a KBDSS for a multiunit residential building kitchen design is demonstrated to explore a user-centered residential design with such decision support. This proposed framework provides naive design practitioners with specific knowledge about decision problems and assists them in ...
  3. sciencedirect.com

    This research delves into the general rules and knowledge reasoning processes based on knowledge graph construction, introducing a three-tiered "Data-Knowledge-Decision" theoretical framework for machining assistance [55], as depicted in Fig. 1. Under the guidance of the manufacturing knowledge graph, this framework facilitates auxiliary ...
  4. ncbi.nlm.nih.gov

    National Center for Biotechnology Information

    https://www.ncbi.nlm.nih.gov › pmc › articles › PMC8361008

    Knowledge graphs (KGs) are featured with representing structured knowledge and have recently been used for supporting decision-making, mostly for clinical decision-making support , , . Constructing disease or health knowledge graph from texts has been an active field where previous studies rely highly on the annotations of domain experts.
  5. pubmed.ncbi.nlm.nih.gov

    Background: Clinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence-enabled clinical decision support systems aid the decision-making process through an intelligent component.
    Author:Mengting Ji, Georgi Z Genchev, Hengye Huang, Ting Xu, Hui Lu, Hui Lu, Guangjun YuPublished:2021
  6. sciencedirect.com

    We first discuss a Macro-level Knowledge-based Decision Support Systems Framework. This high level framework guides our Meso-level Scenario-driven Decision Support Systems Generator Framework. ... life cycle based approach for scenario management that supports a range of activities from idea generation to final use of the scenarios. Key phases ...
  7. repository.up.ac.za

    user or knowledge worker or decision-maker. Like a human expert, the ES advises non-experts and explains the logic behinds its conclusion (Turban et al 2001). 2.2 Decision Support Systems (DSS) and Knowledge-based Decision Support Systems (KB-DSS) Decision support is a context-free expression. It means different things to di fferent people.
  8. link.springer.com

    This chapter reviews the Knowledge Based Decision Support System (KBDSS) as part of the decision making tools. ... Every decision making process produces a final choice. The output can be an action or an opinion of choice. ... Framework—The decision framework is developed based on the knowledge provided in the system. In the framework ...
  9. pubmed.ncbi.nlm.nih.gov

    This paper proposes a decision fusion framework, combining both knowledge-driven and data-driven decision engines for treatment recommendation. End users (e.g. using the clinician workstation or mobile apps) could have a comprehensive view of various engines' opinions, as well as the final decision after fusion.
  10. withing the framework of decision making of the disassembly process of EoL products. The aim of this paper is to introduce a decision support system based on knowledge based and digital twin in order to help stakeholders to choose the most sustainable disassembly scenario.

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