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A heterogeneous multi-attribute case retrieval method based on neutrosophic sets and TODIM for emergency situations.
Zhang, Kai; Zheng, Jing; Wang, Ying-Ming.
Afiliación
  • Zhang K; College of Information and Intelligent Transportation, Fujian Chuanzheng Communications College, Fuzhou, 350007 Fujian China.
  • Zheng J; College of Electronics and Information Science, Fujian Jiangxia University, Fuzhou, 350108 Fujian China.
  • Wang YM; Institute of Decision Science, Fuzhou University, Fuzhou, 350116 Fujian China.
Appl Intell (Dordr) ; 52(13): 15177-15192, 2022.
Article en En | MEDLINE | ID: mdl-35308410
Heterogeneous multi-attribute case retrieval is a crucial step in generating emergency alternatives during the course of emergency decision making (EDM) by referring to historical cases. This paper develops a heterogeneous multi-attribute case retrieval method for EDM that considers five attribute formats: crisp numbers, interval numbers, intuitionistic fuzzy numbers, single-valued neutrosophic numbers (SvNNs), and interval-valued neutrosophic numbers (IvNNs). First, we propose a similarity measurement of IvNNs and calculate the attribute similarities for the five attribute formats. The attribute weights are established using an optimal model. Next, the case similarities are calculated and the set of the similar historical cases is constructed. Furthermore, the evaluated information based on heterogeneous multi-attribute from similar historical cases is provided, and the calculation method for the evaluation of utility based on TODIM (an acronym for interactive and multi-criteria decision-making in Portugese) is proposed. The most suitable historical case is determined based on the case similarity and the evaluated utility. From this, the emergency alternative is generated. Finally, we demonstrate the efficacy of the proposed method with a case study and conduct comparisons against the performance of existing methods to assess the validity and superiority of the proposed method.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Appl Intell (Dordr) Año: 2022 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Appl Intell (Dordr) Año: 2022 Tipo del documento: Article