利用综合区间值q-rung正交模糊决策模型评估区块链技术赋能医院的因素

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-25 DOI:10.1016/j.engappai.2024.109641

Rashmi Pathak , Badal Soni , Naresh Babu Muppalaneni , Muhammet Deveci

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引用次数: 0

摘要

在当今时代，区块链技术（BT）已成为维护医疗管理系统运行的一项新技术。由于存在多个标准，因此对支持区块链技术（BT）的医院进行评估可被视为一个多标准决策（MCDM）问题。本研究的目的是开发一种混合 MCDM 方法，用于评估区间值 q-rung 正交模糊集（IVq-ROFSs）下多标准 BT 支持的医院选择问题的因素。为此，结合 IVq-ROF 交互聚合算子、基于标准偏差（SD）的模型和枢轴成对相对标准重要性评估（PIPRECIA）工具，提出了一个加权聚合和积评估（WASPAS）模型，称为 IVq-ROFSs 下的 IV-q-ROF-SD-PIPRECIA-WASPAS 模型。为此，我们开发了一些新的 IVq-ROF 交互聚合算子，它们具有理想的特性。接下来，提出了基于标准偏差的模型和基于 IVq-ROFSs 的 PIPRECIA 模型来获得标准的最终权重，并提出了基于等级的公式来确定具有 IVq-ROF 信息的决策专家权重。将所提出的 IV-q-ROF-SD-PIPRECIA-WASPAS 模型应用于 BT 医院评估案例研究，证实了该模型的适用性和实用性。敏感性分析和比较讨论揭示了所提出模型的一致性、稳健性和效率。在一系列 BT 能力医院中，UD 最高（0.4453）的 BT 能力医院-II 成为最佳选择。灵活性 "因素的权重（0.0898）最高，其次是可扩展性（0.0809）、交易速度和责任（权重（0.0779）相同）以及网络可用性（权重（0.0771））。最终结果表明，所开发的方法可以在考虑多个指标和输入不确定性的同时提供更准确的决策。

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Assessing the factors of blockchain technology-enabled hospitals using an integrated interval-valued q-rung orthopair fuzzy decision-making model

In the current era, blockchain technology (BT) has emerged as a novel technique to maintain the operations of healthcare management systems. Assessment of blockchain technology (BT)-enabled hospitals can be considered as a multi-criteria decision making (MCDM) problem because of the existence of several criteria. The aim of this study is to develop a hybrid MCDM method for evaluating the factors of multi-criteria BT-enabled hospital selection problem under interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). For this purpose, a weighted aggregated sum product assessment (WASPAS) model is presented with the combination of IVq-ROF interaction aggregation operators, the standard deviation (SD)-based model and pivot pairwise relative criteria importance assessment (PIPRECIA) tool called IV-q-ROF-SD-PIPRECIA-WASPAS model within the context of IVq-ROFSs. For this purpose, some new IVq-ROF interaction aggregation operators are developed with their desirable characteristics. Next, the standard deviation-based model and PIPRECIA model on IVq-ROFSs are proposed to obtain the final weight of criteria, whereas the rank-based formula is presented to determine the decision experts’ weights with IVq-ROF information. The presented IV-q-ROF-SD-PIPRECIA-WASPAS model is applied on a case study of BT-enabled hospitals assessment, which confirms its applicability and usefulness. Sensitivity analysis and comparative discussion have been performed to reveal the consistency, robustness and efficiency of the presented model. The BT-enabled hospital-II with highest UD (0.4453) has emerged as the best choice among a set of BT-enabled hospitals. The factor "flexibilty" with highest weight (0.0898) value followed that the scalability (0.0809), transaction speed and accountability with same weight (0.0779) value, and network availability with weight (0.0771) for BT-enabled hospitals assessment. The final results conclude that the developed methodology can provide more accurate decisions while considering multiple indicators and input uncertainties.

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来源期刊

Engineering Applications of Artificial Intelligence 工程技术-工程：电子与电气

CiteScore

9.60

自引率

10.00%

发文量

505

审稿时长

68 days

期刊介绍： Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.