An intelligent system for assisting personalized COVID-19 vaccination location selection: Taiwan as an example

Abstract

In many regions of the world, with the gradual increase in the supply of COVID-19 vaccines, COVID-19 vaccination has changed from centralized government control to personalized selection. When choosing a location for COVID-19 vaccination, in addition to subjective preferences, objective information (such as the expected waiting time at a COVID-19 vaccination location and the crowdedness and reliability of the vaccination location) also need to be considered. However, it is not convenient for an individual to collect and compare such information. To address this issue, this research applies web content mining to extract the conditions of COVID-19 vaccination locations. Then, a novel asymmetric calibrated fuzzy inverse of column sum and fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje recommendation mechanism is proposed. Finally, an intelligent system is developed to assist a user in selecting a personalized COVID-19 vaccination location. In a regional experiment conducted in Taichung City, Taiwan, the developed intelligent system was applied to assist 20 users in choosing personalized COVID-19 vaccination locations. The successful recommendation rate was 95%.

Assessing the sustainability of smart healthcare applications using a multi-perspective fuzzy comprehensive evaluation approach

“…Many MCDM methods have been to this field, for example, FWA, 4 , 55 fuzzy analytic hierarchy process (FAHP), 4 , 6 , 17 , 56 , 57 FTOPSIS, 6 , 17 , 58 fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (fuzzy VIKOR), 56 , 59 fuzzy combinative distance-based assessment, 57 fuzzy inference systems, 60 , 61 fuzzy measuring attractiveness by a categorical based evaluation technique (fuzzy MACBETH), 62 etc. In the proposed methodology, FAHP and fuzzy VIKOR are used.…”

Evaluating the sustainability of smart technology applications in healthcare after the COVID-19 pandemic: A hybridising subjective and objective fuzzy group decision-making approach with explainable artificial intelligence

“… 47 As illustrated in Figure 3 , the proposed methodology is novel for the following reasons: The process of evaluating a methodology's sustainability is treated as a hybrid (supervised and unsupervised) optimisation problem, whereas in most existing methods, the problem in question is treated as an unsupervised fuzzy group decision-making process. 2 , 48 , 49 The sustainability of a smart technology application in healthcare is evaluated both subjectively and objectively. …”

“…The process of evaluating a methodology's sustainability is treated as a hybrid (supervised and unsupervised) optimisation problem, whereas in most existing methods, the problem in question is treated as an unsupervised fuzzy group decision-making process. 2 , 48 , 49 …”

“…However, presenting the fuzzy priority and the performance of a criterion simultaneously is difficult. Therefore, in the proposed methodology, two XAI techniques, namely the colour management technique and the common expression technique, 49 are applied, and a gradient bar chart is plotted to illustrate the fuzzy priorities of the criteria ( Figure 6 ), where darker colours represent higher membership levels. The fuzzy priority and (normalised) performance of a criterion are presented simultaneously using a grouped bar chart, as illustrated in Figure 7 .…”

“…The fuzzy judgment matrixes of all the experts are aggregated using the FGM as follows 54 : By contrast, an aggregation result obtained through other methods, such as the fuzzy weighted average method, may lead to an unreasonable value or membership level. 21 , 54 The traceable aggregation technique 49 can be used to explain the aggregation process and result, as illustrated in Figure 9 .…”