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

A smart healthcare application can be judged as sustainable if it was already widely used before and will also be prevalent in the future. In contrast, if a smart healthcare application developed during the COVID-19 pandemic is not used after it, then it is not sustainable. Assessing the sustainability of smart healthcare applications is a critical task for their users and suppliers. However, it is also a challenging task due to the availability of data, users' subjective beliefs, and different perspectives. In response to this problem, this study proposes a multi-perspective fuzzy comprehensive evaluation approach to evaluate the sustainability of a smart healthcare application from qualitative, multi-criteria decision-making and time-series perspectives. The proposed methodology has been used to evaluate the sustainability of eight smart healthcare applications. The experimental results showed that the sustainability of a smart healthcare application evaluated from different perspectives may be different. Nevertheless, another technique can be used to confirm the evaluation result generated using one technique. In other words, these views compensate for each other.

Improving people's health by burning low-pollution coal to improve air quality for thermal power generation.

Eliminating the NOx emission after coal combustion is a critical task for thermal power plants to reduce threats to the human body, such as respiratory diseases, heart disease, lung disease and even lung cancer. To this end, various treatments have been taken to optimize, monitor and control the combustion process. However, optimizing the coal composition prior to combustion can further reduce possible NOx emissions. This topic was rarely discussed in the past. To fill this gap, this study proposes a fuzzy big data analytics approach. The proposed methodology combines recursive feature elimination, fuzzy c-means, XG Boost, support vector regression, random forests, decision trees and deep neural networks to predict post-combustion NOx emission based on coal composition and specification. Subsequently, additional treatments can be implemented to optimize boiler configuration and combustion conditions with pollution prevention equipment. In other words, the method proposed in this study is a kind of pretreatment. The proposed methodology has been applied to the real case of a thermal power plant in Taiwan. Experimental results showed that the prediction accuracy using the proposed methodology was significantly better than several existing methods. The forecasting error, measured in terms of root mean square error and mean absolute percentage error, was only 14.55 ppm and 8.9%, respectively.

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.

During the COVID-19 pandemic, some smart technology applications were more effective than had been expected, whereas some others did not achieve satisfactory performance. Consequently, whether smart technology applications in healthcare are sustainable is a question that warrants investigation. To address this question, a hybridising subjective and objective fuzzy group decision-making approach with explainable artificial intelligence was proposed in this study and then used to evaluate the sustainability of smart technology applications in healthcare. The contribution of this research is its subjective evaluation of the sustainability of smart technology applications followed by correction of the evaluation outcome on the basis of the applications' objective performance during the COVID-19 pandemic. To this end, a fuzzy nonlinear programming model was formulated and optimised. In addition, the impact of several major global events that occurred during the pandemic on the sustainability of smart technology applications was considered. The proposed methodology was applied to evaluate the sustainability levels of eight smart technology applications in healthcare. According to the experimental results, three applications-namely healthcare apps, smartwatches, and remote temperature scanners-are expected to be highly sustainable in healthcare, whereas one application, namely smart clothing, is not.

Type-II fuzzy approach with explainable artificial intelligence for nature-based leisure travel destination selection amid the COVID-19 pandemic.

During the coronavirus disease 2019 (COVID-19) pandemic, it is difficult for travelers to choose suitable nature-based leisure travel destinations because many factors are related to health risks and are highly uncertain. This research proposes a type-II fuzzy approach with explainable artificial intelligence to overcome this difficulty. First, an innovative type-II alpha-cut operations fuzzy collaborative intelligence method was used to derive the fuzzy priorities of factors critical for nature-based leisure travel destination selection. Subsequently, a type-II fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje method, which is also novel, was employed to evaluate and compare the overall performance of nature-based leisure travel destinations. Furthermore, several measures were taken to enhance the explainability of the selection process and result. The effectiveness of the proposed type-II fuzzy approach was evaluated in a regional experiment conducted in Taichung City, Taiwan, during the COVID-19 pandemic.

A ubiquitous healthcare system of 3D printing facilities for making dentures: Application of type-II fuzzy logic.

A ubiquitous healthcare (UH) system of multiple 3D printing facilities is established in this study for making dentures. The UH system receives orders from dental clinics, and then distributes the dentures to be printed among 3D printing facilities to save time. Compared with existing systems for similar purposes, the UH system has two novel features. The first is the consideration of the possibility of reprinting in formulating the plan to avoid replanning. The other is the cooperation with home delivery services that have gradually become popular during the COVID-19 pandemic to save transportation time. The new features are subject to considerable uncertainties. To account for the uncertainties, both printing time and transportation time are modelled using interval type-II trapezoidal fuzzy numbers. Subsequently, an interval type-II fuzzy mixed integer-linear programming (FMILP) model is formulated and optimized to plan the operations of the UH system. A case study has been conducted to illustrate the applicability of the proposed methodology. According to experimental results, the proposed methodology was able to shorten the order fulfillment time by up to 9%.

An FGM decomposition-based fuzzy MCDM method for selecting smart technology applications to support mobile health care during and after the COVID-19 pandemic.

In a fuzzy multicriteria decision-making (MCDM) problem, a decision maker may have differing viewpoints on the relative priorities of criteria. However, traditional methods merge these viewpoints into a single one, which leads to an unrepresentative decision-making result. Several recent methods identify the multiple viewpoints of a decision maker by decomposing the decision maker's fuzzy judgment matrix into several symmetric fuzzy subjudgment matrices, which is an inflexible strategy. To enhance flexibility, this study proposed a fuzzy geometric mean (FGM) decomposition-based fuzzy MCDM method in which FGM is applied to decompose a fuzzy judgment matrix into several fuzzy subjudgment matrices that can be asymmetric. These fuzzy subjudgment matrices are diverse and more consistent than the original fuzzy judgment matrix. The proposed methodology was applied to select the best choice from a group of smart technology applications for supporting mobile health care during and after the COVID-19 pandemic. According to the experimental results, the proposed methodology provided a novel approach to decomposing fuzzy judgment matrices and produced more diverse fuzzy subjudgment matrices.

Analyzing the impact of COVID-19 vaccination requirements on travelers' selection of hotels using a fuzzy multi-criteria decision-making approach.

In the later stages of the COVID-19 pandemic, hotels are taking various measures to balance pandemic prevention and business operations. Some hotels require travelers to be fully vaccinated prior to check-in, while others do not. In the latter type of hotels, fully vaccinated travelers may encounter others who are not vaccinated. All of these have created constraints for travelers to choose suitable hotel accommodation during this time. To address this issue, a fuzzy multi-criteria decision-making approach is proposed in this study to help traveler choose suitable hotel accommodation. In the proposed methodology, firstly, hotels are divided into two types considering their requirements for COVID-19 vaccination. Travelers are then asked to list the key factors to consider when choosing between these two types of hotels. To derive the priorities of these key factors, the proportionally calibrated fuzzy geometric mean (pcFGM) method is proposed. Subsequently, the fuzzy VIsekriterijumskoKOmpromisnoRangiranje (fuzzy VIKOR) method is applied to evaluate and compare the overall performances of different types of hotels for recommendations to travelers. The applicability of the proposed methodology is illustrated by a real case study. According to the experimental results, most hotels did not request travelers to be full vaccinated. Nevertheless, the hotels recommended to travelers covered both hotel types.

Assessing sustainable effectiveness of the adjustment mechanism of a ubiquitous clinic recommendation system.

Advances in computer and communication technologies have engendered opportunities for developing an improved ubiquitous health care environment. One of the crucial applications is a ubiquitous clinic recommendation system, which entails recommending a suitable clinic to a mobile patient based on his/her location, hospital department, and preferences. However, patients may not be willing or able to express their preferences. To overcome this problem, some ubiquitous clinic recommendation systems mine the historical data of patients to learn their preferences, and they apply an algorithm to adjust the recommendation algorithm after receiving more patient data. Such an adjustment mechanism may operate for several periods; however, this raises a question regarding the sustainability (i.e., long-term effectiveness) of such an adjustment mechanism. To address this question, this study modeled the improvement in the successful recommendation rate of a ubiquitous clinic recommendation system that adopts an adjustment mechanism as a learning process. Both the asymptotic value and learning speed of the learning process provide valuable information regarding the long-term effectiveness of the adjustment mechanism. The proposed methodology was applied in a regional study to a ubiquitous clinic recommendation system that adjusts the recommendation mechanism by solving an integer nonlinear programming problem on a rolling basis. The experimental results revealed that the proposed method exhibited a considerably higher level of accuracy in forecasting the successful recommendation rate compared with several existing methods. Although the adjustment mechanism exhibits long-term effectiveness, the learning speed requires improvement.

Mining the preferences of patients for ubiquitous clinic recommendation.

A challenge facing all ubiquitous clinic recommendation systems is that patients often have difficulty articulating their requirements. To overcome this problem, a ubiquitous clinic recommendation mechanism was designed in this study by mining the clinic preferences of patients. Their preferences were defined using the weights in the ubiquitous clinic recommendation mechanism. An integer nonlinear programming problem was solved to tune the values of the weights on a rolling basis. In addition, since it may take a long time to adjust the values of weights to their asymptotic values, the back propagation network (BPN)-response surface method (RSM) method is applied to estimate the asymptotic values of weights. The proposed methodology was tested in a regional study. Experimental results indicated that the ubiquitous clinic recommendation system outperformed several existing methods in improving the successful recommendation rate.