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Many citizens nowadays cope with busy and dynamic lifestyles. Adopting or maintaining a healthy lifestyle to prevent chronic diseases or mental disorders is a core societal challenge. The current global pandemic has evidenced more than ever before the critical importance of engaging citizens with healthy and tailored activities that they like, as a key driver for safeguarding good health from a preventive vantage point, aligned with the pursuance of SDG 3: "good health and well-being". This is why Recommender Systems for personalized health and well-being have lately become a research trend, particularly for food and physical activity recommendation. This paper presents F-EvoRecSys: an extension of an evolutionary algorithm-driven approach for "healthy bundle" well-being recommendations that incorporates a fuzzy inference engine aimed at improving physical activity recommendations based on users’ exercising habits. An experimental study demonstrates how this can lead to more diversified recommendations. The paper also discusses challenges and future directions for personalized well-being recommender systems under different perspectives.

From the clinical viewpoint, the statistical approach is still the cornerstone for exploring many diseases. This study was conducted to explore the risk factors related to acute kidney injury (AKI) for elderly patients using the multiple criteria decision-making (MCDM) approach. Ten nephrologists from a teaching hospital in Taipei took part in forming the AKI risk assessment model. The key findings are: (1) Comorbidity and Laboratory Values would influence Comprehensive Geriatric Assessment, (2) Frailty is the highest influential AKI risk factor for elderly patients, and (3) Elderly patients could enhance their daily activities and nutrition to improve frailty and lower AKI risk. Furthermore, we illustrate how to apply MCDM methods to retrieve clinical experience from seasoned doctors, which may serve as a knowledge-based system to support clinical prognoses. In conclusion, this study has shed light on integrating multiple research approaches to assist medical decision-making in clinical practice.

Background: Previous systematic reviews and meta-analyses supported the relationship between frailty and risk of acute kidney injury (AKI) in elderly patients. However, few studies evaluated proactive management to wear down AKI risk in such frail populations.Purpose: To understand how AKI risk factors might influence each other and to identify the source factors for clinical decision aids.Methods: This study uses the decision-making trial and evaluation laboratory (DEMATEL) method to establish influential network-relationship diagrams (INRDs) to form the AKI risk assessment model for the elderly.Results: Based on the DEMATEL approach, the results of INRD identified the six key risk factors: comorbidity, malignancy, diabetes, creatinine, estimated glomerular filtration rate, and nutritional assessment. (The statistical significance confidence is 98.423%, which is higher than 95%; the gap error is 1.577%, which is lower than 5%). After considering COVID-19 as an additional risk factor in comorbidity, the INRD revealed a similar influential relationship among the essential aspects.Conclusion: While evaluating the geriatric population, physicians need to pay attention to patients' comorbidities and nutritional assessment; also, they should note patients' creatinine values and glomerular filtration rate. Physicians could establish a preliminary observation index and then design a series of preventive guidelines to reduce the incidence of AKI risk for the elderly.

Banks attempt to invest in emerging financial technology (FinTech), such as blockchain, to enhance competitiveness. There is a great deal of literature on the technical and legal aspects of blockchain. However, there is little specific guidance on how banks can apply a holistic model to evaluate the blockchain-based business. This study proposes a hybrid decision model with confidence-weighted fuzzy assessments to address this valuable research topic. Supported by a group of seasoned experts, five major blockchain-based business models are evaluated for a domestic bank in Taiwan. The key findings contribute to understanding the importance of the involved factors and identifying the ideal business strategy for the bank. The result suggests that the most crucial dimension is policies and regulations, not the technical capability of banks.

Open banking (OB) is an emerging business field in the financial sector, which relies on intensive collaboration between banks and non-banking service providers. However, how to evaluate OB business partners from multiple perspectives for banks is underexplored. Therefore, this study proposed a hybrid decision model with supports from seasoned domain experts. This study also adopts a domestic bank from Taiwan and four non-banking service providers to illustrate the hybrid approach with the confidence-weighted fuzzy assessment technique. The proposed model might be the first attempt to explore the OB adoption strategy by the novel approach. However, its limitations are the presumed independent relationship among the factors of this hybrid model. Additionally, the results hinge upon domain experts’ knowledge. In practice, the research findings identify the relative importance of banks’ crucial factors to select OB strategic partners, which provide managerial insights and valuable guidance for the banking sector.

Nearly all companies need to retrieve valuable information from business data to increase its efficiency or value, and the rising interests of research in this domain could be named as business analytics. Because most of the problems (obstacles) faced by business have to consider a group of complex and interrelated factors, conventional statistics models (e.g., regression) have constraints in resolving these interrelated and complex problems. Therefore, this study proposes a novel multiple attribute decision-making model to resolve—from ranking/selection to improvement planning—the problems of business analytics in finance, based on the similarity with positive contexts (rules) and the dissimilarity with negative ones. The proposed model not only enhances the previous method (i.e., dominance-based rough set approach, DRSA) on ranking within the same decision class, but also provides a contextual approach to guide businesses for systematic improvements. Infused with the modified VIKOR method, the proposed model could support a company to transform analytics into priority contexts, which may guide improvement planning. To show the proposed model, a group of semiconductor companies in Taiwan is analyzed as an empirical case, and three companies are taken as examples to illustrate the ranking and improvement planning processes. The obtained findings thus contribute to bridge the applications of data-driven business analytics to the field of decision science in practice.

While the importance of Corporate Sociable Responsibility (CSR) has been widely acknowledged, research on how to guide a company in evaluating and improving its CSR performance is relatively under-explored. This paper adopts the predominant framework from the United Nations (UN) and proposes a refined CSR model by using a hybrid multiple criteria decision-making (MCDM) approach. The proposed approach is expected to mitigate the potential information asymmetry issue that might deteriorate the CSR performance of a company. To illustrate the hybrid approach, this study analyzes the CSR performance of four publicly listed information technology (IT) manufacturing companies with the participation of senior domain experts, by using the proposed approach. The CSR performance ranking results are consistent by using various experiments, which is similar to the annual CSR contest held by a prominent organization from Taiwan in 2019. In addition, we illustrate how to apply this refined model to gain managerial insights and pursue sustainable CSR improvement with a priority.

While the importance of corporate governance has been broadly acknowledged in global financial markets and academic research, how to devise a&nbsp, practical&nbsp, evaluation system is relatively unexplored. This paper attempts to refine the Corporate Governance Evaluation System (CGES), constructed by the Taiwan Stock Exchange (TWSE) since 2014. The current CGES has several debatable issues in its complicated design (e.g., it comprises over 80 indicators in different types). To resolve those issues, this study invited ten senior domain experts (including several CEOs of financial holding companies) to retrieve 13 essential criteria from the CGES in four dimensions. Additionally, this study integrates several multiple criteria decision-making (MCDM) methods (i.e., decision-making trial and evaluation laboratory (DEMATEL), modified VIKOR, DEMATEL-based analytical network process (DANP)) and the fuzzy evaluation technique to rank the exemplary companies. The final ranking is consistent with the one released from the CGES in 2017. This study conducted additional experiments to ensure the robustness of the findings. The newly devised model not only assists the ranking decisions but also supports a company in discussing the plausible action plans to strengthen corporate governance based on the analytics. These findings enrich the understanding of corporate governance and contribute to gaining business sustainability for financial holding companies.

This study proposes a novel multiple rule-base decision-making (MRDM) model to transform the current bipolar model into a multi-graded one based on the theoretical foundation of rough set approximations. In the existing bipolar model, the decision class (DC) comprises only three classes: positive, others, and negative ones, and the induced positive or negative rules by the dominance-based rough set approach (DRSA) or variable-consistency dominance-based rough set approach (VC-DRSA) are constrained by the dominance relationship. In certain scenarios or applications, the decision attribute of a bipolar model might need to be transformed into multi-graded DCs to meet practices; examples are the commonly observed Likert 5-point scale questionnaire adopted in a marketing survey. In other words, by eliciting a decision maker’s (DM’s) preferential judgements on the preferred degree of each DC, the newly proposed model may be more flexible to reflect the DM’s preferences or knowledge on modeling an application in a more delicate manner. To reach this goal, the present study proposes a novel MRDM model with multi-graded preferential degree of each DC. Furthermore, the performance of each alternative’s score on each rule can be assessed by the crisp (i.e., binary) or fuzzy set technique (FST) and aggregated by a linear or nonlinear operator. This study provides an exemplary case by evaluating the performance of a group of financial holding companies in Taiwan by using the binary assessment and the simple additive weight (SAW) aggregator. The obtained ranking by evaluating their financial data in 2016 is consistent with their actual financial performance in 2017, which suggests the validity of the proposed model.

While the importance of corporate governance has been broadly acknowledged in global financial markets and academic research, how to devise a practical evaluation system is relatively unexplored. This paper attempts to refine the Corporate Governance Evaluation System (CGES), constructed by the Taiwan Stock Exchange (TWSE) since 2014. The current CGES has several debatable issues in its complicated design (e.g., it comprises over 80 indicators in different types). To resolve those issues, this study invited ten senior domain experts (including several CEOs of financial holding companies) to retrieve 13 essential criteria from the CGES in four dimensions. Additionally, this study integrates several multiple criteria decision-making (MCDM) methods (i.e., decision-making trial and evaluation laboratory (DEMATEL), modified VIKOR, DEMATEL-based analytical network process (DANP)) and the fuzzy evaluation technique to rank the exemplary companies. The final ranking is consistent with the one released from the CGES in 2017. This study conducted additional experiments to ensure the robustness of the findings. The newly devised model not only assists the ranking decisions but also supports a company in discussing the plausible action plans to strengthen corporate governance based on the analytics. These findings enrich the understanding of corporate governance and contribute to gaining business sustainability for financial holding companies.

A combined soft computing model for value investing is proposed.The DRSA model generated 20 rules to classify value stocks.Two strong decision rules were obtained for conducting FCA analysis.The selected "Good" value stock portfolio outperformed the market index.Implications for value stock selection are obtained by DRSA and FCA. The stock selection problem is one of the major issues in the investment industry, which is mainly solved by analyzing financial ratios. However, considering the complexity and imprecise patterns of the stock market, obvious and easy-to-understand investment rules, based on fundamental analysis, are difficult to obtain. Therefore, in this paper, we propose a combined soft computing model for tackling the value stock selection problem, which includes dominance-based rough set approach, formal concept analysis, and decision-making trial and evaluation laboratory technique. The objectives of the proposed approach are to (1) obtain easy-to-understand decision rules, (2) identify the core attributes that may distinguish value stocks, (3) explore the cause-effect relationships among the attributes or criteria in the strong decision rules to gain more insights. To examine and illustrate the proposed model, this study used a group of IT stocks in Taiwan as an empirical case. The findings contribute to the in-depth understanding of the value stock selection problem in practice.

Financial performance is vital for information technology (IT) companies to survive intense global competition. Because of the complexity in the business environment and the rapidly advancing technologies, companies lack specific guidance to understand the implicit relationship among crucial financial indicators for improving prospects in a contextual approach. To resolve the aforementioned concern, this study proposed a new approach by combining the variable consistency dominance-based rough set approach (VC-DRSA) with the decision-making trial and evaluation laboratory (DEMATEL) technique to explore the complex relationship among financial variables and improve future performances. In addition, a fuzzy inference system was devised on the basis of the findings of the VC-DRSA and DEMATEL technique to examine granulized knowledge and implications. A group of real IT companies listed on the Taiwan stock market were used as an empirical case to present the benefits of the new approach. The results generated a set of decision rules that can be used for forecasting future performance prospects and diagnosing the directional influences of crucial variables to gain insights; certain strong decision rules were further examined using fuzzy inference to verify the obtained implications. The findings contribute to the financial applications of decision-making science and computational intelligence in practice.

To support investment decision based on technical analysis (TA), this study aims to retrieve the knowledge or rules of various indicators by a hybrid soft computing model. Although the validity of TA has been examined extensively by various statistical methods in literature, previous studies mainly explored the effectiveness of each technical indicator separately; therefore, a practical approach that may consider the inconsistency of various technical indicators simultaneously and the down-side risk of an investment decision is still underexplored. Thus, a hybrid model—by constructing a variable consistency dominance-based rough set approach (VC-DRSA) information system with the fuzzy inference-enhanced discretization of signals—is proposed, to retrieve the imprecise patterns from commonly adopted technical indicators. At the first stage, the trading signals (i.e., buy, neutral, or sell) are preprocessed in two groups: straight-forward signals and complicated signals. The straight-forward technical indicators (i.e., for signals that are decided by precise rules) are suggested by domain experts, and the buy-in signals are simulated by several trading strategies to examine the outcomes of each indicator. As for those complicated signals (i.e., for signals that require imprecise judgments with perceived feeling of domain experts to identify patterns), a fuzzy inference technique is incorporated to enhance the discretization of signals; those signals are also simulated by the aforementioned trading strategies to obtain the corresponding results. At the second stage, the trading signals generated by each technical indicator and their pertinent results from the previous stage are combined for VC-DRSA modeling to gain decision rules. To illustrate the proposed model, the weighted average index of the Taiwan stock market was examined from mid/2002 to mid/2014, and a set of decision rules with nearly 80 % classification accuracy (both in the training and the testing sets) were obtained in this empirical case. The findings suggest that several technical indicators should be considered simultaneously, and the retrieved rules (knowledge) have practical implications for investors.

The influence and importance of research and development (R&D) for business sustainability have gained increasing interests, especially in the high-tech sector. However, the efforts of R&D might cause complex and mixed impacts on the financial results considering the associated expenses. Thus, this study aims to examine how R&D efforts may influence business to improve its financial performance considering the dual objectives: the gross and the net profitability. This research integrated a rough-set-based soft computing technique and multiple criteria decision-making (MCDM) methods to explore this complex and yet valuable issue. A group of public listed companies from Taiwan, all in the semiconductor sector, was analyzed as a case study. Initially, more than 30 variables were considered, and the adopted soft computing technique retrieved 14 core attributes—for the dual profitability objectives—to form the evaluation model. The importance of R&D for pursuing superior financial prospects is confirmed, and the empirical case demonstrates how to guide an individual company to plan for improvements to achieve its long-term sustainability by this hybrid approach.

This study proposes a combined method to integrate soft computing techniques and multiple criteria decision making (MCDM) methods to guide semiconductor companies to improve financial performance (FP)– based on logical reasoning. The complex and imprecise patterns of FP changes are explored by dominance-based rough set approach (DRSA) to find decision rules associated with FP changes. Companies may identify its underperformed criterion (gap) to conduct formal concept analysis (FCA)– by implication rules– to explore the source criteria regarding the underperformed gap. The source criteria are analysed by decision making trial and evaluation laboratory (DEMATEL) technique to explore the cause-effect relationship among the source criteria for guiding improvements; in the next, DEMATEL-based analytical network process (DANP) can provide the influential weights to form an evaluation model, to select or rank improvement plans. To illustrate the proposed method, the financial data of a real semiconductor company is used as an example to show the involved processes: from performance gaps identification to the selection of five assumed improvement plans. Moreover, the obtained implication rules can integrate with DEMATEL analysis to explore directional influences among the critical criteria, which may provide rich insights and managerial implications in practice. First published online:17 Sep 2015

This study proposes a bipolar model for resolving multiple criteria decision-making (MCDM) problems, based on the integration of rough set theory and three-way decisions, for forming a hybrid bipolar model. It begins by dividing a decision space into three disjoint regions: negative, neutral, and positive states, in the next, a rough set theory based rule induction mechanism generates two sets of rules associated with the positive and the negative states respectively, termed as the positive and the negative rules. The two groups of rules are regarded the bipolar rough experience (knowledge) in the form of rules and granulized knowledge, and decision makers can then define a threshold to select the covered number of rules in the bipolar decision model. This novel approach not only supports decision makers to select or rank alternatives, but also identify rough knowledge for the addressed problem. As a result, this novel bipolar model transforms soft computing analytics into a comprehensible bipolar model for decision aids.

Dominance-based rough set approach (DRSA) has been adopted in solving various multiple criteria classification problems with positive outcomes; its advantage in exploring imprecise and vague patterns is especially useful concerning the complexity of certain financial problems in business environment. Although DRSA may directly process the raw figures of data for classifications, the obtained decision rules (i.e., knowledge) would not be close to how domain experts comprehend those knowledge—composed of granules of concepts—without appropriate or suitable discretization of the attributes in practice. As a result, this study proposes a hybrid approach, composes of DRSA and a multiple attributes decision method, to search for suitable approximation spaces of attributes for gaining applicable knowledge for decision makers (DMs). To illustrate the proposed idea, a case of life insurance industry in Taiwan is analyzed with certain initial experiments. The result not only improves the classification accuracy of the DRSA model, but also contributes to the understanding of financial patterns in the life insurance industry.

The importance of research and development (R&D) for business sustainability have gained increasing interests, especially in the high-tech sector. However, the efforts of R&D might cause complex and mixed impacts on the financial results considering the associated expenses. Thus, this study aims to examine how R&D efforts may influence business to improve its financial performance considering the dual objectives: the gross and the net profitability. This research integrates a rough-set-based soft computing technique and multiple criteria decision-making (MCDM) methods to explore this complex and yet valuable issue. A group of public listed companies from Taiwan, all in the semiconductor sector, is analyzed as a case study. More than 30 variables are considered, and the adopted soft computing technique retrieves 14 core attributes—for the dual profitability objectives—to form the evaluation model. The importance of R&D for pursuing superior financial prospects is confirmed, and the empirical case demonstrates how to guide an individual company to plan for improvements to achieve its long-term sustainability by this hybrid approach.

This study aims to retrieve useful knowledge from commonly adopted technical indicators, based on a soft computing model, to support investment decisions. Though the validity of technical analysis (TA) has been examined extensively by various statistical models in financial literature, a practical approach that may consider the inconsistency among various technical indicators and the down-side risk of an investment is still underexplored. As a result, the present study takes a twostage approach to construct a variable consistency dominancebased rough set approach (VC-DRSA) model, to retrieve the imprecise patterns and implicit knowledge from technical indicators. At the first stage, the trading signals indicated by various technical indicators are suggested by domain experts, and those signals were simulated by a trading strategy to examine the outcomes of each indicator. And the simulation results of each technical indicator are further processed by VC-DRSA model at the second stage for retrieving decision rules (i.e., knowledge). To illustrate the proposed model, the weighted average index of the Taiwan stock market was examined by using its historical data from mid/2002 to mid/2014, and a set of decision rules with more than 70% classification accuracy were inducted in this empirical case. The findings suggest that certain technical indicators should be considered simultaneously, and the obtained rules have practical implications for investors.

Dominance-based rough set approach has been widely applied in multiple criteria classification problems, and its major advantage is the inducted decision rules that can consider multiple attributes in different contexts. However, if decision makers need to make ranking/selection among the alternatives that belong to the same decision class—a typical multiple criteria decision making problem, the obtained decision rules are not enough to resolve the ranking problem. Using a group of semiconductor companies in Taiwan, this study proposes a decision rules-based probabilistic evaluation method, transforms the strong decision rules into a probabilistic weighted model—to explore the performance gaps of each alternative on each criterion—to make improvement and selection. Five example companies were tested and illustrated by the transformed evaluation model, and the result indicates the effectiveness of the proposed method. The proposed evaluation method may act as a bridge to transform decision rules (from data-mining approach) into a decision model for practical applications.

As today's financial market is highly dynamics and comprised of complex factors, few robust models are available for supporting value investors and most of the investment decisions are mainly relied on the judgments of senior investment experts. Among accounting-based fundamental analysis, F-Score system has been a widely accepted model for value investing [1]. F-Score system proposed nine fundamental signals to measure three aspects of a firm's financial conditions: (1) profitability, (2) financial leverage/ liquidity, and (3) operating efficiency. However, the manner which is based on historical financial information would face the difficulty of acquiring implicit knowledge of senior investment experts and accumulating knowledge. To improve the applicability of F-Score system, this research integrates fuzzy set theory and analytic network process (ANP) to propose an innovative model for distinguishing strong financial prospect stocks among high book-to-market (B/M) stocks. Five firms ranked top 20% B/M ratio were benchmarked to test the Fuzzy-ANP model. The practicability of the proposed model is verified by real stocks' data collected from April 2008 to December 2009 in Taiwan.