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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.

This paper focuses on two Apriori-based rule generators. The first is the rule generator in Prolog and C, and the second is the one in SQL. They are namedApriori in PrologandApriori in SQL, respectively. Each rule generator is based on the Apriori algorithm. However, each rule generator has its own properties. Apriori in Prolog employs the equivalence classes defined by table data sets and follows the framework of rough sets. On the other hand, Apriori in SQL employs a search for rule generation and does not make use of equivalence classes. This paper clarifies the properties of these two rule generators and considers effective applications of each to existing data sets.

A recent review discussed a variety of hybrid multiple criteria decision-making (H.M.C.D.M.) methods on the subject of sustainability issues. Some soft computing techniques, such as the fuzzy set, have contributed significantly to H.M.C.D.M. studies, emulating the imprecise or uncertain judgments of experts/decision makers in a complex environment. Nevertheless, a new rising trend in H.M.C.D.M., known as multiple rule-based decision-making (M.R.D.M.), which has the advantage of revealing understandable knowledge for supporting systematic improvements based on influential network relation maps (I.N.R.M.), was not discussed in the review. This study therefore attempts to extend the review by introducing recent developments and the associated work on M.R.D.M. for solving practical problems, updating the discussion.

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.

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.

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.

With the surging complexity of real-world problems in important domains such as sustainability, there is a need to leverage advanced modern computational methods or intelligent techniques to support decisions or policy-making. In this Special Issue, 15 selected and formally peer-reviewed papers contribute their novelty and findings, by applying various advanced decision methods or computational techniques to resolve different sustainability problems. Despite the innovations of the proposed models, most of the selected papers involve domain expert’s opinions and knowledge with in-depth discussions. These case studies enrich the practical contributions of this Special Issue.

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.

The debate of “short-termism” has gained increasing interests from various fields, ranging from management to economics; it mainly concerns the decisions or actions taken by businesses that might yield short-term returns at the cost of long-term value or sustainability. Previous studies have highlighted this dilemma faced by managers, mainly from the pressure of capital markets or short-sighted shareholders who crave for immediate financial outcomes; intelligent decision aids that can compromise between the short- and long-term financial sustainability, based on a company’s policy, are highly needed. Therefore, the aim of this study is to develop a multiple-rule-based hybrid decision model to support management teams on prioritizing new R&D projects, considering the financial prospects in dual timeframes (i.e., short- and long-term) for sustainability. Furthermore, in the presence of business uncertainty and the limited knowledge of managers on new projects, the intuitionistic fuzzy technique is incorporated. A case of selecting new R&D projects for an IC design company is illustrated using the proposed approach, and the financial data from a group of public-listed IC stocks from Taiwan are inducted to form the decision model. The findings not only support the IC design company to select new projects but also provide business insights to facilitate the understandings of this controversial issue in managerial practice.

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

Although the use of earnings prediction in supporting investment decisions has been prevailing in practice, an accounting-based analysis for modeling the key accounting components by time-delay machine learning technique is unexplored. Traditional time-series techniques fail to handle complex data structure, and the fundamental analysis approach cannot model multiple periods’ data effectively. Thus, this study aims to explore the crucial relationships among future earnings and the main historical accounting components, i.e. cash-flow and accrual components. The research method leverages the flexible learning capability of artificial neural network (ANN) with time-delay data structure. The major findings suggested that adding accrual components is helpful for better earnings prediction, and the proposed 5-period time-delay ANN model may capture the future earnings in a positive way. The results of this study may help to support investment decisions and better understanding for the role of accruals in earnings.

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.

Research and development (R&D) is inherently important to high-tech industry, such as semiconductor companies. However, the complex and ambiguous relationship between R&D and the subsequent financial performance varies and is still underexplored. Previous studies based on statistical analyses are not able to provide individual guidance considering the specific circumstance/status of a company. Therefore, the aim of this study is to explore the inherent patterns or rules — by a two-stage soft computing model — to guide semiconductor companies to improve its financial performance, considering R&D and the other factors. This research uses a group of semiconductor companies in Taiwan as an empirical case, and the research period ranges from 2006 to 2013. Compare with the conventional discriminant analysis approach, the obtained decision rules from the proposed rule-based analytic model shows superior results that may classify the financial performance of semiconductor companies by a set of financial and R&D indicators with 75% accuracy in average. In addition, we also show how to use the retrieved knowledge (i.e., rules) to guide a real company to improve its performance in the future. The findings contribute to the understanding of R&D influences to the financial performance of semiconductor companies with managerial implications in practice.

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.

Stock market is full of opportunity and uncertainty, which is characterized by return and risk. Investors have to utilize their knowledge and experience to explore the available information for selecting stocks with good prospects. Investment professionals mainly utilize available financial information for making evaluation, which is often termed as fundamental analysis. Nevertheless, which financial information (variables/ criteria) should be considered and the way for analysis is still inconclusive. Furthermore, while making comparison among a group of stocks, different criteria sometime reveal inconsistent signals. To handle the complexity of financial information analysis, this study proposes an integrated method to deal with the glamour stock evaluation problem. The method is based on an examined financial model. In the first stage, the weights of criteria are obtained by DANP method, and the second stage integrates the influential weights with expert's assessment for each criterion to complete the modified TOPSIS based on influential network relation map (INRM) for making decision in improvement direction. This study provides an empirical case for 5 real stocks in the Taiwan market as example. The suggested ranking result generated high correlation with the 5 stocks' subsequent holding period returns, which indicates the usefulness of the proposed method.