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Axioms
Special Issues
Intelligent and Fuzzy Systems
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Intelligent and Fuzzy Systems

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 17755

Special Issue Editors

Assist. Prof. Dr. Ming-Fu Hsu

E-Mail Website
Guest Editor
English Program of Global Business, Chinese Culture University, Taipei 11114, Taiwan
Interests: data/text mining; performance analysis; multiple criteria deacision making; intelligent system; data envelopment analysis
Prof. Dr. Fu-Hsiang Chen

E-Mail Website
Co-Guest Editor
Department of Accounting, Chinese Culture University, Taipei 11114, Taiwan
Interests: sustainability accounting standard board(SASB); corporate sustainability reports; multiple rule/rough‑based decision‑making; machine learning; data mining; text mining
Special Issues, Collections and Topics in MDPI journals
Assoc. Prof. Dr. Kuang-Hua Hu

E-Mail Website
Co-Guest Editor
Accounting School, Nanfang College of Sun Yat-sen University, Guangzhou 510970, China
Interests: multiple criteria deacision making; financial accounting; intelligent system; data mining

Special Issue Information

Dear Colleagues,

We wish to invite researchers and professionals from universities, enterprises, and governmental units to share innovative ideas, concepts, trends, and experiences concerning any issues related to intelligent and fuzzy systems. Topics may include, but are not limited to, the following:

  • Intelligent and fuzzy systems in business operation;
  • Intelligent and fuzzy systems in sustainable development;
  • Intelligent and fuzzy systems in risk management;
  • Intelligent and fuzzy systems in earnings management;
  • Intelligent and fuzzy systems in corporate governance;
  • Intelligent and fuzzy systems in various industries;
  • Intelligent and fuzzy systems in production;
  • Intelligent and fuzzy systems in performance measurement;
  • Intelligent and fuzzy systems in strategy determination.

Assist. Prof. Dr. Ming-Fu Hsu
Prof. Dr. Fu-Hsiang Chen
Assoc. Prof. Dr. Kuang-Hua Hu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (5 papers)

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Research

23 pages, 881 KiB  
Article
Value Creation Performance Evaluation for Taiwanese Financial Holding Companies during the Global Financial Crisis Based on a Multi-Stage NDEA Model under Uncertainty
by Tzu-Yu Lin, Sheng-Hsiung Chiu, Yunxi Wang and Zihan Ouyang
Axioms 2022, 11(2), 35; https://doi.org/10.3390/axioms11020035 - 18 Jan 2022
Cited by 2 | Viewed by 2581
Abstract
In this paper, we use the multi-stage network slacks-based measure model under uncertainty to evaluate the performance of Taiwanese financial holding companies (FHCs) from 2007 to 2012. We conceptualize the value creation process to be a two-stage framework, as the profitability and the [...] Read more.
In this paper, we use the multi-stage network slacks-based measure model under uncertainty to evaluate the performance of Taiwanese financial holding companies (FHCs) from 2007 to 2012. We conceptualize the value creation process to be a two-stage framework, as the profitability and the marketability stages with a serial-linkage relationship, in order to solicit more information from the value creation process of FHCs. Under this framework, the profitability stage can be further divided into banking, insurance, and securities profitability sub-stages. In addition, we further extend the proposed model mentioned above to not only incorporate the NPLs as the undesirable output in the banking profitability stage but also use the fuzzy set approach to process its uncertainty. Results indicate that the discriminatory ability of our model is higher when we simplify the variables in the model specification. We also find that all Taiwanese FHCs in that particular period were “inefficient performers”, which is mainly attributed to the weak profitability performance, especially in banking service. We postulate that the global financial crisis, the US subprime crisis in particular, had a negative effect on the value creation performance of Taiwanese FHCs; however, they gradually recovered and improved their performance after the crisis. Full article
(This article belongs to the Special Issue Intelligent and Fuzzy Systems)
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18 pages, 1783 KiB  
Article
Constructing a Precise Fuzzy Feedforward Neural Network Using an Independent Fuzzification Approach
by Hsin-Chieh Wu, Tin-Chih Toly Chen and Min-Chi Chiu
Axioms 2021, 10(4), 282; https://doi.org/10.3390/axioms10040282 - 28 Oct 2021
Cited by 9 | Viewed by 1807
Abstract
This study discusses how to fuzzify a feedforward neural network (FNN) to generate a fuzzy forecast that contains the actual value, while minimizing the average range of fuzzy forecasts. This topic has rarely been investigated in past studies, but is an essential step [...] Read more.
This study discusses how to fuzzify a feedforward neural network (FNN) to generate a fuzzy forecast that contains the actual value, while minimizing the average range of fuzzy forecasts. This topic has rarely been investigated in past studies, but is an essential step to constructing a precise fuzzy FNN (FFNN). Existing methods fuzzify all parameters at the same time, which re-sults in a nonlinear programming (NLP) problem that is not easy to solve. In contrast, in this study, the parameters of a FNN are fuzzified independently. In this way, the optimal values of fuzzy parameters can be derived theoretically. An illustrative example is used to illustrate the ap-plicability of the proposed methodology. According to the experimental results, fuzzifying the thresholds on hidden-layer nodes or the connection weights between input and hidden layers may not guarantee that all fuzzy forecasts contain the corresponding actual values. In contrast, fuzzi-fying the threshold on the output node and the connection weights between the hidden and out-put layers is more likely to achieve a 100% hit rate. The results lay a foundation for establishing a precise deep FFNN in the future. Full article
(This article belongs to the Special Issue Intelligent and Fuzzy Systems)
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18 pages, 1616 KiB  
Article
An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages
by Hsueh-Li Huang, Sin-Jin Lin and Ming-Fu Hsu
Axioms 2021, 10(3), 179; https://doi.org/10.3390/axioms10030179 - 4 Aug 2021
Cited by 1 | Viewed by 2101
Abstract
Compared to widely examined topics in the related literature, such as financial crises/difficulties in accurate prediction, studies on corporate performance forecasting are quite scarce. To fill the research gap, this study introduces an advanced decision making framework that incorporates context-dependent data envelopment analysis [...] Read more.
Compared to widely examined topics in the related literature, such as financial crises/difficulties in accurate prediction, studies on corporate performance forecasting are quite scarce. To fill the research gap, this study introduces an advanced decision making framework that incorporates context-dependent data envelopment analysis (CD-DEA), fuzzy robust principal component analysis (FRPCA), latent Dirichlet allocation (LDA), and stochastic gradient twin support vector machine (SGTSVM) for corporate performance forecasting. Ratio analysis with the merits of easy-to-use and intuitiveness plays an essential role in performance analysis, but it typically has one input variable and one output variable, which is unable to appropriately depict the inherent status of a corporate’s operations. To combat this, we consider CD-DEA as it can handle multiple input and multiple output variables simultaneously and yields an attainable target to analyze decision making units (DMUs) when the data present great variations. To strengthen the discriminant ability of CD-DEA, we also conduct FRPCA, and because numerical messages based on historical principles normally cannot transmit future corporate messages, we execute LDA to decompose the accounting narratives into many topics and preserve those topics that are relevant to corporate operations. Sequentially, the process matches the preserved topics with a sentimental dictionary to exploit the hidden sentiments in each topic. The analyzed data are then fed into SGTSVM to construct the forecasting model. The result herein reveals that the introduced decision making framework is a promising alternative for performance forecasting. Full article
(This article belongs to the Special Issue Intelligent and Fuzzy Systems)
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25 pages, 12220 KiB  
Article
The Key Success Factors for Attracting Foreign Investment in the Post-Epidemic Era
by Sun-Weng Huang, James J. H. Liou, Shih-Hsiung Cheng, William Tang, Jessica C. Y. Ma and Gwo-Hshiung Tzeng
Axioms 2021, 10(3), 140; https://doi.org/10.3390/axioms10030140 - 30 Jun 2021
Cited by 12 | Viewed by 4750
Abstract
The global economy has been hit by the unexpected COVID-19 outbreak, and foreign investment has been seen as one of the most important tools to boost the economy. However, in the highly uncertain post-epidemic era, determining how to attract foreign investment is the [...] Read more.
The global economy has been hit by the unexpected COVID-19 outbreak, and foreign investment has been seen as one of the most important tools to boost the economy. However, in the highly uncertain post-epidemic era, determining how to attract foreign investment is the key to revitalizing the economy. What are the important factors for governments to attract investment, and how to improve them? This will be an important decision in the post-epidemic era. Therefore, this study develops a novel decision-making model to explore the key factors in attracting foreign investment. The model first uses fuzzy Delphi to explore the key factors of attracting foreign investment in the post-epidemic era, and then uses DEMATEL to construct the causal relationships among these factors. To overcome the uncertainty of various information sources and inconsistent messages from decision-makers, this study combined neutrosophic set theory to conduct quantitative analysis. The results of the study show that the model is suitable for analyzing the key factors of investment attraction in the post-epidemic period. Based on the results of the study, we also propose strategies that will help the relevant policy-making departments to understand the root causes of the problem and to formulate appropriate investment strategies in advance. In addition, the model is also used for comparative analysis, which reveals that this novel approach can integrate more incomplete information and present expert opinions in a more objective way. Full article
(This article belongs to the Special Issue Intelligent and Fuzzy Systems)
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20 pages, 1308 KiB  
Article
Identification of the Critical Factors for Global Supply Chain Management under the COVID-19 Outbreak via a Fusion Intelligent Decision Support System
by Kuang-Hua Hu, Fu-Hsiang Chen, Ming-Fu Hsu, Shuyi Yao and Ming-Chin Hung
Axioms 2021, 10(2), 61; https://doi.org/10.3390/axioms10020061 - 16 Apr 2021
Cited by 6 | Viewed by 4437
Abstract
Under the ravages of COVID-19, global supply chains have encountered unprecedented disruptions. Past experiences cannot fully explain the situations nor provide any suitable responses to these fatal shocks on supply chain management (SCM), especially in todays’ highly intertwined/globalized business environment. This research thus [...] Read more.
Under the ravages of COVID-19, global supply chains have encountered unprecedented disruptions. Past experiences cannot fully explain the situations nor provide any suitable responses to these fatal shocks on supply chain management (SCM), especially in todays’ highly intertwined/globalized business environment. This research thus revisits and rechecks the crucial components for global SCM during such special periods, and the basic essence of such management covers numerous perspectives that can be categorized into a multiple criteria decision making (MCDM) approach. To handle this complex issue appropriately, one can introduce a fusion intelligent system that involves data envelopment analysis (DEA), rough set theory (RST), and MCDM to understand the reality of the analyzed problem in a faster and better manner. Based on the empirical results, we rank the priorities in order as cash management and information (D), raw material supply (B), global management strategy (C), and productivity and logistics (A) for improvement in SCM. This finding is confirmed by companies now undergoing a downsizing strategy in order to survive in this harsh business environment. Full article
(This article belongs to the Special Issue Intelligent and Fuzzy Systems)
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