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  1. AU="Yu-Cheng Wang"
  2. AU="Post, Robert"
  3. AU="Pemmari, Toini"
  4. AU="Stefanik-Guizlo, Kelsey"
  5. AU=Elsea Sarah H
  6. AU="Barbara Mognetti"
  7. AU="Gibb, Jonathan"
  8. AU="Garg, Priya S"
  9. AU="Van Driessche, Veroniek"
  10. AU="Solianova, Veronika"
  11. AU="Strauss, Sarah"
  12. AU="Messemaker, Tobias C"
  13. AU="Daniel, Maria Urszula"
  14. AU=Edwards Robert J AU=Edwards Robert J
  15. AU="Shriver, Craig D"
  16. AU="Huang, Xiang-Zhong"
  17. AU=Cabanne Eglantine
  18. AU="Bernal, A"
  19. AU="Malorie Perry"
  20. AU="Oppenheim, Alan"
  21. AU="Ozcan, Muhit"
  22. AU="Zhang, Cissy"
  23. AU="Blaize, Justin L"
  24. AU="R, Ram Babu"
  25. AU="Khalili Arash"
  26. AU="Bhatia, Sandeep"
  27. AU="Ticha, Johnson M"
  28. AU="Aranzabal Barrio, N"

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  1. Artikel ; Online: New XAI tools for selecting suitable 3D printing facilities in ubiquitous manufacturing

    Yu-Cheng Wang / Toly Chen

    Complex & Intelligent Systems, Vol 9, Iss 6, Pp 6813-

    2023  Band 6829

    Abstract: Abstract Several artificial intelligence (AI) technologies have been applied to assist in the selection of suitable three-dimensional (3D) printing facilities in ubiquitous manufacturing (UM). However, AI applications in this field may not be easily ... ...

    Abstract Abstract Several artificial intelligence (AI) technologies have been applied to assist in the selection of suitable three-dimensional (3D) printing facilities in ubiquitous manufacturing (UM). However, AI applications in this field may not be easily understood or communicated with, especially for decision-makers without relevant background knowledge, hindering the widespread acceptance of such applications. Explainable AI (XAI) has been proposed to address this problem. This study first reviews existing XAI techniques to explain AI applications in selecting suitable 3D printing facilities in UM. This study addresses the deficiencies of existing XAI applications by proposing four new XAI techniques: (1) a gradient bar chart with baseline, (2) a group gradient bar chart, (3) a manually adjustable gradient bar chart, and (4) a bidirectional scatterplot. The proposed methodology was applied to a case in the literature to demonstrate its effectiveness. The bidirectional scatterplot results from the experiment demonstrated the suitability of the 3D printing facilities in terms of their proximity. Furthermore, manually adjustable gradient bars increased the effectiveness of the AI application by decision-makers subjectively adjusting the derived weights. Furthermore, only the proposed methodology fulfilled most requirements for an effective XAI tool in this AI application.
    Schlagwörter Ubiquitous manufacturing ; Alpha-cut operations ; Fuzzy technique for order preference by similarity to ideal solution ; Explainable artificial intelligence ; Electronic computers. Computer science ; QA75.5-76.95 ; Information technology ; T58.5-58.64
    Thema/Rubrik (Code) 670 ; 600
    Sprache Englisch
    Erscheinungsdatum 2023-06-01T00:00:00Z
    Verlag Springer
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: A Bi-objective AHP-MINLP-GA approach for Flexible Alternative Supplier Selection amid the COVID-19 Pandemic

    Yu-Cheng Wang / Toly Chen

    Soft Computing Letters, Vol 3, Iss , Pp 100016- (2021)

    2021  

    Abstract: A decision maker may hold multiple viewpoints regarding the relative priorities of criteria simultaneously, but this has rarely been considered in past studies. Therefore, this study proposes a bi-objective analytic hierarchy process (AHP)–mixed integer ... ...

    Abstract A decision maker may hold multiple viewpoints regarding the relative priorities of criteria simultaneously, but this has rarely been considered in past studies. Therefore, this study proposes a bi-objective analytic hierarchy process (AHP)–mixed integer nonlinear programming (MINLP)–genetic algorithm (GA) approach. First, AHP is applied to decompose the decision maker's judgment matrix into several sub-judgment matrices. Each sub-judgment matrix represents a single viewpoint and generates a priority set. To generate diversified priority sets, a bi-objective MINLP problem is solved using a GA, and multiple alternatives can be selected based on these priority sets. The proposed approach has been applied to the real case of choosing diversified alternative suppliers amid the COVID-19 pandemic to assess its effectiveness. Several existing methods were also applied to this case for comparison. Experimental results showed that only the proposed approach was able to diversify the recommended alternative suppliers that were simultaneously optimal, thereby enhancing decision-making flexibility. In addition, the application of GA increased the solution efficiency by up to 75%.
    Schlagwörter Analytic hierarchy process ; Multiple criteria decision making ; Consistency ; Subjudgment matrix ; Bi-objective ; Information technology ; T58.5-58.64 ; Electronic computers. Computer science ; QA75.5-76.95
    Thema/Rubrik (Code) 000
    Sprache Englisch
    Erscheinungsdatum 2021-12-01T00:00:00Z
    Verlag Elsevier
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Artikel ; Online: A systematic approach to enhance the explainability of artificial intelligence in healthcare with application to diagnosis of diabetes

    Yu-Cheng Wang / Tin-Chih Toly Chen / Min-Chi Chiu

    Healthcare Analytics, Vol 3, Iss , Pp 100183- (2023)

    2023  

    Abstract: Explainable artificial intelligence (XAI) tools are used to enhance the applications of existing artificial intelligence (AI) technologies by explaining their execution processes and results. In most past research, XAI tools and techniques are typically ... ...

    Abstract Explainable artificial intelligence (XAI) tools are used to enhance the applications of existing artificial intelligence (AI) technologies by explaining their execution processes and results. In most past research, XAI tools and techniques are typically applied to only the inference part of the AI application. This study proposes a systematic approach to enhance the explainability of AI applications in healthcare. Several AI applications for type 2 diabetes diagnosis are taken as examples to illustrate the applicability of the proposed methodology. According to experimental results, the XAI tools and technologies in the proposed methodology were more diverse than those in the past research. In addition, an artificial neural network was approximated to a simpler and more intuitive classification and regression tree (CART) using local interpretable model-agnostic explanation (LIME). The extracted rules were used to recommend actions to the users to restore their health.
    Schlagwörter Explainable artificial intelligence ; Healthcare ; Local interpretable model-agnostic explanation ; Diabetes diagnosis ; Artificial intelligence ; Computer applications to medicine. Medical informatics ; R858-859.7
    Thema/Rubrik (Code) 401
    Sprache Englisch
    Erscheinungsdatum 2023-11-01T00:00:00Z
    Verlag Elsevier
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Artikel ; Online: An improved explainable artificial intelligence tool in healthcare for hospital recommendation

    Yu-Cheng Wang / Tin-Chih Toly Chen / Min-Chi Chiu

    Healthcare Analytics, Vol 3, Iss , Pp 100147- (2023)

    2023  

    Abstract: Artificial intelligence (AI) technologies have been widely applied in medicine and healthcare. Explainable AI (XAI) has been proposed to make AI applications more transparent and efficient. This study applies some simple cross-domain tools and techniques, ...

    Abstract Artificial intelligence (AI) technologies have been widely applied in medicine and healthcare. Explainable AI (XAI) has been proposed to make AI applications more transparent and efficient. This study applies some simple cross-domain tools and techniques, including common expression (with linguistic terms), color management, traceable aggregation, and segmented distance diagrams, among others, to improve the explainability of AI applications in healthcare. Four applications of AI technologies in hospitals were used, and recommendations were studied to illustrate the applicability of the proposed methodology. The explainability of each AI application was evaluated before and after improvement for comparison. According to the experimental results, these AI-based hospital recommendation methods could be better explained by modifying their explanations using simple and cross-domain tools.
    Schlagwörter Artificial intelligence ; Deep learning ; Explainable artificial intelligence ; Healthcare ; Hospital recommendation ; Computer applications to medicine. Medical informatics ; R858-859.7
    Thema/Rubrik (Code) 401
    Sprache Englisch
    Erscheinungsdatum 2023-11-01T00:00:00Z
    Verlag Elsevier
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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

    Tin-Chih Toly Chen / Teng Chieh Chang / Yu-Cheng Wang

    Digital Health, Vol

    2023  Band 9

    Abstract: Eliminating the NO x 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 ...

    Abstract Eliminating the NO x 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 NO x 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 NO x 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.
    Schlagwörter Computer applications to medicine. Medical informatics ; R858-859.7
    Thema/Rubrik (Code) 006
    Sprache Englisch
    Erscheinungsdatum 2023-07-01T00:00:00Z
    Verlag SAGE Publishing
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  6. Artikel ; Online: Analyzing the impact of COVID-19 vaccination requirements on travelers’ selection of hotels using a fuzzy multi-criteria decision-making approach

    Yu-Cheng Wang / Tin-Chih Toly Chen

    Healthcare Analytics, Vol 2, Iss , Pp 100064- (2022)

    2022  

    Abstract: 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 ... ...

    Abstract 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 VIšekriterijumskoKOmpromisnoRangiranje (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.
    Schlagwörter Hotel recommendation ; Vaccination ; Fuzzy analytic hierarchy process ; Fuzzy geometric mean ; Computer applications to medicine. Medical informatics ; R858-859.7
    Thema/Rubrik (Code) 910
    Sprache Englisch
    Erscheinungsdatum 2022-11-01T00:00:00Z
    Verlag Elsevier
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  7. Artikel ; Online: Analyzing the Impact of Vaccine Availability on Alternative Supplier Selection Amid the COVID-19 Pandemic

    Toly Chen / Yu-Cheng Wang / Hsin-Chieh Wu

    Healthcare, Vol 9, Iss 1, p

    A cFGM-FTOPSIS-FWI Approach

    2021  Band 71

    Abstract: The supply chain disruption caused by the coronavirus disease 2019 (COVID-19) pandemic has forced many manufacturers to look for alternative suppliers. How to choose a suitable alternative supplier in the COVID-19 pandemic has become an important task. ... ...

    Abstract The supply chain disruption caused by the coronavirus disease 2019 (COVID-19) pandemic has forced many manufacturers to look for alternative suppliers. How to choose a suitable alternative supplier in the COVID-19 pandemic has become an important task. To fulfill this task, this research proposes a calibrated fuzzy geometric mean (cFGM)-fuzzy technique for order preference by similarity to ideal solution (FTOPSIS)-fuzzy weighted intersection (FWI) approach. In the proposed methodology, first, the cFGM method is proposed to accurately derive the priorities of criteria. Subsequently, each expert applies the FTOPSIS method to compare the overall performances of alternative suppliers in the COVID-19 pandemic. The sensitivity of an expert to any change in the overall performance of the alternative supplier is also considered. Finally, the FWI operator is used to aggregate the comparison results by all experts, for which an expert’s authority level is set to a value proportional to the consistency of his/her pairwise comparison results. The cFGM-FTOPSIS-FWI approach has been applied to select suitable alternative suppliers for a Taiwanese foundry in the COVID-19 pandemic.
    Schlagwörter COVID-19 pandemic ; alternative supplier ; fuzzy collaborative intelligence ; wafer fabrication ; Medicine ; R
    Thema/Rubrik (Code) 650
    Sprache Englisch
    Erscheinungsdatum 2021-01-01T00:00:00Z
    Verlag MDPI AG
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  8. Artikel ; Online: Assessing the Robustness of a Factory Amid the COVID-19 Pandemic

    Toly Chen / Yu-Cheng Wang / Min-Chi Chiu

    Healthcare, Vol 8, Iss 481, p

    A Fuzzy Collaborative Intelligence Approach

    2020  Band 481

    Abstract: The COVID-19 pandemic has affected the operations of factories worldwide. However, the impact of the COVID-19 pandemic on different factories is not the same. In other words, the robustness of factories to the COVID-19 pandemic varies. To explore this ... ...

    Abstract The COVID-19 pandemic has affected the operations of factories worldwide. However, the impact of the COVID-19 pandemic on different factories is not the same. In other words, the robustness of factories to the COVID-19 pandemic varies. To explore this topic, this study proposes a fuzzy collaborative intelligence approach to assess the robustness of a factory to the COVID-19 pandemic. In the proposed methodology, first, a number of experts apply a fuzzy collaborative intelligence approach to jointly evaluate the relative priorities of factors that affect the robustness of a factory to the COVID-19 pandemic. Subsequently, based on the evaluated relative priorities, a fuzzy weighted average method is applied to assess the robustness of a factory to the COVID-19 pandemic. The assessment result can be compared with that of another factory using a fuzzy technique for order preference by similarity to ideal solution. The proposed methodology has been applied to assess the robustness of a wafer fabrication factory in Taiwan to the COVID-19 pandemic.
    Schlagwörter COVID-19 pandemic ; robustness ; fuzzy collaborative intelligence ; wafer fabrication ; Medicine ; R
    Thema/Rubrik (Code) 006
    Sprache Englisch
    Erscheinungsdatum 2020-11-01T00:00:00Z
    Verlag MDPI AG
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  9. Artikel ; Online: Transcriptome analysis reveals gene expression differences in Liriomyza trifolii exposed to combined heat and abamectin exposure

    Yu-Cheng Wang / Ya-Wen Chang / Yu-Zhou Du

    PeerJ, Vol 9, p e

    2021  Band 12064

    Abstract: Liriomyza trifolii is an invasive pest of horticultural and vegetable crops that possesses robust competitive advantages that enable it to replace closely-related species. High temperatures often occur concomitantly with insecticide usage during L. ... ...

    Abstract Liriomyza trifolii is an invasive pest of horticultural and vegetable crops that possesses robust competitive advantages that enable it to replace closely-related species. High temperatures often occur concomitantly with insecticide usage during L. trifolii outbreaks. In this study, we compared the transcriptomes of L. trifolii exposed to high temperature (40 °C T40), insecticide (LC50 of technical grade abamectin, I50) and combined high temperature and abamectin exposure (IT5040, I50 followed by T40; and TI4050, T40 followed by I50). RNA-seq generated and revealed 44,633 unigenes with annotation data; these were compared with COG and KEGG databases for functional classification and enrichment analysis. Compared with the I50 treatment, COG classification indicated that ‘post-translational modification, protein turnover, chaperones’ was enriched in the IT5040 treatment. In the TI4050 treatment, ‘carbohydrate transport and metabolism’ was the most abundant group. The most enriched KEGG pathways in the TI4050 and IT5040 treatments were ‘longevity regulating pathway - multiple species’ and ‘protein processing in endoplasmic reticulum’, respectively. Subsequent annotation and enrichment analyses indicated that stress-related genes such as CYP450s and HSPs were differentially expressed in the I50 vs. TI4050 or I50 vs. IT5040 treatment groups. Three commercial insecticide formulations were also used to further verify the expression of selected differentially-expressed genes. This study will be conductive to consider the temperature effect on insecticide tolerance in L. trifolii, and provides a framework for improving the application efficiency of insecticides in hot weather, which will ultimately reduce the overuse of pesticides.
    Schlagwörter Liriomyza trifolii ; High temperature ; Insecticide tolerance ; Transcriptome ; Medicine ; R ; Biology (General) ; QH301-705.5
    Thema/Rubrik (Code) 580
    Sprache Englisch
    Erscheinungsdatum 2021-08-01T00:00:00Z
    Verlag PeerJ Inc.
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  10. Artikel ; Online: Interface pH regulation to improve ORR performance of FePc catalyst in acid electrolyte

    Yu-Yang Li / Yu-Cheng Wang / Zhi-You Zhou / Shi-Gang Sun

    Electrochemistry Communications, Vol 141, Iss , Pp 107357- (2022)

    2022  

    Abstract: Iron–nitrogen doped carbon (Fe/N/C) electrocatalysts are among the most promising materials to replace Pt for the oxygen reduction reaction (ORR). Up to now, most work has been devoted to improving the performance of Fe/N/C catalysts by material design, ... ...

    Abstract Iron–nitrogen doped carbon (Fe/N/C) electrocatalysts are among the most promising materials to replace Pt for the oxygen reduction reaction (ORR). Up to now, most work has been devoted to improving the performance of Fe/N/C catalysts by material design, while ignoring the design of the electrode/electrolyte interface environment. Considering the superior ORR performance of Fe/N/C catalysts in alkaline electrolyte, we attempt to construct a proton-deficient environment at the electrode surface to raise the local pH value. An anion-exchange ionomer with H+ blocking ability was chosen as the binder of the iron phthalocyanine (FePc) catalyst, a model molecule for Fe/N/C. The anion-exchange ionomer can increase the interface pH value as compared with commonly used Nafion binder, which was verified by the electrochemical shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) technique with CO32–/HCO3– as a probe. This strategy significantly improves the ORR activity and stability of the FePc catalyst in acidic medium.
    Schlagwörter Oxygen reduction reaction ; Non-precious metal catalysts ; Macrocyclic compound ; Interface environment ; In situ Raman spectroscopy ; Industrial electrochemistry ; TP250-261 ; Chemistry ; QD1-999
    Thema/Rubrik (Code) 660
    Sprache Englisch
    Erscheinungsdatum 2022-08-01T00:00:00Z
    Verlag Elsevier
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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