Fuzzy Intelligence Learning Based on Bounded Rationality in IoMT Systems: A Case Study in Parkinson’s Disease

Zhang, Chao; Ding, Juanjuan; Zhan, Jianming; Sangaiah, Arun Kumar; Li, Deyu

doi:10.1109/tcss.2022.3221933

Cited by 41 publications

(10 citation statements)

References 45 publications

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“…This can help to provide a more transparent and understandable explanation of how the algorithm arrived at its output. Recent examples of the application of GrC and FM in healthcare are in the disease areas of Parkinson's disease [38], COVID-19 [39], and Alzheimer's disease [40].…”

Section: Explanation Methods: Granular Computing (Grc) and Fuzzy Mode...mentioning

confidence: 99%

Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare

Hulsen

2023

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Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks, and deep learning. AI can be applied in many different areas, such as econometrics, biometry, e-commerce, and the automotive industry. In recent years, AI has found its way into healthcare as well, helping doctors make better decisions (“clinical decision support”), localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a “black box”, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans. In this narrative review, we will have a look at some central concepts in XAI, describe several challenges around XAI in healthcare, and discuss whether it can really help healthcare to advance, for example, by increasing understanding and trust. Finally, alternatives to increase trust in AI are discussed, as well as future research possibilities in the area of XAI.

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Section: Explanation Methods: Granular Computing (Grc) and Fuzzy Mode...mentioning

confidence: 99%

Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare

Hulsen

2023

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“…Stochastic programming is appropriate when historical information demonstrating parameter behavior is accessible and manifested by probability distribution functions. For parameters with characteristics of fuzzy numbers, fuzzy programming is used ( Zhang et al, 2022 , Zhang et al, 2020 ). Robust programming is usually considered when sufficient data is not available to estimate the probability distribution function of the aforementioned uncertain parameters ( Ben-Tal et al, 2009 ).…”

Section: Methodsmentioning

confidence: 99%

Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era

Moadab¹,

Kordi²,

Paydar³

et al. 2023

Expert Systems with Applications

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“…It is noted that there are many popular methods can be applied to evaluate the FDI risk. For example, TOPSIS [14], the MADM techniques with three-way decisions [22][23][24], EDAS (Evaluation based on Distance from Average Solution) [25], COPRAS (Complex Proportional Assessment) [26], WASPAS (Weighted Aggregates Sum Product Assessment) [27], SECA (Simultaneous Evaluation of Criteria and Alternatives) [28], CODAS (Combinative Distancebased Assessment) [29], SWARA (Stepwise Weight Assessment Ratio Analysis) [30], MEREC (Method based on the Removal Effects of Criteria) [31]. Compared with these methods, the VIKOR is a MCDM technique designed to rank a set of alternatives in the presence of conflicting criteria by proposing a compromise solution.…”

Section: Plos Onementioning

confidence: 99%

Multi-attribute decision making based on VIKOR with probabilistic linguistic term sets: An application to the risk evaluation of foreign direct investment

Xu,

Zhang,

et al. 2024

PLoS ONE

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The multiple global environments have triggered changes in the international environment, leading to a sharp decline of foreign direct investment (FDI) compared to pre-pandemic level. To evaluate the investment risk of FDI and make optimal investment decision becomes the most important issue for investors. This paper focuses on the evaluation of investment risk for FDI. First, an index system for risk evaluation of FDI is constructed. Then, we introduce the probabilistic linguistic entropy and cross entropy measures, based on which, a programming model is developed to identify the objective attribute weights. A composite weight derivation method, which takes both the objective attribute weights and the subjective attribute weights into account, is further introduced. In view of attributes’ uncertainty and fuzziness and the conflicting characteristics of some attributes, the VIKOR (the Serbian name: VlseKriterijumska Optimizacija I Kompromisno Resenje, means multi-criteria optimization and compromise solution) method is used to evaluate the risk of FDI under the probabilistic linguistic environment. Furthermore, a case study is presented to illustrate the proposed method. The comparative analysis and some further discussions verify the validity of the proposed method for the FDI risk evaluation.

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Fuzzy Intelligence Learning Based on Bounded Rationality in IoMT Systems: A Case Study in Parkinson’s Disease

Cited by 41 publications

References 45 publications

Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare

Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare

Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era

Multi-attribute decision making based on VIKOR with probabilistic linguistic term sets: An application to the risk evaluation of foreign direct investment

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