A New Multi-Attribute Emergency Decision-Making Algorithm Based on Intuitionistic Fuzzy Cross-Entropy and Comprehensive Grey Correlation Analysis

Li, Ping; Ji, Ying; Wu, Zhongqing; Qu, Shaojian

doi:10.3390/e22070768

Cited by 22 publications

(19 citation statements)

References 40 publications

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“…Gray correlation analysis (GCA) was used to examine the correlation between two groups of variations (P. Li et al, 2020 ). In this study, the signal intensities of the different components present in the 9 GF samples (S1-S9) obtained from different producing areas were measured.…”

Section: Methodsmentioning

confidence: 99%

An Integrated Strategy for Rapid Discovery and Identification of Quality Markers in Gardenia Fructus Using an Omics Discrimination-Grey Correlation-Biological Verification Method

et al. 2021

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Background: Gardenia Fructus (GF), a traditional Chinese medicine of Gardenia Ellis in Rubiaceae family, has the potential to clear heat and purge fire and has been widely used to treat multiple infection-related diseases. However, the quality markers (Q-Markers) of GF have not been revealed comprehensively.Methods: In this experiment, the transgenic zebrafish lines, Tg (l-fabp:EGFP) and Tg (lyz:EGFP), were used to evaluate two main kinds of traditional efficacies of GF, hepatoprotective and anti-inflammatory effects. All the GF samples from different production areas were tested their anti-liver injury and anti-inflammantory activities. High-performance liquid chromatography-quadrupole time-of-flight mass spectrometry method (HPLC-Q-TOF/MS) was employed for herbal metabonomic analysis of GF samples. Gray correlation analysis (GCA) was utilized to screen out the components closely associated with the activities. Finally, the zebrafish model was applied to verify the bioactivity of the crucial components to determine the Q-Markers of GF.Results: The zebrafish models were established by inducing with hydrogen peroxide or copper sulfate and applied to quickly evaluate the hepatoprotective effect and inflammation of GF samples. 27 potentially active components for liver protection and 21 potentially active components with anti-inflammatory properties were identified by herbal metabolomic analysis based on HPLC-Q-TOF/MS. The GCA result showed that five of the 27 components were highly correlated with liver protection, 15 of the 21 components were highly correlated with anti-inflammatory activity. Among them, geniposide and crocin-1 were confirmed their bioactivities on zebrafish experiment to be responsible for the protective effects of GF against liver injury, and genipin-1-β-D-gentiobioside, quinic acid, gardenoside, d-glucuronic acid, l-malic acid, mannitol, rutin, and chlorogenic acid were confirmed to be responsible for the anti-inflammatory effects. Finally, according to the screening principles of Q-Markers, genipin-1-β-D-gentiobioside, geniposide, and gardenoside were preliminarily identified to be the Q-Markers of GF.Conclusion: This study established an effective research strategy of “Omics Discrimination-Grey Correlation-Biological Verification,” which enabled the rapid identification of key pharmacological components of GF. These markers have provided a scientific basis for constructing a modern quality evaluation system for GF.

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Section: Methodsmentioning

confidence: 99%

An Integrated Strategy for Rapid Discovery and Identification of Quality Markers in Gardenia Fructus Using an Omics Discrimination-Grey Correlation-Biological Verification Method

et al. 2021

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“…In recent years, with the development of society, people's demand for spiritual life is increasing, the incidence of mental illness is also increasing year by year, and the incidence is mostly concentrated in big cities [ 11 – 13 ]. Modern medicine has a huge amount of complex data; in many cases, it is difficult to achieve a reasonable allocation of resources, which puts forward an urgent demand for the artificial intelligence of modern medicine and also brings great pressure to the development of the medical industry [ 14 – 16 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Construction and Drug Evaluation Based on Convolutional Neural Network System Optimized by Grey Correlation Analysis

Huang

2021

Computational Intelligence and Neuroscience

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Incidence rate of mental illness is increasing year by year with the development of city. The amount of modern medical data is huge and complex. In many cases, it is difficult to realize the rational allocation of resources, which puts forward an urgent demand for the artificial intelligence of modern medicine and brings great pressure to the development of the medical industry. The purpose of this study is to develop and construct a grey correlation analysis and related drug evaluation system of mental diseases based on deep convolution neural network. The establishment of the system can effectively improve the automation and intelligence of modern psychiatric treatment process. In this article, the grey correlation analysis of patient data is carried out, and then, the optimized deep convolution neural network is constructed. Combined with the medical knowledge base, the analysis of disease results is realized, and on this basis, the efficacy of related drugs in the treatment of mental diseases is evaluated. The results show that the advantage of the deep convolution neural network system is to effectively improve the induction rate. What’s more, compared with other algorithms, this algorithm has higher accuracy and efficiency. It improves the comprehensiveness and informatization of disease screening methods, improves the accuracy of screening, reduces the consumption of doctors’ human resources, and provides a theoretical basis for the digitization of the medical industry in the future.

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“…Future works can utilize our proposed TFNNs-MABAC method to risk assessment for MCGDM cases, such as [45][46][47][48][49][50], many other uncertain and fuzzy circumstances [51][52][53][54][55][56][57][58][59][60]. The neutrosophic sets, such as [61][62][63][64][65][66][67][68][69], can be many possible chances to improve our proposed MABAC method for further studies.…”

Section: Discussionmentioning

confidence: 99%

An Extended MABAC Method Based on Triangular Fuzzy Neutrosophic Numbers for Multiple-Criteria Group Decision Making Problems

Irvanizam

Zuhra

et al. 2020

Axioms

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In this manuscript, we extend the traditional multi-attributive border approximation area comparison (MABAC) method for the multiple-criteria group decision-making (MCGDM) with triangular fuzzy neutrosophic numbers (TFNNs) to propose the TFNNs-MABAC method. In the proposed method, we utilize the TFNNs to express the values of criteria for each alternative in MCGDM problems. First, we briefly acquaint the basic concept of TFNNs and describe its corresponding some operation laws, the functions of score and accuracy, and the normalized hamming distance. We then review two aggregation operators of TFNNs. Afterward, we combine the traditional MABAC method with the triangular fuzzy neutrosophic evaluation and provide a sequence of calculation procedures of the TFNNs-MABAC method. After comparing it with some TFNNs aggregation operators and another method, the results showed that our extended MABAC method can not only effectively handle the conflicting attributes, but also practically deal with incomplete and indeterminate information in the MCGDM problem. Therefore, the extended MABAC method is more effective, conformable, and reasonable. Finally, an investment selection problem is demonstrated as a practice to verify the reasonability of our MABAC method.

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A New Multi-Attribute Emergency Decision-Making Algorithm Based on Intuitionistic Fuzzy Cross-Entropy and Comprehensive Grey Correlation Analysis

Cited by 22 publications

References 40 publications

An Integrated Strategy for Rapid Discovery and Identification of Quality Markers in Gardenia Fructus Using an Omics Discrimination-Grey Correlation-Biological Verification Method

An Integrated Strategy for Rapid Discovery and Identification of Quality Markers in Gardenia Fructus Using an Omics Discrimination-Grey Correlation-Biological Verification Method

[Retracted] Construction and Drug Evaluation Based on Convolutional Neural Network System Optimized by Grey Correlation Analysis

An Extended MABAC Method Based on Triangular Fuzzy Neutrosophic Numbers for Multiple-Criteria Group Decision Making Problems

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