Grey relation between main meteorological factors and mortality

Chen, Xiao; Pei, De‐Sheng; Li, Liping

doi:10.1108/gs-11-2018-0061

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…[ [75] , [76] , [77] , [78] , [79] , [80] ]. Existing researches usually reflect WC function/capacity in terms of WC amount, and none have verified the evaluation results [ [10] , [48] , [70] , [81] , [82] , [83] ]. The reason may be the regional differences in relevant studies and the lack of validation data.…”

Section: Discussionmentioning

confidence: 99%

Evaluation and prediction of water conservation of the Yellow river basin in Sichuan Province, China, based on Google Earth Engine and CA-Markov

Yang

Dai

Lü

et al. 2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

Evaluation and prediction of water conservation of the Yellow river basin in Sichuan Province, China, based on Google Earth Engine and CA-Markov

Yang

Dai

Lü

et al. 2023

Heliyon

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“…where ρ represents the resolution coefficient, the value range is 0-1, usually set to 0.5 [27]. The purpose of introducing ρ is to convert the grey correlation coefficient to a suitable numerical scale so that the difference is more obvious.…”

Section: Grey Correlation Degreementioning

confidence: 99%

Indoor unknown radio transmitter localization using improved RSSD and grey correlation degree

Zhang,

Xu,

Gao

et al. 2024

Meas. Sci. Technol.

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Accurate location of unknown radio transmitter (URT) is the key to secure wireless communication. Since the fingerprint positioning methods based on received signal strength difference (RSSD) can adapt to the diversity of transmitting power and frequency, RSSD has become a popular scheme for locating the unknown radio transmitter. However, the RSSD is obtained by subtracting the RSS from two different access points (APs), so the interference of noise on the RSS is inherited and amplified by the RSSD. Besides, the need for more APs to ensure positioning accuracy leads to an increase in hardware costs. In this paper, a RSSD-based fuzzy weight grey correlation degree positioning algorithm, called FUZZY-GREY, is proposed to reduce the interference of noise, save AP hardware cost and improve the positioning accuracy. Firstly, online RSSD vector is improved by using fuzzy weight to reduce the noise interference. Secondly, the RSSD-based grey correlation coefficient is designed to calculate the correlation degree of the corresponding RSSD and ensure data integrity. Finally, a RSSD-based grey correlation degree scheme combining with fuzzy weight is proposed to select optimal reference points (RPs). Simulation and experimental results show that the proposed algorithm has better positioning performance than weighted k-nearest neighbor (WKNN), maximum correlation coefficient estimation (MCORE), Naive Bayes and support vector machine (SVM) in the case of different selected K numbers, grid distances, noise levels and AP numbers.

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Hospital readmission prediction based on improved feature selection using grey relational analysis and LASSO

Miswan

Chan

2021

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PurposeThis paper develops a robust hospital readmission prediction framework by combining the feature selection algorithm and machine learning (ML) classifiers. The improved feature selection is proposed by considering the uncertainty in patient's attributes that leads to the output variable.Design/methodology/approachFirst, data preprocessing is conducted which includes how raw data is managed. Second, the impactful features are selected through feature selection process. It started with calculating the relational grade of each patient towards readmission using grey relational analysis (GRA) and the grade is used as the target values for feature selection. Then, the influenced features are selected using the Least Absolute Shrinkage and Selection Operator (LASSO) method. This proposed method is termed as Grey-LASSO feature selection. The final task is the readmission prediction using ML classifiers.FindingsThe proposed method offered good performances with a minimum feature subset up to 54–65% discarded features. Multi-Layer Perceptron with Grey-LASSO gave the best performance.Research limitations/implicationsThe performance of Grey-LASSO is justified in two readmission datasets. Further research is required to examine the generalisability to other datasets.Originality/valueIn designing the feature selection algorithm, the selection on influenced input variables was based on the integration of GRA and LASSO. Specifically, GRA is a part of the grey system theory, which was employed to analyse the relation between systems under uncertain conditions. The LASSO approach was adopted due to its ability for sparse data representation.

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Grey relation between main meteorological factors and mortality

Cited by 6 publications

References 22 publications

Evaluation and prediction of water conservation of the Yellow river basin in Sichuan Province, China, based on Google Earth Engine and CA-Markov

Evaluation and prediction of water conservation of the Yellow river basin in Sichuan Province, China, based on Google Earth Engine and CA-Markov

Indoor unknown radio transmitter localization using improved RSSD and grey correlation degree

Hospital readmission prediction based on improved feature selection using grey relational analysis and LASSO

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