Analysis of mechanical properties for tea stem using grey relational analysis coupled with multiple linear regression

Du, Zhe; Hu, Yongguang; Buttar, Noman Ali

doi:10.1016/j.scienta.2019.108886

Cited by 48 publications

(33 citation statements)

References 14 publications

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“…Multiple linear regression -TOPSIS is studied by Luu, von Meding, and Mojtahedi [ 12 ] for predicting disaster from data. Similarly, the relationship between the mechanical properties of the tea stem and their impact factor has been studied by Du, Hu, and Buttar [ 13 ] to improve the picking efficiency of the tea plucking machine using MLR technique. Kadam et al.…”

Section: Discussionmentioning

confidence: 99%

Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model

Rath

Tripathy

2020

Diabetes & Metabolic Syndrome: Clinical Research & Revi

172

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Introduction and Aims The COVID-19 pandemic originated from the city of Wuhan of China has highly affected the health, socio-economic and financial matters of the different countries of the world. India is one of the countries which is affected by the disease and thousands of people on daily basis are getting infected. In this paper, an analysis of daily statistics of people affected by the disease are taken into account to predict the next days trend in the active cases in Odisha as well as India. Material and methods A valid global data set is collected from the WHO daily statistics and correlation among the total confirmed, active, deceased, positive cases are stated in this paper. Regression model such as Linear and Multiple Linear Regression techniques are applied to the data set to visualize the trend of the affected cases. Results Here a comparison of Linear Regression and Multiple Linear Regression model is performed where the score of the model tends to be 0.99 and 1.0 which indicates a strong prediction model to forecast the next coming days active cases. Using the Multiple Linear Regression model as on July month, the forecast value of 52,290 active cases are predicted towards the next month of 15th August in India and 9,358 active cases in Odisha if situation continues like this way. Conclusion These models acquired remarkable accuracy in COVID-19 recognition. A strong correlation factor determines the relationship among the dependent (active) with the independent variables (positive, deceased, recovered).

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Section: Discussionmentioning

confidence: 99%

Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model

Rath

Tripathy

2020

Diabetes & Metabolic Syndrome: Clinical Research & Revi

172

View full text Add to dashboard Cite

show abstract

“…is study used GM (1, 1) prediction model to predict the tendency of the CCDs of the tourism-air environment system for the 11 regions of the Zone, with the results shown in Table 8. In the accuracy test of the model, the estimated parameter, which is the most used to test the accuracy [68], a < 0.3, and the correlation degree r > 0.6, demonstrating that the model meets the requirement and is applicable for prediction. Moreover, most data meet the posterior error ratio C ≤ 0.8 and the small error possibility P ≥ 0.60, meaning that this model reaches a certain accuracy and can be used for prediction [68].…”

Section: Prediction Of the Ccdmentioning

confidence: 94%

“…If 0.3 < a ≤ 0.5, the data are valid to predict the shortterm trend; if a ≤ 0.3, the data are valid to predict the longand medium-term trend. is is the most used indicator to test the model's accuracy [68].…”

Section: Prediction Of the System's Ccdmentioning

confidence: 99%

Analysis and Prediction of the Coupling Coordination Relationship between Tourism and Air Environment: Yangtze River Economic Zone in China as Example

Geng

Wei

Zhang

et al. 2020

Discrete Dynamics in Nature and Society

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The results show that the comprehensive development degree (CDD) of the tourism-air environment system mainly maintains stable with fluctuation and the gap among different reaches in the Zone is declining; the coupling coordination degree’s (CCD) tendency in most regions remains similar as in the previous decade. The results illustrate that the method combing information entropy weight and the technique for order preference by similarity to an ideal solution (IEW-TOPSIS), coupling coordination degree model (CCDM), and gray GM (1, 1) prediction model is effective in evaluating the coupling coordination relationship between the subsystems of tourism and air environment and in proposing specific countermeasures for tourism development and air environment governance.

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“…To guarantee the prediction accuracy, a should be less than 0.5, which is obtained from abundant practical experiences and now has been widely accepted as the principle of the accuracy test [53]. (5) Calculate the relative error q (t) and the residual p 0 (t):…”

Section: N) (7)mentioning

confidence: 99%

Measurement and Prediction: Coupling Coordination of Finance and Air Environment

Geng

Tan

2020

Discrete Dynamics in Nature and Society

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This study finds that the comprehensive development degree (CDD) of the finance subsystem is less fluctuated than that of the air environment subsystem, and both subsystems share similarities in spatial distributions. The coupling coordination degrees (CCD) keep fluctuating with varied development directions and extents in different regions; besides, the eastern regions are higher than the western ones for the coupling coordination degrees. In the next years, the coordination degrees of the regions will have different tendencies: despite of the former fluctuation trends, regions in the coordination range will have upward trends, while those in the transition range will be likely to decline. The results are useful in proposing corresponding measures to promote the coordination development between finance and air environment.

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Analysis of mechanical properties for tea stem using grey relational analysis coupled with multiple linear regression

Cited by 48 publications

References 14 publications

Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model

Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model

Analysis and Prediction of the Coupling Coordination Relationship between Tourism and Air Environment: Yangtze River Economic Zone in China as Example

Measurement and Prediction: Coupling Coordination of Finance and Air Environment

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