Link Load Prediction using Support Vector Regression and Optimization

Priyadarshin, Debashree; Acharya, Milu; Mishra, Ambika Prasad

doi:10.5120/2966-3964

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…Therefore, a delay is assumed to be minimised. It has been reported that the parameters of SVR, that is epsilon (e), cost or error penalty (C) and the kernel type influence prediction performance (Priyadarshini et al, 2011;Ustuner et al, 2015). Additionally, the sensitivity index can be used to determine the degree to which the independent variables impact the dependent variable (Hamby, 1994).…”

Section: Model Development and Performancementioning

confidence: 99%

Application of machine learning in predicting construction project profit in Ghana using Support Vector Regression Algorithm (SVRA)

Adinyira

Adjei

Agyekum

et al. 2021

ECAM

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PurposeKnowledge of the effect of various cash-flow factors on expected project profit is important to effectively manage productivity on construction projects. This study was conducted to develop and test the sensitivity of a Machine Learning Support Vector Regression Algorithm (SVRA) to predict construction project profit in Ghana.Design/methodology/approachThe study relied on data from 150 institutional projects executed within the past five years (2014–2018) in developing the model. Eighty percent (80%) of the data from the 150 projects was used at hyperparameter selection and final training phases of the model development and the remaining 20% for model testing. Using MATLAB for Support Vector Regression, the parameters available for tuning were the epsilon values, the kernel scale, the box constraint and standardisations. The sensitivity index was computed to determine the degree to which the independent variables impact the dependent variable.FindingsThe developed model's predictions perfectly fitted the data and explained all the variability of the response data around its mean. Average predictive accuracy of 73.66% was achieved with all the variables on the different projects in validation. The developed SVR model was sensitive to labour and loan.Originality/valueThe developed SVRA combines variation, defective works and labour with other financial constraints, which have been the variables used in previous studies. It will aid contractors in predicting profit on completion at commencement and also provide information on the effect of changes to cash-flow factors on profit.

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Section: Model Development and Performancementioning

confidence: 99%

Application of machine learning in predicting construction project profit in Ghana using Support Vector Regression Algorithm (SVRA)

Adinyira

Adjei

Agyekum

et al. 2021

ECAM

View full text Add to dashboard Cite

show abstract

“…The model generated by SVR only depends on a subset of the training data because the cost function for building the model ignores the training data that is close to the model prediction. SVR has been successfully applied in various fields such as bioinformatics, engineering and financial research [36].…”

Section: Support Vector Regression(svr)mentioning

confidence: 99%

Complexity and Non-Linearity of Cardiovascular Risk Factors in Older Patients With Multimorbidity and Reduced Renal Function

Majnarić¹,

Volarić²,

Yıldırım³

et al. 2020

JICOA

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The aim of this study was to reveal the complex pathophysiology network that may underly associations between chronic kidney disease (CKD), defined with mildly to moderately decreased renal function, and increased CV risk. For this purpose, we used a set of parameters indicating biochemical disorders, known to be associated with CKD. This set of parameters was taken from the larger dataset, where clinical characteristics of older patients with multimorbidity (the existence of two or more chronic diseases at the same person) and mildly to moderately decreased renal function, have been described with multiple parameters. On the selected set of parameters, we applied Machine Learning (ML) methods, to demonstrate relationships between the parameters. We used SMOreg algorithm for developing regression model. At first, we applied the SMOreg algorithm on the dataset to predict C-reactive protein (CRP) and then we used same algorithm to discover pairwise nonlinear relationships between variables such as Age-fglu, Chol-HTC and HB-FE, Age-Homcis, Clear-Homcis, Homcis-TG, CRP-TG. The assessment of non-linear relationships among multiple parameters indicating confounding factors of renal function decline, in older people with multimorbidity, has revealed the close associations between insulin resistance and serum albumin and homocystein levels. Several hypotheses are arising from these study with the potential to facilitate research on the concept of the reverse epidemiology. Although this analytical approach is far from being sufficient to provide the full understanding of the relationships between cardiovascular risk factors and decreased renal function, in older population, by means of the reverse epidemiology, this study emphasizes a need for more integrated and dynamical approaches, when assessing these factors.

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CCI-based link quality estimation mechanism for wireless sensor networks under non-perceived packet loss

Shu

Tao

Liu

et al. 2013

The Journal of China Universities of Posts and Telecommunicatio

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Link Load Prediction using Support Vector Regression and Optimization

Cited by 3 publications

References 20 publications

Application of machine learning in predicting construction project profit in Ghana using Support Vector Regression Algorithm (SVRA)

Application of machine learning in predicting construction project profit in Ghana using Support Vector Regression Algorithm (SVRA)

Complexity and Non-Linearity of Cardiovascular Risk Factors in Older Patients With Multimorbidity and Reduced Renal Function

CCI-based link quality estimation mechanism for wireless sensor networks under non-perceived packet loss

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