Comparison between ANN and Multiple Linear Regression Models for Prediction of Warranty Cost

Darmawan, Mohd Faaizie; Jamahir, Nur Izzati; Saedudin, Rd. Rohmat; Kasim, Shahreen

doi:10.30880/ijie.2018.10.06.027

Cited by 5 publications

(3 citation statements)

References 8 publications

(12 reference statements)

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“…In the field of modeling and classification framework, there are many studies that use the Neuro-Fuzzy Approach and. The models have been developed for monthly precipitation forecasts using ANFIS [15][16][17][18][19][20][21].…”

Section: Resultsmentioning

confidence: 99%

Rainfall-runoff modelling using adaptive neuro-fuzzy inference system

Razali

Ghani

Hisham

et al. 2020

IJEECS

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<span lang="EN-GB">This paper discusses the working mechanism of ANFIS, the flow of research, the implementation and evaluation of ANFIS models, and discusses the pros and cons of each option of input parameters applied, in order to solve the problem of rainfall-runoff forecasting. The rainfall-runoff modelling considers time-series data of rainfall amount (in mm) and water discharge amount (in m<sup>3</sup>/s). For model parameters, the models apply three triangle membership functions for each input. Meanwhile, the accuracy of the data is measured using the Root Mean Square Error (RMSE). Models with good performance in training have low values of RMSE. Hence, the 4-input model data is the best model to measure prediction accurately with the value of RMSE as 22.157. It is proven that ANFIS has the potential to be used for flood forecasting generally, or rainfall-runoff modelling specifically.</span>

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Section: Resultsmentioning

confidence: 99%

Rainfall-runoff modelling using adaptive neuro-fuzzy inference system

Razali

Ghani

Hisham

et al. 2020

IJEECS

Self Cite

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“…Multiple Linear Regression (MLR) approaches is the basic and simple method for data processing in analytical data [19]. MLR is a powerful statistical tool finding relationships between one dependent and multiple independent variables [20][21][22]. Several studies shows that four kinematic variables were high enough to result a good predictive performance of a regression model [6,23].…”

Section: Performance Of Assessmentmentioning

confidence: 99%

Non-motorised Rehabilitation Device for Performance Assessment in Upper Limb Stroke Rehabilitation: A Pilot Study

Mazlan

Rahman

KaderIbrahim

et al. 2022

IJIE

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Stroke patients with upper limb disability restricted to carry out their activities of daily living. The patient needs a motivation to recover from a stroke and the patient also needs to go through a rehabilitation process at the same time. The conventional rehabilitation process scoring systems are always subjective, lack reliability and relies heavily on the ability of the trained physiotherapist that providing only rough estimates on motor function. On the other hand, robot-based assessments are objective, repeatable, and could potentially reduce the assessment time. Therefore, a simple non-motorized device was developed as a tool to objectively assess hand function of stroke patients. This study was carried out to investigate the suitability of using thedeveloped device with stroke patient populations and to evaluate the performance of clinical scores prediction of the stroke patients. A total of five patients with upper limb disability following stroke consented to take part in this study. Twelve predictive variables were investigated, relating to the total movement time, velocity, strategy, accuracy, and smoothness from three robotic assessment modules which are Draw I, Draw Diamond and Draw Circle. The hardware for measuring elbow angle hasbeen developed to measurethe shoulder movement performedby patient during the assessment process. In addition to that, the shoulder movement calculation method has been proposed and validated.The findings indicatethat the performance of prediction for all assessment modules hasbeen increased after implementing the shoulder movement calculation. It is recommended this calculation method to be used in conjunction with kinematic variables to carry out the data acquisition process in the future for improvement of effectiveness and accuracy of the robotic assessment.

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“…Multiple Linear Regression (MLR) approaches is the basic and simple method for experimental and data processing in analytical data [30]. MLR is a powerful statistical tool finding relationships between one dependent and multiple independent variables [31][32][33]. In MLR, the dependent variables y is linearly correlated to multiple independent variables x1, x2, …, xn.…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

Multiple Linear Regression in Predicting Motor Assessment Scale of Stroke Patients

Mazlan

Rahman

Ibrahim³

et al. 2021

IJIE

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The Multiple Linear Regression (MLR) is a predictive model that was commonly used to predict the clinical score of stroke patients. However, the performance of the predictive model slightly depends on the method of feature selection on the data as input predictor to the model. Therefore, appropriate feature selection method needs to be investigated in order to give an optimum performance of the prediction. This paper aims (i) to develop predictive model for Motor Assessment Scale (MAS) prediction of stroke patients, (ii) to establish relationship between kinematic variables and MAS score using a predictive model, (iii) to evaluate the prediction performance of a predictive model based on root mean squared error (RMSE) and coefficient of determination R2. Three types of feature selection methods involve in this study which are the combination of all kinematic variables, the combination of the best four or less kinematic variables, and the combination of kinematic variables based on p < 0.05. The prediction performance of MLR model between two assessment devices (iRest and ReHAD) has been compared. As the result, MLR model for ReHAD with the combination of kinematic variables that has p < 0.05 as input predictor has the best performance with Draw I (RMSEte = 1.9228, R2 = 0.8623), Draw Diamond (RMSEte = 2.6136, R2 = 0.7477), and Draw Circle (RMSEte = 2.1756, R2 = 0.8268). These finding suggest that the relationship between kinematic variables and MAS score of stoke patients is strong, and the MLR model with feature selection of kinematic variables that has p < 0.05 is able to predict the MAS score of stroke patients using the kinematic variables extracted from the assessment device.

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Comparison between ANN and Multiple Linear Regression Models for Prediction of Warranty Cost

Cited by 5 publications

References 8 publications

Rainfall-runoff modelling using adaptive neuro-fuzzy inference system

Rainfall-runoff modelling using adaptive neuro-fuzzy inference system

Non-motorised Rehabilitation Device for Performance Assessment in Upper Limb Stroke Rehabilitation: A Pilot Study

Multiple Linear Regression in Predicting Motor Assessment Scale of Stroke Patients

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