Comparing Methods for Lee–Carter Parameter’s Estimation for Predicting Hospital Admission Rates

Yap, Siti Zulaikha Zulkarnain; Zahari, Siti Meriam; Derasit, Zuraidah; Shariff, S. Sarifah Radiah

doi:10.1007/978-981-10-8471-3_36

Cited by 1 publication

(1 citation statement)

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“…In the second stage, the within sample statistics is used to estimate the model using three built in algorithms, LM, CGD and QP. The best estimation approach is selected based on the outcomes of comparing their error measures performances [22]. For this purpose, RMSE and MAPE are used [23][24].…”

Section: Model Validationmentioning

confidence: 99%

Application of artificial neural network to predict amount of carried weight of cargo train in rail transportation system

Zubir

Shariff

Zahari

2020

IJ-AI

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<span lang="EN-US">Derailments of cargo have frequently occurred in Malaysian train services during the last decade. Many factors contribute to this incident, especially its total amount of carried weight. It is found that severe derailments cause damage to both lives and properties every year. If the amount of carried weight of cargo train could be accurately forecasted in advance, then its detrimental effect could be greatly minimized. This paper presents the application of Artificial Neural Network (ANN) to predict the amount of carried weight of cargo train, with KTMB used as the study case. As there are many types of cargo being carried by KTMB, this study focuses only on cement that being carried in twelve (12) different routes. In this study, Artificial Neural Network (ANN) has been incorporated for developing a predictive model with three (3) different training algorithms, Levenberg-Marquardt (LM), Quick Propagation (QP) and Conjugate Gradient Descent (CGD). The best training algorithm is selected to predict the amount of carried weight by comparing the error measures of all the training algorithm which are Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The obtained results indicated that the ANN technique is suitable for predicting the amount of carried weight.</span>

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Section: Model Validationmentioning

confidence: 99%