ORCID Profile
0000-0002-4629-0483
Current Organisations
University of Malaya
,
Curtin University
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Publisher: IEEE
Date: 08-2016
Publisher: Springer Singapore
Date: 2016
Publisher: Zenodo
Date: 2012
Publisher: Springer Science and Business Media LLC
Date: 05-02-2014
Publisher: World Scientific Pub Co Pte Ltd
Date: 08-11-2021
DOI: 10.1142/S0217590821500740
Abstract: This paper uses systematic panel data methods to scrutinize the impact of China’s foreign direct investment (FDI) on economic growth in eight Association of Southeast Asian Nations (ASEAN) countries from 2004 to 2018. The findings indicate a statistically significant causal association between these countries’ economic growth and Chinese investment, which shows that China’s FDI is not a cause but rather a result of the economic expansion. Specifically, the results show that there was a causal chain running from fixed capital to Chinese FDI, through trade openness, in the relatively wealthier ASEAN countries also, there was a causal chain running from economic growth to Chinese FDI, through trade openness, in relatively poorer ASEAN countries.
Publisher: IEEE
Date: 05-2015
Publisher: Author(s)
Date: 2017
DOI: 10.1063/1.4980864
Publisher: Public Library of Science (PLoS)
Date: 12-05-2023
DOI: 10.1371/JOURNAL.PONE.0285407
Abstract: Improving forecasting particularly time series forecasting accuracy, efficiency and precisely become crucial for the authorities to forecast, monitor, and prevent the COVID-19 cases so that its spread can be controlled more effectively. However, the results obtained from prediction models are inaccurate, imprecise as well as inefficient due to linear and non-linear patterns exist in the data set, respectively. Therefore, to produce more accurate and efficient COVID-19 prediction value that is closer to the true COVID-19 value, a hybrid approach has been implemented. Thus, aims of this study is (1) to propose a hybrid ARIMA-SVM model to produce better forecasting results. (2) to investigate in terms of the performance of the proposed models and percentage improvement against ARIMA and SVM models. statistical measurements such as MSE, RMSE, MAE, and MAPE then conducted to verify that the proposed models are better than ARIMA and SVM models. Empirical results with three real datasets of well-known cases of COVID-19 in Malaysia show that, compared to the ARIMA and SVM models, the proposed model generates the smallest MSE, RMSE, MAE and MAPE values for the training and testing datasets, means that the predicted value from the proposed model is closer to the actual value. These results prove that the proposed model can generate estimated values more accurately and efficiently. As compared to ARIMA and SVM, our proposed models perform much better in terms of error reduction percentages for all datasets. This is demonstrated by the maximum scores of 73.12%, 74.6%, 90.38%, and 68.99% in the MAE, MAPE, MSE, and RMSE, respectively. Therefore, the proposed model can be the best and effective way to improve prediction performance with a higher level of accuracy and efficiency in predicting cases of COVID-19.
Publisher: MDPI AG
Date: 17-08-2021
DOI: 10.3390/SYM13081511
Abstract: The mathematical theory behind the porous medium type equation is well developed and produces many applications to the real world. The research and development of the fractional nonlinear porous medium models also progressed significantly in recent years. An efficient numerical method to solve porous medium models needs to be investigated so that the symmetry of the designed method can be extended to future fractional porous medium models. This paper contributes a new numerical method called Newton-Modified Weighted Arithmetic Mean (Newton-MOWAM). The solution of the porous medium type equation is approximated by using a finite difference method. Then, the Newton method is applied as a linearization approach to solving the system of nonlinear equations. As the system to be solved is large, high computational complexity is regulated by the MOWAM iterative method. Newton-MOWAM is formulated technically based on the matrix structure of the system. Some initial-boundary value problems with a different type of nonlinear diffusion term are presented. As a result, the Newton-MOWAM showed a significant improvement in the computation efficiency compared to the developed standard Weighted Arithmetic Mean iterative method. The analysis of efficiency, measured by the reduced number of iterations and computation time, is reported along with the convergence analysis.
Publisher: Science Publications
Date: 06-2010
Publisher: MDPI AG
Date: 30-03-2023
Abstract: The present work is highly interested in examining the transport phenomena of the thin Cross hybrid nanofluid film flow over a continuously stretching surface. The proposed thin film flow study elucidates the film extrusion process, which is prominent in the packaging industry. With the intention of improvising the quality of the coating process, the thermocapillarity and injection effects have been probed in the present model. A suitable similarity transformation and the MATLAB software aid in producing accurate numerical solutions. The accumulated numerical results indicate that an increment in the hybrid nanofluid viscosity and surface tension intensity reduces the wall shear stress past the permeable stretching sheet and improves the heat transfer rate. Remarkably, negative film thickness has been identified when the unsteadiness parameter is greater than or equal to 0.9 while the thermocapillarity parameter falls within the range of 0 and 0.6.
Publisher: Penerbit Universiti Kebangsaan Malaysia (UKM Press)
Date: 31-05-2023
DOI: 10.17576/JSM-2023-5205-20
Abstract: Outliers are some observation points outside the usual pattern of the other observations. It is essential to detect outliers as anomalous observations can affect the inference made in the analysis. In this study, we propose an efficient clustering procedure to identify multiple outliers in the linear functional relationship model using the single linkage algorithm with the Euclidean distance as the similarity measure. A new robust cut-off point using the median and median absolute deviation for the tree heights to classify the potential outliers are proposed in this study. Experimental results from the simulation study suggest our proposed method is able to identify the presence of multiple outliers with very small probability of sw ing and masking. Application in real data also shows that the proposed clustering method for this linear functional relationship model successfully detects the outliers, thus suggesting the method's practicality in real-world problems.
Publisher: Springer Science and Business Media LLC
Date: 10-06-2012
DOI: 10.1007/S00128-012-0698-4
Abstract: A baseline study was carried out to assess the metal concentrations and microbial contamination at selected Lake waters in and around Miri City, East Malaysia. Sixteen surface water s les were collected at specific Lakes in the environs of major settlement areas and recreational centers in Miri City. The Physico-chemical parameters [pH, Electrical Conductivity (EC) and Dissolved Oxygen (DO)], metals (Fe, Mn, Cu, Cd, Ni and Zn) and Escherichia coli (E. coli) were analysed. The concentrations of Fe, Mn and Ni have been found to be above the permissible limits of drinking water quality standards. The metals data have also been used for the calculation of heavy metal pollution index. Higher values of E. coli indicate microbial contamination in the Lake waters.
Publisher: Springer Science and Business Media LLC
Date: 06-08-2013
Publisher: Penerbit Universiti Kebangsaan Malaysia (UKM Press)
Date: 2015
Publisher: AIP Publishing LLC
Date: 2014
DOI: 10.1063/1.4898456
Publisher: IEEE
Date: 12-2015
DOI: 10.1109/AIMS.2015.24
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 15-04-2016
Publisher: AIP Publishing LLC
Date: 2014
DOI: 10.1063/1.4887582
Publisher: Hikari, Ltd.
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 02-03-2021
DOI: 10.1186/S13662-021-03310-2
Abstract: This paper will solve one of the fractional mathematical physics models, a one-dimensional time-fractional differential equation, by utilizing the second-order quarter-sweep finite-difference scheme and the preconditioned accelerated over-relaxation method. The proposed numerical method offers an efficient solution to the time-fractional differential equation by applying the computational complexity reduction approach by the quarter-sweep technique. The finite-difference approximation equation will be formulated based on the Caputo’s time-fractional derivative and quarter-sweep central difference in space. The developed approximation equation generates a linear system on a large scale and has sparse coefficients. With the quarter-sweep technique and the preconditioned iterative method, computing the time-fractional differential equation solutions can be more efficient in terms of the number of iterations and computation time. The quarter-sweep computes a quarter of the total mesh points using the preconditioned iterative method while maintaining the solutions’ accuracy. A numerical ex le will demonstrate the efficiency of the proposed quarter-sweep preconditioned accelerated over-relaxation method against the half-sweep preconditioned accelerated over-relaxation, and the full-sweep preconditioned accelerated over-relaxation methods. The numerical finding showed that the quarter-sweep finite difference scheme and preconditioned accelerated over-relaxation method can serve as an efficient numerical method to solve fractional differential equations.
Publisher: Elsevier BV
Date: 2017
Publisher: AIP Publishing LLC
Date: 2014
DOI: 10.1063/1.4887566
Publisher: MDPI AG
Date: 15-03-2023
DOI: 10.3390/DIAGNOSTICS13061121
Abstract: Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease. However, the results obtained from the predictive models are imprecise and inefficient because the dataset contains linear and non-linear patterns, respectively. Linear models such as autoregressive integrated moving average cannot be used effectively to predict complex time series, so nonlinear approaches are better suited for such a purpose. Therefore, to achieve a more accurate and efficient predictive value of COVID-19 that is closer to the true value of COVID-19, a hybrid approach was implemented. Therefore, the objectives of this study are twofold. The first objective is to propose intelligence-based prediction methods to achieve better prediction results called autoregressive integrated moving average–least-squares support vector machine. The second objective is to investigate the performance of these proposed models by comparing them with the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine. Our investigation is based on three COVID-19 real datasets, i.e., daily new cases data, daily new death cases data, and daily new recovered cases data. Then, statistical measures such as mean square error, root mean square error, mean absolute error, and mean absolute percentage error were performed to verify that the proposed models are better than the autoregressive integrated moving average, support vector machine model, least-squares support vector machine, and autoregressive integrated moving average–support vector machine. Empirical results using three recent datasets of known the Coronavirus Disease-19 cases in Malaysia show that the proposed model generates the smallest mean square error, root mean square error, mean absolute error, and mean absolute percentage error values for training and testing datasets compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine models. This means that the predicted value of the proposed model is closer to the true value. These results demonstrate that the proposed model can generate estimates more accurately and efficiently. Compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine models, our proposed models perform much better in terms of percent error reduction for both training and testing all datasets. Therefore, the proposed model is possibly the most efficient and effective way to improve prediction for future pandemic performance with a higher level of accuracy and efficiency.
Publisher: MDPI AG
Date: 17-05-2022
DOI: 10.3390/SYM14051017
Abstract: Initially, the concept of the complexity reduction approach was applied to solve symmetry algebraic systems that were generated from the discretization of the partial differential equations. Consequently, in this paper, the effectiveness of a complexity reduction approach based on half- and quarter-sweep iteration concepts for solving linear Fredholm integral equations of the second kind is investigated. Half- and quarter-sweep iterative methods are applied to solve dense linear systems generated from the discretization of the second kind of linear Fredholm integral equations using a repeated modified trapezoidal (RMT) scheme. The formulation and implementation of the proposed methods are presented. In addition, computational complexity analysis and numerical results of test ex les are also included to verify the performance of the proposed methods.
Publisher: Springer Science and Business Media LLC
Date: 28-03-2012
Publisher: AIP
Date: 2013
DOI: 10.1063/1.4801120
Publisher: Author(s)
Date: 2016
DOI: 10.1063/1.4954529
Publisher: AIP
Date: 2013
DOI: 10.1063/1.4823934
Publisher: Hikari, Ltd.
Date: 2015
Publisher: IEEE
Date: 2016
DOI: 10.1109/ISMS.2016.84
No related grants have been discovered for Elayaraja Aruchunan.