Applied Mathematics and Computational Intelligence (AMCI)
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AMCI is peer-reviewed and published as an online open-access journal as well as in printed copy. The journal welcomes original and significant contributions in the area of applied mathematics and computational intelligence. It emphasises on empirical or theoretical foundations, or their applications to any field of investigation where mathematics and computational intelligence techniques are used. The journal is designed to meet the needs of a wide range of mathematicians, computer scientists and engineers in academic or industrial research.
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PublicationDistance Constrained Capacitated Vehicle Touting Problem For The Abuja Post Office Using Lexisearch Algorithm(Universiti Malaysia Perlis, 2023-11-10)Transportation problems are generally modelled as vehicle routing problems (VRPs) since they consist of minimizing the overall cost while satisfying routing constraints and customers' orderings. In this paper, the mail delivery of the Nigerian Postal Services (NIPOST) is modeled as a distance- constrained capacitated variant (DCVRP) in order to address the problem of delay in mail delivery occurring frequently experienced in NIPOST. The Lexi-search algorithm was applied and implemented on the spreadsheet solver to solve the capacitated vehicle routing problem of Abuja Post Office. The algorithm was tested on Christofides Benchmark and found to perform well with computational analyses coded in c++ and statistical analyses of goodness fit performed using R Software. The problem has been formulated as a mixed integer programming problem based on a modification of the Tompkin’s et al., model. Appropriate data was obtained from Abuja Post Office where the data is used to come up with the distance matrix. The annual cost savings for NIPOST using three vehicles is N1,950,054.66, N1,708,127.73 and N1,140,148.50 for N142, N163 and N213 per litre respectively. The annual cost savings for four vehicles, is N1,494,466.25, and therefore the purchse of a fourth vehicle will reduce delay in mail delivery though it might cost them more to aquire a new bus as already indicated. In the long run however, revenue will increase because of high patronage.
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PublicationUnique Solution of an Infinite 2-System Model of First Order Ordinary Differential Equation(Universiti Malaysia Perlis, 2023-04)This work is to solve an infinite 2-system model of first order ordinary differential equations. The system is in Hilbert space l2 with the coefficients are any positive real numbers. The system is rewritten as a system in the form of matrix equations and it is first studied in ℝ2 where its solution is obtained and a fundamental matrix is constructed. The results are carried out to solve the infinite 2-system in Hilbert space l2. The control functions satisfy integral constraint and are elements of the space of square integrable function in l2. The existence and uniqueness of the solution of the system in Hilbert space l2 on an interval time [0, T] for a sufficiently large T is then proven.
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PublicationMultiple Linear Regression for Academic Achievements for Public Universities in Malaysia(Universiti Malaysia Perlis, 2022-12)The relationship between socioeconomic status (SES) and academic achievement has been thoroughly studied in earlier educational research. Explanatory research design was employed to assess the effects of SES on the academic achievement of students for public universities students. A survey self-administered questionnaire distributed to 300 students of UiTM Kota Bharu. Multiple linear regression (MLR) is one of the most popular techniques used in analyzing and predicting academic performance. Hence, the aims of this study to evaluate MLR in finding the relationship between family SES and academic performance. It was found that parent’s educational level, parent’s employment, parent’s income, home environment were significant factors that affected students’ performance.
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PublicationModelling and Forecasting Gold Price Return and Its Volatility(Universiti Malaysia Perlis, 2022-12)n the financial industry, forecasting gold price return and its volatility are crucial. Even tiny improvements in prediction performance can add up to a significant sum of money. One strategy for anticipating gold price volatility is the GARCH family of models. The GARCH process provides a more realistic background for estimating financial instrument values and rates than other models. The aim of this research is to model the volatility and price of gold via GARCH(1,1), EGARCH(1,1) and TGARCH(1,1) models. The data was employed from 4th January 2016 until 29th October 2021 retrieved from Yahoo Finance website. The data was converted into return price to make it stationary. The performance of the estimated models was compared by using information criterion. The best model is the one that has the lowest values of information criterion. TGARCH(1,1) is outperformed other proposed models where it has an ability to capture the bad and good news that exist in the data series. The best model is used to forecast the volatility and return of gold price. The return is expected to be constant with high risk for the next 5 days from the point origin in this study.
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PublicationModeling of prediction system : An application of the nearest neighbor approach to chaotic data(Universiti Malaysia Perlis, 2013-07)This paper is about modeling of chaotic systems via nearest neighbor approach. This approach holds the principle that future data can be predicted using past data information. Here, all the past data known as neighbors. There are various prediction models that have been developed through this approach. In this paper, the zeroth-order approximation method (ZOAM) and improved ZOAM, namely the k-nearest neighbor approximation (KNNAM) and weighted distance approximation method (WDAM) were used. In ZOAM, only one nearest neighbor is used to predict future data while KNNAM uses more than one nearest neighbor and WDAM add the distance element for prediction process. These models were used to predict one of the chaotic data, Logistic map. 3008 Logistic map data has been produced, in which the first 3000 data were used to train the model while the rest is used to test the performance of the model. Correlation coefficient and average absolute error are used to view the performance of the model. The prediction results by the three models are in excellent agreement with the real data. This shows that the nearest neighbor approach works well to predict the chaotic data. Unfortunately, increasing the number of nearest neighbors from ZOAM to KNNAM not managed to improve prediction performance. However, the added element of the distance is a great idea for improving prediction performance. Overall, WDAM is the best model to predict the chaotic data compared to ZOAM and KNNAM.
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