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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PublicationDeveloping a Credit Scoring of the SMEs Manufacturing based on Multi Criteria Decision Making (MCDM) Algorithm(Universiti Malaysia Perlis, 2025-02-17)Credit risk is a very important risk to banks since failure of borrowers to make required payment will lead to high non-performing loans. Hence, it is necessary for banks to develop a mechanism to gauge the credit risk of its borrowers. One of the methods is credit scoring. Small and Medium Enterprises (SMEs) are the backbone of the Malaysian economy comprising 98.5% of the total business established in Malaysia. Despite their importance, access to finance is relatively limited. According to banks, lending money to SMEs are risky compared to large companies due to few factors such as less of publicly available information, young and lack of collateral. Hence, this study tried to predict the credit risk of SMEs in Malaysia by developing a credit scoring that combined financial and non-financial criteria. This study proposes a credit scoring method based on MCDM algorithm that will be able to forecast the score of the potential borrowers at a certain time by using the historic information. Result obtained is verified via the comparison with the given credit risk level provided by banks and by measuring the correlation. The correlation value is 0.88640526 indicates the high positive linear relationship.
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PublicationMitigating Overfitting in Extreme Learning Machine Classifier Through Dropout Regularization(Universiti Malaysia Perlis, 2024-02-14)Achieving optimal machine learning model performance is often hindered by the limited availability of diverse datasets, a challenge exacerbated by small sample sizes in real-world scenarios. In this study, we address this critical issue in classification tasks by integrating the Dropout technique into the Extreme Learning Machine (ELM) classifier. Our research underscores the effectiveness of Dropout-ELM in mitigating overfitting, especially when data is scarce, leading to enhanced generalization capabilities. Through extensive experiments on synthetic and real-world datasets, our findings consistently demonstrate that Dropout-ELM outperforms traditional ELM, yielding significant accuracy improvements ranging from 0.19% to 16.20%. By strategically implementing dropout during training, we promote the development of robust models that reduce reliance on specific features or neurons, resulting in increased adaptability and resilience across diverse datasets. Ultimately, Dropout-ELM emerges as a potent tool to counter overfitting and bolster the performance of ELM-based classifiers, particularly in scenarios with limited data. Its established efficacy positions it as a valuable asset for enhancing the reliability and generalization of machine learning models, providing a robust solution to the challenges posed by constrained training data.
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PublicationParticle Swarm Optimization for Directional Overcurrent Relay Coordination with Distributed Generation(Universiti Malaysia Perlis, 2024-02)The Directional Overcurrent Relays (DOCRs) Coordination with Distributed Generation (DG) optimization problem is addressed in this study using the optimization method Particle Swarm Optimization (PSO). Changes in fault current, bus voltages, power flow, and reliability may result from DG integration. Thus, it might have an impact on the current protection coordination system. The formulation is built on a Mixed Integer Non-Linear Programming (MINLP) problem to address this DOCR issue. MATLAB was used to validate the technique on the IEEE-14 bus system, and Electrical Test Transient Analyzer Programming (ETAP) version 2021 software was used to model the test system. According to the simulation results, the suggested PSO with DG for Case 2 has reduced power loss by 6.24% and relay operating time by 46.79% when compared to PSO without the presence of DG.
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PublicationVisualization of monotonic shaped data by a rational cubic Ball(Universiti Malaysia Perlis, 2025-02-17)This paper discusses the monotonicity-preserving curve interpolation of 2D monotone data. A piecewise rational cubic Ball function in form of (cubic numerator /cubic denominator), with four shape parameters is presented. The rational cubic Ball spline has four shape parameters in its descriptions where two of them are constrained shape parameters and remaining two of them provide the freedom to user to easily control the shape of the curve by simply changing their values. The sufficient data dependent conditions are derived for two shape parameters to insure the monotonicity everywhere. Numerical results show that the Ball interpolation scheme is quite efficient and well tested for monotone data.
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PublicationSatellite attitude estimation in simulated non-Gaussian white noise using Particle Filter and Extended Kalman Filter(Universiti Malaysia Perlis, 2023-11-10)Extended Kalman filter (EKF) has been found as most widely used algorithm for state estimation due to its simplicity for implementation and theoretically attractive in the sense that minimizes the variance of the estimation error. Nevertheless it is known that EKF algorithm strictly assumed that the nature of the noise or errors in the system is Gaussian white noise. Yet, in real world this is not always true, which will lead to less accurate estimation. However there is an estimation approach that does not require the assumption of a specific noise as EKF which is particle filter (PF), which hypothetically can provide more accurate estimation under non-Gaussian noise condition. Hence, this work will study and compare accuracy performance of both estimation algorithms in simulated non-Gaussian white noise for satellite attitude application.
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