Pusat Pengajian Sains Matematik - Tesis

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A Weighted Least Squares Estimation Of The Polynomial Regression Model On Paddy Production For The Muda Agriculture And Development Authority (Mada) Area
(2016-01)
Musa, Rosliza
The curvilinear relationship between a dependent variable and several independent variables can be represented by a polynomial regression model. This model is used to study the relationship between response variable and predictor variable which contain square and higher-order term. Polynomial regression model is a special case of multiple regression model. The building of polynomial regression model has the same characteristics as multiple linear regressions in term of parameter estimation, regression inference, variable selection and model diagnostic. Weighted least square estimation is used as a remedy for non-constant variance. This study used polynomial regression model with weighted least square estimation to investigate paddy production of different paddy lots based on environmental and cultivation characteristics in Muda Agriculture and Development Authority (MADA) area.
An Improved Training Method For Wavelet Neural Networks For Solving Ordinary And Partial Differential Equations
(2022-10)
Tan Lee Sen
Differential equations (DEs) have been widely used for modelling countless studies and applications in various disciplines. In recent years, artificial neural networks (ANNs) have gained attention in solving DEs in a more efficient way to approximate solutions of DEs. In this thesis, wavelet neural networks (WNNs) were employed and programmed to solve ordinary differential equations (ODEs) and partial differential equations (PDEs).
Application Of Two-Stage Game Cross- Efficiency Approach To Primary Health Care In Nigeria
(2023-04)
Adejoh Friday Oduh
This study aims to analyse the performance of primary health care (PHC) in Nigeria by using two states (Benue and Lagos) as case studies and the game theory-based data envelopment analysis (DEA) technique is introduced. Game theory helps to uncover the dominant (leader) stage of an unobservable two-stage decision making unit (DMU) in the absence of prior knowledge.
Arrangement Of Letters In Words Using Parikh Matrices
(2019-04)
Poovanandran, Ghajendran
The Parikh matrix mapping is an ingenious generalization of the classical Parikh mapping in the aim to arithmetize words by numbers. Two words are M-equivalent if and only if they share the same Parikh matrix. The characterization of M-equivalent words remains open even for the case of the ternary alphabet. Due to the dependency of Parikh matrices on the ordering of the alphabet, the notion of strong M-equivalence was proposed as an order-independent alternative to M-equivalence. In this work, we introduce a new symmetric transformation that justifies strong M-equivalence for the ternary alphabet. We then extend certain work of §erbanuja to the context of strong ^-equivalence and show that the number of strongly M-unambiguous prints for any alphabet is always finite.
Assessment Of Hydraulic Response Due To Flood Mitigation Measures In Sg. Pinang Catchment, Penang
(1995-02)
Low Chee Soon
The current study aims to assess the hydraulic response of Sg. Pinang catchment under both existing and proposed conditions, by means of the advanced flow simulation model SWMM developed by the USEPA.
B-Spline Collocation Methods For Coupled Nonlinear Schrödinger Equation
(2021-01)
Saiful Anuar, Hanis Safirah Binti
In this study, the Coupled Nonlinear Schrödinger Equation (CNLSE) which models the propagation of light waves in optical fiber is solved using numerical methods namely Finite Difference Method (FDM) and B-Spline collocation methods. The equation was discretized in space and time. We propose the discretization of the nonlinear terms in the CNLSE following the Taylor approach and a newly developed approach called Besse. The theta-weighted method is used to generalize the scheme whereby the Crank-Nicolson scheme (i.e θ = 0.5) is chosen. The time derivatives are discretized by forward difference approximation. For each approach, the space dimension is then discretized by five different collocation methods independently. The first method for Taylor approach is based on FDM whereby the space derivatives are replaced by central difference approximation.
Bayesian Networks With Greedy Backward Elimination In Feature Selection For Data Classification
(2019-03)
Ang, Sau Loong
Naive Bayes (NB) is an efficient Bayesian classifier with wide range of applications in data classification. Having the advantage with its simple structure. Naive Bayes gains attention among the researchers with its good accuracy in classification result. Nevertheless, the major drawback of Naive Bayes is the strong independence assumption among the features which is restrictive. This weakness causes not only confusion in the causal relationships among the features but also doubtful representation of the real structure of Bayesian Network for classification. Further development of Naive Bayes in augmenting extra links or dependent relationships between the features such as the Tree Augmented Naive Bayes (TAN) end up with slight improvement in accuracy of classification result where the main problems stated above remain unsolved.
Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network
(2023-05)
Muhammad Sidik, Siti Syatirah
One of the alternatives to improve the modeling of the Discrete Hopfield Neural Network is by implementing different variants of logical rules. In this context, Satisfiability is suitable as a logical rule in Discrete Hopfield Neural Network due to the simplicity of the structure, and fault tolerance. Hence, this thesis will utilize Non-Systematic Weighted Random 2 Satisfiability incorporating with Binary Artificial Bee Colony algorithm in Discrete Hopfield Neural Network. The Binary Artificial Bee Colony will be utilized to optimize the logical structure according to the ratio of negative literals by capitalizing the features of the exploration mechanism of the algorithm. Then, the Election algorithm will be utilized to obtain a satisfied interpretation of the correct logical structure in the training phase of the Discrete Hopfield Neural Network. This proposed model will be employed in the Improved Reverse Analysis method to extract the relationship between various fields of real-life data sets based on logical representation. This thesis will be presented by implementing simulated, and benchmark data sets with multiple performance evaluation metrics. Based on the findings, the proposed model outperforms other models.
Detection Of Outliers And Structural Breaks In Structural Time Series Model Using Indicator Saturation Approach
(2023-03)
Rose, Farid Zamani Che
The presence of structural changes, specifically outliers and structural breaks, adversely affects the estimation of economic and financial indicators in terms of the model accuracy and forecasting performance. Focusing on the detection of outliers and structural breaks, which has recently gained growing research interest, this study aimed to examine the performance of indicator saturation, as an extension of the general-to-specific (GETS) modelling, in detecting these structural changes in structural time series model framework. The proposed technique is capable to detect the location, duration, magnitude and number of structural changes in time series data. To date, prior studies only considered using Autometrics embodied in OxMetrics to apply this approach in static data generating process (DGP). Addressing this gap, this study used the gets package in R to examine the performance of indicator saturation in dynamic model viz state space model. Through Monte Carlo simulations, the performance of indicator saturation was evaluated in terms of potency and gauge. Based on the simulation results, the sequential selection algorithm outperformed the non-sequential selection approach in the automatic GETS model selection procedure. The results also suggested α = 1/T as the optimum level of significance level.
Development Of Variable Sampling Interval Run Sum T Chart And Triple Sampling X ̅ Chart With Estimated Process Parameters
(2022-03)
Nahar Mim, Faijun
The Shewhart x ̅ control chart is a useful chart in process monitoring. However, the Shewhart x ̅ chart’s performance is significantly affected if the process standard deviation is erroneously estimated. To circumvent this problem, the t chart is commonly used as an alternative to the Shewhart x ̅ chart. The first and second objectives of this thesis aim at enhancing the performance of the basic t chart by proposing the variable sampling interval run sum (VSI RS) t charts for monitoring the mean of a process from a normal distribution, based on known and estimated process mean, respectively. The Markov chain technique is used to compute the optimal parameters for the new charts.
Double Sampling Auxiliary Information Chart And Exponentially Weighted Moving Average Auxiliary Information Chart, Both Based On Variable Sampling Interval, And Measurement Errors Based Triple Sampling Chart
(2022-09)
Umar, Adamu Abubakar
The concept of using auxiliary information (AI) in control charts is growing in popularity among researchers. Control charts using the AI technique have been found to be more effective than control charts without the AI technique. The first objective of this thesis is to develop a variable sampling interval (VSI) double sampling (DS) chart using the AI technique (called VSI DS-AI chart) for monitoring the process mean. The charting statistics, optimal designs and implementation of the VSI DS-AI chart are discussed. The steady-state average time to signal (ssATS) and steady-state expected average time to signal (ssEATS) criteria are used as the performance measures of the proposed VSI DS-AI chart. The ssATS and ssEATS results of the VSI DS-AI chart are compared with those of the double sampling AI, variable sample size and sampling interval AI, exponentially weighted moving average AI (EWMA-AI) and run sum AI (RS-AI) charts. The comparison reveals that the VSI DS-AI chart performs better than the competing charts for all shift sizes, except the EWMA-AI and RS-AI charts for small shifts.
Dynamics Of Co-Infectious Childhood Respiratory Diseases: Pertussis And Pneumonia
(2023-02)
Yakubu, Aisha Aliyu
Pertussis is a vaccine-preventable respiratory disease that affects humans of all age groups, yet there are reported cases of resurgence. The disease is highly contagious and has posed detrimental effects on the lives of infants globally. The impact of pertussis worsened with the presence of viral infections such as pneumonia. Therefore, it is imperative to study the behavior of these diseases and suggest control strategies using a mathematical modeling approach. The study area and literature of mathematical models on pertussis and pneumonia co-infection dynamics is rather scanty. Therefore, this study is aimed at obtaining model equations using a system of nonlinear ordinary differential equations for a better understanding of the transmission dynamics and control of these diseases in the infant population. Further, the models are used to evaluate the intervention strategies for disease control. The first model is the general model describing the transmission dynamics of pertussis which incorporates a maternally derived immunity compartment. The dynamical behavior of the basic model is analyzed analytically and numerically. Numerical simulations were carried out using mathematical software. The basic reproduction number of the model is obtained and its behavior is analyzed by varying parameters.
Economic And Economic-Statistical Designs Of Variable Sample Size And Sampling Interval Coefficient Of Variation Chart And Development Of Variable Sample Size Multivariate Coefficient Of Variation Chart For Short Runs
(2023-05)
Chew, Yiying
Control charts are one of the most useful statistical process control tools that have been adopted for process monitoring in numerous fields. Traditional control charts are ineffective in process monitoring when the process being monitored does not have the process mean and variance that are independent of one another. Under such a circumstance, the coefficient of variation (CV) is used in process monitoring, where the ratio of the standard deviation to the mean is monitored. The variable sample size and sampling interval CV (VSSI CV) chart was shown to be more effective than the Shewhart CV (SH CV) chart in the literature but only in terms of the statistical performance. Thus, the first objective of this thesis is to investigate the economic and the economic-statistical performance of the VSSI CV chart. By minimizing the cost via the economic and economic-statistical designs, the VSSI CV chart can be implemented more economically. The economic and economical-statistical performance of the VSSI CV chart is studied using numerical examples, where comparisons with the SH CV chart are made. The results show that the VSSI CV chart outperforms the SH CV chart, in terms of both economic and economic-statistical performance. A study on the misspecification of the shift size is also conducted to study the effect of wrongly specifying the shift size on the optimal cost.
Efficient Entropy-Based Decoding Algorithms For Higher-Order Hidden Markov Model
(2019-03)
Chan, Chin Tiong
Higher-order Hidden Markov model (HHMM) has a higher prediction accuracy than the first-order Hidden Markov model (HMM). This is due to more exploration of the historical state information for predicting the next state found in HHMM. State sequence for HHMM is invisible but the classical Viterbi algorithm is able to track the optimal state sequence. The extended entropy-based Viterbi algorithm is proposed for decoding HHMM. This algorithm is a memory-efficient algorithm due to its required memory space that is time independent. In other words, the required memory is not subjected to the length of the observational sequence. The entropybased Viterbi algorithm with a reduction approach (EVRA) is also introduced for decoding HHMM. The required memory of this algorithm is also time independent. In addition, the optimal state sequence obtained by the EVRA algorithm is the same as that obtained by the classical Viterbi algorithm for HHMM.
Estimation Of Weibull Parameters Using Simulated Annealing As Applied In Financial Data
(2023-03)
Hamza, Abubakar
An accurate analysis of financial data is vital to justify sustainability for investment potential in a company. Weibull distributions can be used to examine investment behaviour due to their flexibility to be transformed into other types of distribution. However, the selection of the most suitable estimators is still a challenging task. The present study proposes a simulated annealing algorithm (SA) in estimating the parameters of Weibull distribution with application to modified internal rate of return data (MIRR).The objective is to examine the investment potential of the shari’ah compliance companies of the Malaysia property sector (MPS). The MIRR were computed based on the data extracted from the companies’ financial reports from 2010 to 2018. The performance of the SA algorithm has been explored in terms of accuracies and estimation errors. The finding reveals that the Weibull distribution is well-suited to describing the investment behaviour of the MPS based on the estimates via the SA algorithm. Therefore, purchasing shares in this sector is very attractive for a long-term investment period, but may have a high risk of committing it as a result of fluctuations in the mean and variance of the estimate. Additionally, the two-parameter Weibull distribution has been extended by incorporating additional parameters to capture the uncertainty behaviour in the financial data.
Evaluating The Effectiveness Of Monetary Versus Fiscal Policies In Malaysia Using Macroeconometric Approaches
(2022-09)
Ismail, Siti Fatimah
The purpose of this thesis is to examine the roles of monetary and fiscal policies in achieving Malaysia's basic macroeconomic goals of price stability and long-term growth. This thesis is divided into two main parts. The first part employs nonlinear modelling techniques to investigate the nonlinear effect of policy stances on GDP growth and inflation using Malaysian data from 1980Q1 to 2018Q1. The results of the STAR and TAR approaches reveal the existence of a nonlinear relationship. The results show that no single policy tool can lead to the policy objectives of high GDP growth and low inflation at once. Both STAR and TAR results evident that the fiscal tools of government expenditure, current account balance and debt are harmful to the economic growth and the impact on inflation is either negative or not significant. In terms of monetary policy, the policy rate is a less effective tool to stimulate GDP growth but is a better option to control or reduce inflation. Meanwhile, real effective exchange rate encourages GDP growth but it does not influence price level significantly. The STAR model is a preferred model in capturing the gradual threshold adjustment in economic variables. In the second part of the analysis, a macroeconometric model is developed to evaluate the performance of Malaysia's monetary and fiscal policies as well as to project different economic outcomes and scenarios through numerical simulations.
Forecasting The Decision Based On Risk Perception Using Bayesian Game Theory Approach In Preventing Hand, Foot And Mouth Disease (Hfmd) In Pulau Pinang, Malaysia
(2023-07)
Abu Mansor, Siti Nurleena
Hand, foot, and mouth disease (HFMD) is amongst common diseases which often occurs in outbreaks and up to now, there is no effective vaccine yet to be found. Known to be caused by enterovirus usually typed Coxsackie virus A16 and Enterovirus 71, this disease is common among infants and children below the age of five. In this research, the HFMD epidemic models in Seberang Perai Tengah, Pulau Pinang are examined. Number of cases in epidemic week are used to compare HFMD epidemic model with and without virus shedding in the environment and contemplate their reproduction numbers. Furthermore, human behaviour has become the main factor in HFMD disease transmission systems. The effectiveness of interventions is largely dependent on the behaviour of the population. Risk perception shapes human behaviours in many ways, especially in decision making. Here, the factors that determine parents’ risk perception on HFMD disease and how they quantify their level of risk are explored. Factors such as knowledge of HFMD, health beliefs, preventive behaviour, and experienced with HFMD are put together and analysed to develop a framework to show the relationship with risk perception. Moreover, when making decisions related to health behaviours, one's decisions do not influence the decisions of others.
Geometrical Analysis Of Quintic Trigonometric Bézier Surface
(2023-05)
Mohd Kamarudzaman, Anis Solehah Binti
The quintic trigonometric Bézier curve with two shape parameters has been extensively investigated due to its flexibility. Commonly, the Bézier curve has been widely used as a curve or surface designing tool in manufacturing industries. Hence, the study of surface curvature is required in design analysis. In this research, the quintic trigonometric Bézier curve has been implemented to generate various adjustable surfaces such as tensor product, swept, swung, ruled, and developable surfaces by using various value of shape parameters. The effect of the shape parameters on the surfaces has been demonstrated. Gaussian curvature, mean curvature, and Shape Index-Curvedness (SC Curvature) will be used to examine the geometric characteristics of surfaces. The Gaussian and mean curvature plots for each surface are visualised and evaluated. In addition, this study presents an alternate method for inspecting the geometrical properties of a surface using algebraic invariants. Surface curvature can be compared using differential geometry and algebraic invariants approach, leading to interesting discoveries. Additionally, the numerical data are presented to support the surface’s geometrical analysis that has been demonstrated by the 3D plot display. In conclusion, different surfaces will produce different curvature value, however, the shape parameters will alter the curvature’s intensity.
Hierarchical Gaussian Process Models For Loss Reserving
(2021-12)
Ang,Zi Qing
Loss reserving is one of the main activities of actuaries in the insurance industry and is done to ensure the financial health of companies as well as protecting consumers’ interest. Techniques applied by the practitioners are highly regulated, but researchers are still ongoing in the pursuit of finding methods to improve predictive accuracy and to establish a measure of predictive uncertainties. Diverting from the link ratio methods, researchers have experimented with parametric models such as growth-curve models and models involving dynamical systems, as well as nonparametric models. Researchers in this field have increasingly shown interests in utilizing Bayesian methods to measure predictive uncertainties.
Hybrid Model In Machine Learning With Robust Regression For Handling Multicollinearity Outlier In Big Data And Its Application To Agriculture
(2023-01)
Mukhtar
In this research, 29 independent single variables and 435 independent interaction variables were identified. The limitation of this research were to address the problems such as irrelevant variables, multicollinearity and outliers.

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