Pusat Pengajian Sains Matematik - Tesis
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- PublicationNegative Based Higher Order Systematic Satisfiability Logic With Hybrid Black Hole Algorithm In Enhancing Multiunit Discrete Hopfield Neural Network(2025-06)Rusdi, Nur ‘AfifahUnderstanding intelligence is crucial for developing advanced intelligent models. In pursuit of this goal, satisfiability logical representation in Discrete Hopfield Neural Network has provided new insight in understanding the behaviour of the data. However, the role of negation in understanding intelligence has been overlooked as negation is often associated with false outcome. Negative Based Higher Order Systematic Satisfiability Logic is proposed to promote the appearance of negative literals within the clauses. The proposed logic demonstrates optimal performance as compared to existing logical rules. To further improve the quality of the final neuron states, Hybrid Black Hole Algorithm is proposed to update the neuron states that satisfy the multi-objective functions. The newly proposed mechanism will be incorporated into the logic mining model known as Multi-unit Negative Based Higher Order Systematic Satisfiability Reverse Analysis.
- PublicationHybrid Model Of Climate Effects On Dengue Forecast And Control In Selangor, Malaysia(2025-07)Lu, XinyiExisting dengue forecasting approaches in Malaysia are constrained by two main limitations: deterministic models typically assume a constant mosquito biting rate, neglecting its climate-driven variability, while statistical time series models are usually effective only for short-term forecasts. These constraints hinder the accurate prediction of outbreak dynamics and limit the utility of earlywarning systems. This study develops two integrated approaches: (i) coupled Susceptible-Infective for vector populations and Susceptible-Infective-Recovered for human populations (SI-SIR) and Autoregressive Integrated Moving Average with eXogenous variables (ARIMAX) model, and (ii) SISIR with Multiple Linear Regression and Long Short-Term Memory (MLR-LSTM) model.
- PublicationModified Iterative And Repetitive Estimation Methods For Fitting Measurement Error Model(2025-03)Al-Dibi’I, Ro’Ya Saleh FalehThis thesis introduces new estimation methods for fitting structural measurement error model (mem), addressing challenges in estimating relationships where all variables are subject to error. The study focuses on distribution-free weighted estimation techniques, which improve parameter estimation without the strict assumptions required by traditional methods. A new weighted wald-type estimation procedure, developed in modified iterative and repetitive forms, is proposed and tested on both simple and multiple mems. Simulation studies show that these methods achieve lower mean square errors across various weighting scenarios, outperforming classical approaches such as maximum likelihood estimation (mle) and the method of moments (mom) in terms of accuracy and reliability. Additionally, robust weighted estimation methods, including theil and siegel procedures, demonstrate superior performance in different settings. The proposed techniques are applied to two real datasets: one examining the relationship between infant deaths, hepatitis b, and polio, revealing strong negative correlations, and another analyzing gross domestic product (gdp), unemployment, and the human development index (hdi) in jordan, highlighting strong positive correlations between gdp and hdi and a strong negative relationship between unemployment and hdi.
- PublicationNew Modifications Of Ranked Set Sampling For Estimating Some Population Parameters(2025-03)Aldrabseh, Mahmoud Zuhier AbdulraheemTraditional sampling methods often lack efficiency and incur high measurement costs, creating a need for more efficient approaches that minimize the number of units measured while ensuring accuracy. Ranked set sampling (rss) is a cost-effective sampling technique that enhances parameter estimation by combining simple random sampling (srs) with the judgmental ranking of sample sets before measurement. Although rss and its variations have shown promise in cost reduction, the impact of excluding extreme ranks on rss efficiency remains unexplored. This study introduces a novel sampling design, except extreme ranked set sampling (eerss), to improve the efficiency of key estimators. The proposed eerss design is evaluated and compared with existing sampling methods for estimating the population mean, variance, and strength-stress reliability
- PublicationMotion Planning Of Differential Drive Mobile Robot By Circular Arc And Spiral Arc Trajectories(2025-01)Zakaria, Wan Zafira Ezza WanIn today’s world, mobile robots are essential tools capable of moving from one location to another. Motion planning, a critical task for these robots, has garnered extensive attention, particularly in sensor-based applications. However, robots that do not rely on sensor data pose significant research challenges, as continuously adjusting their movements to follow a predefined path becomes difficult. This work focuses on employing computer-aided geometric design (cagd) for sensorless motion planning as an effective curve computation technique. In this study, we present a sensorless motion planning method for differential drive mobile robots (ddmrs), considering scenarios where the start point, endpoint, and initial direction are known. Ddmrsare widely used in various applications due to their reliability and ease of use. They achieve mobility by distributing velocity between their left and right wheels, determined by the curvature value. Our study outlines a method for constructing and connecting arcs between points, focusing on two types of arcs which are circular arcs and clothoids. Circular arcs are defined by parametric equations describing their circular form, while clothoids are characterized through fresnel integrals.