Pusat Pengajian Sains Fizik - Tesis

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    Factors Influencing Student Engagement With Game Elements Among Undergraduate Students In Saudi Arabia
    (2025-07)
    Freeh M , Allehaidan Ahmed,
    This research aimed to examine the direct effect of Unified Theory of Acceptance and Use of Technology (UTAUT) components (i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions), on the attitude toward using gamification as well as on students’ engagement (i.e., skill engagement and participant engagement).
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    Enhancement Of ZnO-Based UV Photodetectors By Incorporating Bi₂O₃, Ag, And Ge Nanostructures Synthesized Using Laser Ablation In Liquid
    (2025-07)
    Marzouq B., Alharbi Abdullah
    This research enhances the efficiency of UV photodetectors through the synthesis and integration of nanoparticles using laser ablation techniques. The study investigates the effects of bismuth oxide nanosheets (Bi2O3-Nsh), silver nanoparticles (AgNPs), and germanium nanowalls (GeNWs) on ZnO/Si-based UV photodetectors.
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    Investigation Of Debris Flow In The Muda River Basin Kedah, Using Remote Sensing, Hydrology, Gis, And Geoelectrical Methods
    (2025-04)
    Sirajo, Abubakar
    This study investigates the July 4, 2022, debris flow event in Kampung Iboi, Muda River Basin (MRB), Kedah, which caused an estimated RYM25.91 million in financial losses and claimed three lives. Employing a multidisciplinary approach of remote sensing, hydrology, Geographic Information System (GIS), and geoelectrical techniques, the study identifies surface and subsurface factors contributing to debris flow and delineates hazard-prone areas, with an emphasis on preventive measures
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    Improving Photometric Redshifts By Varying Activation Functions In Artificial Neural Networks
    (2024-12)
    Pathi, Imdad Binti Mahmud
    In recent years, the astronomical community has faced a data deluge of up to exabytes due to the advancement of telescope technology. In fact, the application of machine learning techniques has simplified the task of analysing data for astronomers. Photometric redshift estimation or photo-z is one of the relevant applications of machine learning and statistical methods in cosmology. Due to its significant contribution to cosmology, there is an increasing demand for precise photo-z for forthcoming astronomical surveys, including the Euclid mission and LSST. The accuracy and performance of the photo-z algorithm have been improved by adopting and modifying machine learning hyperparameters. The Artificial Neural Network Redshift (annz) algorithm is a fast and simple machine learning photometric redshift estimator. One of the hyperparameters in the artificial neural network (ANN) is the activation function. It acts as a decision-making unit, which introduces non-linearity into the model, leading to better differentiation capabilities and potentially boosting the ANN’s performance if optimally tuned. We test the performance of annz by varying the activation functions, replacing the original logistic sigmoid with tanh, Softplus, SiLU, ReLU, Leaky ReLU and Mish. The training is demonstrated on the Luminous Red Galaxy (LRG) sample of the Sloan Digital Sky Survey (SDSS), Stripe-82 survey, and Physics of the Accelerating Universe Survey (PAUS). We also tested the performances of these activation functions by varying the depth and width of the ANN architectures.
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    Exploring The Synergy Of Template And Machine Learning Methods To Improve Photometric Redshifts
    (2024-10)
    Khalfan, Alshuaili Ishaq Yahya
    This thesis explores the use of both template-based and machine learning methods to improve the accuracy of galaxy photometric redshift estimation. The first method involves using template fitting to model the spectral energy distribution of a galaxy and estimate its redshift. The second method uses machine learning algorithms to learn the relationship between a galaxy’s photometric properties and its redshift, based on a training set of spectroscopic redshift measurements. This thesis also aims to investigates the potential synergy between these two methods by combining them in various ways and comparing the results to those obtained using each method individually. ( Password P-SD0060/21(R )