Pusat Pengajian Sains Komputer - Tesis

Browse

Now showing 1 - 5 of 470

Mental Stress Classification Among Higher Education Students In Malaysia From Electroencephalogram (Eeg) Using Convolutional Neural Network With Modified Stochastic Gradient Descent
(2024-09)
Rashid, Nur Ramizah Ramino
This study investigates the classification of mental stress among Malaysian university students using Electroencephalogram (EEG) data and a 1D-Convolutional Neural Network (1D-CNN) optimized with Modified Stochastic Gradient Descent (SGD). The research addresses a significant gap in the availability of localized datasets for stress detection using EEG signals, as existing models and datasets predominantly focus on other populations and do not account for regional variations in stressors and responses. Moreover, there is a lack of optimization in stress detection models, specifically in handling EEG data, which can affect the models’ accuracy and real-time application potential. To address these challenges, EEG signals were collected during Stroop tests and self-reported stress levels were measured using the Perceived Stress Scale (PSS). A rigorous preprocessing approach, including Independent Component Analysis (ICA) for artifact removal, was applied, followed by feature extraction focusing on key metrics such as energy, entropy, and standard deviation from both time and frequency domains. The chosen algorithm, 1D-CNN, was modified using a tailored SGD optimizer that incorporates momentum and learning rate decay to improve convergence and address challenges like vanishing gradients. This modification was essential for enhancing the model’s learning process, ultimately leading to better stress classification performance. The proposed 1D CNN model, enhanced with Modified SGD, demonstrated superior performance compared to traditional models such as Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and deeper architectures like Standard CNN and AlexNet. Specifically, the 1D CNN achieved an accuracy of 92.64%, outperforming SVM (84.5%), k-NN (76.6%), Standard CNN (91.3%), RNN (90.04%) and AlexNet (91.65%). The 1D CNN model also demonstrated high sensitivity and specificity, making it a robust solution for EEG-based stress detection.
Modified Nsga-Iii As A Many-Objective Optimization Technique For Ab Initio Protein Structure Prediction
(2024-05)
Smadi, Motasem Mohammad Yaqoob Al
Predicting the 3D structure of proteins, known as protein structure prediction (PSP), is an essential component of bioinformatics. One of the adopted approaches to solving the PSP problem is an ab initio modeling method, which has demonstrated great performance in recent years. In this work of research, the non-dominated sorting genetic algorithm III (NSGA-III) has been adapted by modeling an ab initio PSP problem as a many-objective optimization problem (MOOP). Furthermore, this study proposes four novel objectives from three distinct energy function types with the goal of solving as a MOOP. In this regard, the first two objectives are derived from the physics-based energy function (PEF) which decomposes into bond and nonbond terms. Besides, the third objective is a solvent effect model represented by SASA, and the last objective is a knowledge-based energy function (KEF) represented by dDFIRE. To demonstrate the efficiency of energy functions, the implemented NSGA-III is handled with three suggested sets of objectives: MO3SASA (consisting of bond, nonbond, and SASA); MO3dDFIRE (consisting of bond, nonbond, and dDFIRE); and MaO4 (consisting of bond, nonbond, SASA, and dDFIRE). Moreover, to further improve the exploration and exploitation of conformational space search processes with regard to NSGA-III, three genetic operators of the genetic algorithm (GA) are proposed, including two selection operators, four crossover operators, and three mutation operators. These proposed operators produced 25 conformational search algorithms (CSAs) for the proposed NSGA-III.
Software Model Checking For Distributed Applications Using Hybridization Of Centralization And Cache Approaches
(2024-04)
Hing, Ratana
Developing reliable distributed systems poses significant challenges due to the non-deterministic nature of thread and process execution, as well as communication channels. Software model checking offers a means to verify system correctness by exhaustively analyzing all program execution paths. However, the existing bytecode model checker, capable of verifying multiple processes, suffers from state space explosion and computational overhead. This thesis introduces Java PathFinder (JPF)-Nas-Hybrid (JNH), a novel model checker addressing these limitations. JNH employs a redesigned inter-process communication (IPC) model and integrates a scalable caching mechanism. This mechanism efficiently stores communication data between processes, mitigating computational overhead and state space explosion during model checking. By optimizing resource utilization and minimizing overhead, JNH significantly improves verification performance. Key enhancements include the development of a scalable caching mechanism integrated into the centralization IPC model, relocating request and response trees, and processing data in multi-byte chunks. JNH's creation involves extending from the JPF-core system and modifying Java network libraries. Additionally, the thesis explores bug detection strategies, distinguishing between local and global bugs, and evaluates various search strategies to explore distributed program state spaces. Through comprehensive testing and statistical analysis, the research provides insights into effective bug detection approaches, further advancing model-checking methodologies.
Examining The Impact Of Computer-Based Assessment On Higher Education Student Performance In Kano, Nigeria
(2024-04)
Bello, Hassan
Although scholars have explored the drivers of students’ Computer-Based Assessment (CBA) intention, actual responses to CBA, and the impact of CBA on student performance, the intention-performance link remain under-explored. Thus, this study aimed to investigate the impact of CBA on student performance in Kano, Nigeria and suggests that factors such as students' goal expectancy and technology readiness influence their performance with the CBA system. Additionally, this study explores how service satisfaction affects overall satisfaction and thus influences student performance within the CBA system. The study merged the Expectation Confirmation Model (ECM), the Information System Success Model (ISSM), and the Technology Readiness Model (TRI 2.0) to examine the research framework. Survey data collected from 459 undergraduate students in selected higher education institutions in Kano who used the CBA system to test the framework. The study reveals that the impact of the CBA system on individual performance of Kano state higher education students depends not only on the continuance intention of the system but also on students’ satisfaction. The findings further indicate that goal expectancy and technology readiness negatively influence the connection between continuance intention and individual performance. These findings suggest that continuance use intention, overall satisfaction, goal expectancy, and technology readiness facilitate the individual performance of the CBA system. In addition, the results demonstrated that service satisfaction mediates between system quality, service quality, and overall satisfaction. Moreover, the study established that service satisfaction is the most significant factor xix that affects the overall success of the in-campus CBA system.
Acltshe-Amts: A New Adaptive Brain Tumour Enhancement And Segmentation Approaches
(2014-05)
Alkhafaji, Ali Fawzi Mohammed Ali
Brain tumor subregion segmentation from multimodal Magnetic Resonance (MR) images is of great interest for better tumor diagnosis. Multilevel thresholding is one of the prominent approaches used for brain image segmentation. Currently, when applying multilevel thresholding for brain tumor segmentation, two important problems must be carefully addressed. First, the MR brain images suffer from sensitivity to intensity inhomogeneity, poor contrast, and hidden details, which corrupt the original MR image during capturing. Second, the conventional multilevel thresholding approaches, including optimization-based thresholding approaches, have several main issues, such as manual adjustment of multilevel thresholds, dedicating single thresholding criteria as objective functions, leading to the bias of the thresholding towards a specific type of MR image, and the requirement to tune a large number of control parameters. In this thesis, a two-stage approach is proposed to address these issues. In the first stage, a new image enhancement approach called Adaptive Clip Limit Tile Size Histogram Equalization (ACLTSHE) is proposed to improve contrast, highlight the hidden details, and achieve homogenized intensity distribution of MR images. The ACLTSHE integrates Contrast-Limited Adaptive Histogram Equalization, Multi-Objective Whale Optimization Algorithm, Discrete Entropy (DE), Peak Signal-to-Noise Ratio (PSNR), and Structure Similarity Index (SSI) to improve the quality of MR images while preserving the original structure of the MR images. In the second stage, a new approach called Adaptive Multilevel Thresholding Segmentation (AMTS) is proposed for unsupervised brain tumor xxi subregion segmentation from normal brain tissue. The AMTS approach segments and extracts the whole tumor, core tumor, and enhanced tumor regions from the brain MR images, integrating the Multi-Objective Grasshopper Optimization algorithm, Kapur Entropy, Cross-Entropy, and Localized active contour.

Browse

Recent Submissions