Pusat Pengajian Sains Komputer - Tesis

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Zero Waste Campus: A Framework And Digital Learning Module For Sustainable Environmental Practices In Higher Education Institution
(2024-08)
Nalikant, Mayawi Baba
The global rise in population and living standards has led to increased waste production, a significant issue in Malaysian universities where existing waste management methods such as recycling and reusing are inadequate. Guided by Saunders Research Onion methodology, this study examines the awareness and participation of Malaysian university communities in zero-waste initiatives focused on waste prevention. Utilizing a mixed-method approach with focus group discussions and in-depth interviews, the research identified ten key factors influencing Pro-Environmental Behaviour: personal experiences, environmental education, social technology, social responsibility, self-awareness, reinforcement contingencies, policy, leadership, community engagement, and environmental goals. These factors were synthesized into a framework that integrates an adapted Knowledge, Attitudes, Practices, Values, and Technology (KAPVT) theoretical framework with Kollmuss and Agyeman's PEB Model, Theory of Planned Behavior, and Value-Belief-Norm Theory, providing a comprehensive view of PEB. Structural Equation Modelling - Confirmatory Factor Analysis validated the relationships within this expanded framework using survey data from 393 respondents across three Malaysian universities. The findings demonstrated significant positive correlations between factors, leading to the creation of a digital Zero-Waste Campus Learning Module to promote sustainable practices. This provides a robust framework for enhancing national waste reduction strategies and advancing campus sustainability.
Analysable Chaos-based Design Paradigms For Cryptographic Applications
(2024-07)
Abba, Abubakar
Chaos-based cryptography has garnered significant attention, with many designs focusing on obscuring security through complex designs that make them difficult to analyze, improper design structures (ad-hoc designs) with inaccurate keyspace justification. These compromise the standards of well designed, simple, and secure design principles in cryptographical design protocol and do not facilitate future cryptanalytic efforts. Moreover, to date, there have not been any chaos-based cryptosystems being implemented to secure real-world communications. This study proposes simple and analyzable paradigms based on well-established cryptographic principles (SPN and Feistel) to address these issues. First, an in-depth review is conducted on the current state-of-the-art in the field of chaos-based cryptographic algorithms to identify the challenges of various design and evaluation methods that have been developed over the years. A comprehensive analysis into a largely overlooked problem in chaos-based cryptosystems is conducted, highlighting their unusually large keyspaces. Multiple examples demonstrate instances of chaos-based ciphers overestimating keyspaces and reveal practical and theoretical challenges in key generation approaches. The study highlights recommendations and alternative solutions for utilizing secret keys in chaos-based cryptography. Then, a simple chaos-based key schedule that ensures the involvement of every bit of the secret key in the generation of round keys is proposed. The proposed key schedule successfully passed both the NIST and ENT statistical test suites, indicating that highly complex designs are unnecessary to achieve desirable security properties.
Clustering Ensemble And Hybrid Of Deep Learning For Spatio-Temporal Crime Predictions
(2024-06)
The increase in the urban population poses challenges in managing services and safety from criminal activities. The concerned stakeholders intend to predict the time, location, number, and types of crimes to take suitable preventive measures. Accurate identification and prediction of crime hotspots can significantly benefit the concerned stakeholders in preventing crime by creating accurate threat visualizations and allocating police resources efficiently. Several techniques have been proposed for crime prediction, but they are limited in accuracy and predicting crime according to crime type on an hourly, monthly, and seasonal basis. Crime hotspot detection approaches are primarily sensitive to initial parameter selection and finding clusters of varying shapes and densities. Similarly, existing Crime prediction approaches are limited in capturing non-stationary data and long-term dependencies by focusing on crime types. Thus, the crime detection and prediction mechanisms need improvement in the number of crimes, crime span, accuracy, and dense crime region and prediction. The core objective of this study is twofold. First, it proposes a crime hotspot detection model to improve accuracy using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) and its clustering ensemble to capture varying shapes and densities clusters and improve accuracy. HDBSCAN is used with varying parameter initialization in the generation mechanism under the cluster ensemble paradigm. Moreover, six different distance measures are used to ensure diversity. In addition, an evaluation function is proposed parameterized by silhouette score to select the stable clustering among a pool of clustering solutions to ensure quality.
Hybrid Deep Networks Based On Periodshift Cosine Annealing For Customer Retention Prediction In Telecom Industry
(2024-08)
Victor, Johnson Olanrewaju
In the dynamic landscape of Customer Retention Prediction (CRP), the imperative to strategically direct marketing and promotion efforts towards targeted customers has never been more crucial. Identifying potential churn indicators and continually exploring innovative retention methods becomes paramount. However, a major challenge is customers terminating their services are rarely known among the loyal ones leading to an imbalance problem. Conventional Machine Learning (ML), with its prevalent reliance on feature extraction and data sampling methods, including cost-sensitive techniques, grapples with issues such as overfitting, computational complexity, and an undue emphasis on rare cases. Deep Learning (DL) techniques applied to CRP is promising for automatic feature extraction compared to the handcrafted method used in ML. However, non-cost-sensitive nature, appropriately chosen Learning Rate (LR) for better convergence, and quality feature learning in DL models still pose challenges. This thesis introduces a Class Imbalance Ratio Weight (CIRW) designed to tackle the imbalance problem in DL classifiers without incurring additional computational costs or loss of data symmetry. Additionally, it proposes a novel Period-Shift Cosine Annealing Learning Rate (ps-CALR) method to address LR dynamics during DL model training, thereby enhancing generalization. Finally, a hybrid DL model, combining an improved multilayer perceptron and a onedimensional convolutional neural network, is developed to learn improved features for customer retention analysis.
Modelling The Transmission Of Tuberculosis In Closed Space Using Microscopic Pedestrian Simulation
(2023-02)
Sabri, Nor Shamira
Due to the infamous COVID-19 pandemic, Tuberculosis deaths are rising for the first time in more than a decade. Tuberculosis is the second (after COVID-19) deadliest infectious killer that has been exists in this world for centuries. Despite the availability of adequate vaccination, it is still roamed around the world and became one of the leading diseases that contribute significantly to the world’s mortality rate. Therefore, this works aims to simulate how this infectious disease spread by utilizing the Susceptible-Infected (SI) model. As the research progresses, it is found that the general epidemiological studies that uses compartmental method ignore the heterogeneous of social interaction between humans. Thus, this research has proposed the integration of the Social Force model with the SI approach to imitate realistic human interaction while capturing the pathogen transmission process from one person to another. This work aims to simulate the movement and interaction between infected person and another susceptible person in a closed space when an infectious disease is present and develop with a process-oriented methodology framework following this setting set in the research. The methodological framework proposed is divided into three major stages: problem characterization, model construction also model analysis and evaluation, which work as step-by-step processes to achieve the objective sets.

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