Pusat Pengajian Sains Komputer - Tesis

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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.
Predictors Of Flourishing Among Elderly In Penang, Malaysia: The Role Of Demographic Variables And Perma Elements
(2022-07)
Liew, Wei Peng
Flourishing is a state of optimal mental well-being. It is the experience of doing and living well in all aspects of one's life, including psychological and social well-being. Flourishing plays a significant role in all life span, and every person has the potential to flourish. Elderly as the growing age group in Malaysia can benefit from being flourished. Nevertheless, current approach to studying flourishing is too general and limited studies have focused on flourishing, particularly among Malaysian elderly from a heterogeneous society with diverse cultural backgrounds. Each ethnicity has its own definition of what it means to flourish. By understanding the definitions of each ethnicity, interventions, programs, or measurements of those who flourish and those who do not can be taken appropriately. As a result, this study focuses on the role of demographic variables (e.g., age, gender, ethnicity, religion, marital status, source of income and education attainment) and elements in PERMA (e.g., engagement, positive relations with others, meaning in life and accomplishment) in contributing to flourishing among Malaysian elderly from various races.
A Simulated Annealing-Based Hyper-Heuristic For The Flexible Job Shop Scheduling Problem
(2023-03)
Kelvin, Lim Ching Wei
Flexible job shop scheduling problem (FJSP) is a common optimisation problem in the industry. The use of parallel machines allows an operation to be executed on a machine assigned from a set of alternative machines, raising a combination of machine assignment and job sequencing sub-problems. A straightforward technique to solve the FJSP is to apply a pair of machine assignment rule (MAR) and job sequencing rule (JSR), i.e. a MAR-JSR pair. However, the performance of each MAR-JSR pair is problem-dependent. In addition, within an algorithm execution, the MAR-JSR pair performs differently at different problem states. Given a wide range of MAR-JSR permutations, selecting a suitable MAR-JSR pair for a FJSP becomes a challenge. Positive outcomes on the application of simulated annealing-based hyper-heuristic (SA-HH) in addressing similar scheduling problem has been reported in the literature. Hence, this research proposes the SA-HH to produce a heuristic scheme (HS) made up of MAR-JSR pairs to solve the FJSP. The proposed SA-HH also incorporates a set of problem state features to facilitate the application of MAR-JSR pairs in the HS according to the state of the FJSP. This research investigates two variants of SA-HH, i.e. SA-HH based on the HS with problem state features (SA-HHPSF) and without problem state features (SA-HHNO-PSF).

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