Pusat Pengajian Sains Komputer - Tesis

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A Theoretical Framework To Evaluate The Behavioural Intention Towards A Habit Change Support System In Hospitals
(2025-01)
Cheryll, Anne Augustine
As technology advances across various fields, healthcare data gathering and processing have yet to reach their full potential. This study aims to design a theoretical framework to evaluate the behavioural intention towards a habit change support system, in hospitals. The study integrates commonly used adoption theories such as the technology acceptance model, the unified theory of acceptance and use of technology (utaut), and the theory of planned behaviour (tpb). A theoretical model comprising 10 variables was developed, to find the usability, feasibility and behavioural intention to use doctive and tested using a quantitative approach. This study was carried out using an online survey with the distribution of qr codes among medical professionals, out of which 162 responses were returned from two government hospitals in penang, malaysia.
Integration Of Fit And Viability In Cloud Enterprise Resource Planning Adoption Among Micro Small And Medium Enterprises In Egypt
(2024-09)
Amer, Salma Mohamed Elsayed Hassan
Cloud enterprise resource planning systems (cerp) are hosted on-demand services that offer significant opportunities for micro, small, and medium enterprises (msmes). The “pay-per-use” model, scalability, and flexibility of the cloud have removed many of the barriers associated with traditional erp systems, potentially contributing to the economic growth of msmes. However, msmes in developing countries, particularly in africa and the middle east, have been slow to adopt cerp although they are facing difficulties to operate efficiently. Based on a preliminary study and extensive literature review, a conceptual model was developed drawing on the diffusion of innovation (doi), technology, organization, environment (toe), and fit viability model (fvm) to investigate the factors influencing cerp adoption among msmes in egypt. This model was empirically validated through a survey of 236 respondents from egyptian msmes. The findings revealed new insights about cerp adoption. The results showed that fit and viability positively influence cerp adoption. Compatibility, observability, technological readiness, top management support, and competitive pressure were found to be major determinants of cerp adoption through the mediating effects of fit and viability
A Sliding Adaptive Beta Distribution Model For Concept Drift Detection In A Dynamic Environment
(2025-06)
Angbera, Ature
Machine learning models deployed in data streaming environments often suffer from concept drift, where the underlying data distribution changes over time, leading to performance degradation. Detecting and adapting to these shifts in real time is crucial to maintaining model accuracy and reliability. This study introduces the Sliding Adaptive Beta Distribution Model (SABeDM), a novel approach for concept drift detection and adaptation in dynamic data streams.
A Hybrid Multi-Tier Approach For Iot Botnet Detection And Enhanced Risk Assessment
(2025-01)
Ali, Mashaleh Ashraf Sulieman
The proliferation of internet of things (iot) devices has led to new cybersecurity challenges. A significant issue is the increasing occurrence of iot botnets, which refers to networks of compromised iot devices like routers, ip cameras, and smart appliances. These compromised entities are strategically utilized to carry out various cyber threats, including but not limited to distributed denial of service (ddos) attacks, data exfiltration, and network reconnaissance. Identifying iot botnets has unique issues due to the constrained resources of the devices involved. This research contributes significantly by identifying the active phase of the iot botnet attack life cycle and enabling flexible evaluation of attack severity levels through an ensemble model stacking and boosting via a soft voting system integrated with a fuzzy logic-based risk assessment methodology optimized by particle swarm optimization. This provides a basis for security teams to allocate resources efficiently, enabling a proactive and dynamic cybersecurity defense against iot botnet threats. A realistic and representative iot dataset was also generated, simulating the iot botnet lifecycle and incorporating the most recent attacks on iot ecosystems. The proposed approach significantly advances iot security by enabling precise detection of botnet activities and proactive threat mitigation. The integration of ensemble learning, fuzzy logic, and pso offers a dynamic solution that adapts to evolving cyber threats, ensuring targeted, efficient responses and safeguarding network integrity.
Hybrid Machine Translation Using Malay-English Language Parallel Text Extraction From Comparable Text
(2024-12)
Yeong, Yin Lai
Machine translation (mt) investigates the approaches to translate a text from a source language (sl) to a target language (tl). Parallel text is the resource that is essential for building the translation model of an mt system. A parallel text is a text and its translation in one or more languages. Nevertheless, there are not many parallel texts that are freely available. Thus, a few directions were explored and investigated in this thesis to improve the translation quality despite the limited parallel text. Firstly, we analysed using linguistic information in machine translation to compensate for the lack of data for training. Secondly, we studied the problem of acquiring a parallel text from comparable texts. Comparable texts are similar texts in different languages that may be independently produced. Thirdly, we investigated the architecture of statistical machine translation (smt) and neural machine translation (nmt) to combine the strength of both systems. This study was carried out using english-malay machine translation in the news domain and computer science domain. For the first problem, we propose to use affixation and part-of-speech information to build a translation model. We improve the bleu score from 13.40% to 15.41% using 315,194 parallel texts. In the second problem, we propose an algorithm to extract parallel sentences and parallel fragments/subsentences from comparable texts. The approach finds matching comparable texts. Then, a sentence aligner and a classifier are used to align the sentences in the comparable text.

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