UTAR Institutional Repository (Universiti Tunku Abdul Rahman)
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Efficiency and productivity of insurance industry in Malaysia: a comparison between conventional insurance and takaful
Get PDFMotivated by the emergence of digital insurers in Malaysia, which poses a challenge to the long-term sustainability of established industry players, this study aims to evaluate the efficiency and productivity of the Malaysian insurance sector, focusing on a comparison between conventional insurers and Takaful operators. Additionally, with corporate governance receiving heightened attention after the collapse of American International Group (AIG) in 2008, attributed to improper accounting practices, and the rising significance of Environmental, Social, and Governance (ESG) considerations, this research further investigates the link between corporate governance attributes and the efficiency of these insurers.
The efficiency and productivity of the insurers are measured using Data Envelopment Analysis (DEA) and the Malmquist Productivity Index (MPI). Panel regression techniques, including pooled Ordinary Least Square (OLS), Fixed Effect Model (FEM), and Random Effect Model (REM), were applied to examine the factors influencing insurer performance, particularly corporate governance aspects. By utilising data from 59 conventional insurers and 29 Takaful operators from 2013 to 2021, this study reveals that scale efficiency exerts a more pronounced impact on overall industry efficiency compared to pure technical efficiency. Additionally, productivity showed a 10% improvement during the study period, largely driven by technological advancements. Parametric and non-parametric test results demonstrate a notable difference in the efficiency and productivity between conventional and Takaful insurance
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sectors in Malaysia. Listed firms in the country displayed lower levels of both technical and pure technical efficiency, while scale efficiency was positively associated with GDP and the proportion of males on the Shariah committee. This study underscores the necessity for conventional insurers to enhance operational efficiency, strategic scaling, and technological adoption to mitigate inefficiencies associated with expansion and to maintain competitiveness. Furthermore, it highlights the positive influence of corporate governance attributes, such as firm size and foreign participation, on the technical efficiency of Malaysian insurers, emphasising the vital role of governance in boosting performance within the insurance sector.
Keywords: Insurance, efficiency, productivity, conventional insurers, takaful operators
Subject area: HG8011-9999 Insuranc
Efficient tracking operation for supply chain management based on blockchain technology
Get PDFThe global supply chain for high-value goods is persistently challenged by issues of opacity, inefficiency, and susceptibility to counterfeiting, which significantly erode consumer trust and brand value. Traditional Supply Chain Management (SCM) systems often lack the requisite transparency and traceability to effectively combat these problems. This project is the development of a blockchain-based system for efficient tracking operations in luxury watch supply chain management. The luxury watches industry faces significant challenges from counterfeiting and a lack of transparency in traditional supply chains which lead to substantial financial losses and erosion of consumer trust. This project proposes a solution leveraging blockchain technology to create an immutable, transparent, and secure digital ledger for tracking luxury watches throughout their lifecycle. The system implements smart contracts on the Ethereum platform to manage raw material registration, raw material tracking, component registration, component tracking, certification, NFT watch, and ownership transfers, with a role-based access control mechanism to ensure appropriate permissions for different stakeholders. The proposed system architecture consists of interconnected smart contracts and a React-based frontend interface, designed to provide comprehensive tracking capabilities while addressing implementation challenges related to the physical-digital link, privacy, and scalability. Through the integration of artificial intelligence, the system can make watch price predictions and provide consulting services. Preliminary work demonstrates the feasibility of the approach through the implementation of core smart contracts and basic frontend functionality. This project contributes to the field by presenting an integrated approach to luxury watch tracking to implement granular access control, enable component-level traceability, and provide a practical blueprint for blockchain applications in luxury goods authentication
Implementing server-side federated learning in an edge-cloud framework for precision aquaculture
Get PDFThis project is about a growing need in the aquaculture industry which is precision
aquaculture. Precision aquaculture involves the use of smart technologies such as
sensors, cloud computing, and artificial intelligence to monitor and manage fish
farming environments. However, small-scale farmers still face major challenges such
as high implementation costs, poor internet connectivity, and concerns about data
privacy. This project focuses on two key problems which are data privacy and system
scalability. Most existing systems rely heavily on cloud connectivity and do not provide
secure or efficient solutions for farms in remote areas with limited internet access. In
this project, research and literature reviews were conducted to explore the current
technologies in smart aquaculture, federated learning, and data privacy. Reviews
include systems using IoT and AI-based models, along with analysis of different
federated learning algorithms such as FedSGD, FedAvg, and FedProx, and privacypreserving methods such as DA, SA, HE and CKKS encryption. After identifying the
gaps in current systems, this project proposes a secure and scalable server-side
federated learning framework in an edge-cloud architecture for precision aquaculture.
The system is designed to enable encrypted model training at the edge using IoT sensors
and Raspberry Pi, while a federated learning server hosted on AWS aggregates updates
without accessing raw data. The final product integrates edge computing, federated
learning, encryption and cloud storage to create a privacy-preserving and cost-effective
solution for monitoring aquaculture environments and supporting small-scale farmers
Personal mobile-health application
Get PDFNowadays in the society, there is a phenomenon for raising health awareness and encouraging healthier lifestyles with the growing use of mobile health apps. However, the issues have been discovered such as ineffectiveness in managing the risks of chronic diseases, low user engagement, and insufficient features for individualized health insights. Through the application of innovative technological approaches and a user centred design approach, this project aims to fill these gaps. The Android-based mobile health application will employ ML-driven algorithms to diagnose the risk of chronic diseases, giving users personalized early warnings and proactive health management tools. The proposed mobile health application will be considered about user engagement and motivation of using the application which provides user engagement features like chatbot as personal health assistant to increase user engagement and motivation by achieve healthy lifestyle practice. The proposed application will also be leveraging the device's camera, various of assessment, text-sentiment analysis and AI emotion recognition technology that will evaluate the user's emotions before offering personalised insights and practical guidance to enhance mental and emotional health well-being. The application will provide user-friendly interfaces that make it simple to access and use personalized health information, goal monitoring and health comparisons of themself. In order to create a secure, scalable, and highly user interactive that enables users to efficiently manage their general health, the solution will be created in an Agile environment using tools like TensorFlow Lite, Firebase, and Kotlin as the primary of programming language in this development
Leveraging artificial intelligence in modern supply chains
Get PDFThis project addresses the challenge of last-mile delivery delays caused by urban traffic congestion by creating a smart route optimization system that combines the traffic prediction with classical pathfinding. A synthetic dataset was generated to simulate urban traffic flows, and a Long Short-Term Memory (LSTM) model was trained to forecast short-term congestion patterns. These predictions were converted into congestion factors and applied as dynamic weights within Dijkstra’s algorithm to compute adaptive delivery routes. A Streamlit-based dashboard was designed to visualize model performance, predicted traffic conditions, optimized routes, and system-level evaluations in a simulated real-time environment. Evaluation results demonstrated that the LSTM model achieved reliable short-term forecasts, outperforming a baseline by more than 25% in error reduction, while the congestion-aware routing consistently avoided heavily congested edges. The prototype validates the feasibility of combining predictive analytics with graph-based optimization, offering a practical foundation for enhancing efficiency and reliability in last-mile logistics operations
Online learning management system with advanced quiz and marking system
Get PDFThis project aims to developing an Online Learning Management System (LMS) With Advanced Quiz and Marking System which focuses on improving the efficiency on making quizzes, grading, and feedback easier and better. Many current LMS platforms like Moodle, Google Classroom, and Frappe LMS help with teaching and assessments, but they still have some weaknesses. This project is going to compares those platforms, takes their best parts and tries to addressing their limitations. This proposed system is built by using ASP.NET Core and includes features like an easy-to-use quiz builder, auto-grading for objective questions, a real-time feedback for students, and a manual grading option with rubrics for essay questions and assignment. Thus, this system hopes to make online teaching and learning better for both teachers and students by saving their time and improving assessment quality
Job qualifications affecting employability among private university students
Get PDFThis study examines job qualifications affecting employability among students from four Malaysian private universities: TARUMT, Segi University, Help University, and Nilai University. Employability is a key benchmark of higher education effectiveness, reflecting how well institutions prepare graduates for today’s competitive job market. The research focuses on four independent variables—academic performance, communication skills, technical skills, and self-efficacy—and their impact on employability. Data were collected through a structured questionnaire and analyzed using descriptive, reliability, normality, and regression techniques in SPSS 26.0. The results show that communication skills, technical skills, and self-efficacy significantly enhance employability, while academic performance has only marginal influence. Self-efficacy emerged as the most influential predictor, highlighting the importance of confidence, adaptability, and resilience. These findings suggest that employers prioritize practical skills and personal attributes over grades, underscoring the need for universities to integrate soft skills training, technical exposure, and personal development into their programs. The study contributes to literature by focusing on private universities, which remain underexplored compared to public institutions. It also stresses the importance of stronger industry–academia collaboration to close skills gaps. Future research should include additional factors such as digital literacy, internships, and networking, using mixed methods for deeper insights. Keywords: Employability; Academic Performance; Communication Skills; Technical Skills; Self-Efficacy Subject Area: HD5701–6000.9 Labor market. Labor supply. Labor demand (Including unemployment, manpower policy, occupational training, employment agencies
Vehicle maintenance and service tracking system
Get PDFThis report details the development of the Vehicle Maintenance and Service Tracking System (VMTS), designed to address inefficiencies in traditional vehicle service operations in Malaysia. Manual appointment scheduling, paper-based records, and offline communication methods often lead to errors and delays, which negatively impact customer satisfaction. The VMTS is a web-based solution that automates service bookings, digitizes maintenance records, and provides real-time tracking of service progress, ensuring greater accuracy and convenience for both vehicle owners and administrators. A comparison of existing systems, such as those offered by Toyota Malaysia, Proton ProCare, Audi Malaysia, and Mazda Malaysia, reveals common shortcomings such as the lack of real-time updates and limited customer engagement. To overcome these challenges, VMTS integrates key functionalities such as appointment scheduling, predictive maintenance alerts, automated service reminders, live chat support, feedback and rating systems, and an administrator dashboard with interactive data visualizations. The system was developed using a prototyping methodology, allowing for iterative improvements based on user feedback. The system serves two main user groups: vehicle owners, who can book appointments, track service progress, view historical records, and communicate with service providers; and administrators, who manage appointments, user accounts, services, and feedback through a centralized control panel. This approach ensures the system meets the specific needs of both user groups. The areas of study for this project include Vehicle Maintenance Management and Web Application Developmen
Patient appointment scheduling system
Get PDFThis is a development-based project, and the area of study is Information System.
This project focuses on building a Patient Appointment Scheduling System for
academic purposes. This project is a platform to let patients make appointments for
consultation and treatment, while also enables doctors to manage the appointment
schedule more efficiently. Furthermore, providers who sign up for the system will
be able to add their doctors. The system handles typical problems such as unclear
appointment status, fragmented appointment process, and limited provider
accessibility. Patients are allowed to view the profiles of the doctors before
choosing their preferred and most suitable time slots, ensuring a smoother and
more transparent appointment procedure. This is due to the fact that the system is a
one-stop solution, which prioritizes user-friendliness and improves the overall
experience for both patients and healthcare providers. The technologies that will be
used in the system include Visual Studio 2022 ASP.NET Core (C#), and Microsoft
SQL Server. The system investigates color classification methods to clearly
represent appointment statuses in order to further enhance visualization clarity and
avoid misunderstanding
Factors affecting cybersecurity awareness among students in comprehensive private universities in the northern region of Malaysia
Get PDFThe rise in cyberthreat cases among Malaysian university students highlights a pressing need to understand the factors influencing cybersecurity awareness. While prior studies have largely focused on computer science students or in workplace settings, limited research has examined cybersecurity awareness across diverse academic backgrounds in Malaysian comprehensive private universities. This study addresses this gap by investigating the impact of gender, student knowledge, social media, and password security management on students’ cybersecurity awareness in the northern region of Malaysia. A quantitative research design was adopted and employed a structured questionnaire distributed to 381 students from seven comprehensive private universities in this region. Data were analysed using descriptive statistics, reliability test, Pearson Correlation, Independent-samples Ttest, and Multiple Regression Analysis via SPSS 26.0. The results revealed that student knowledge, social media, and password security management had significant relationships with cybersecurity awareness, whereas gender differences showed no statistically significant effect. Among the significant variables, student knowledge emerged as the strongest determinant of cybersecurity awareness levels. The findings hold practical value for universities, policymakers, and government by providing evidence-based insights and enhancing digital safety education. Integrating targeted cybersecurity training into academic programmes and leveraging social media as an educational tool can foster a more security-conscious student community. This study contributes to the existing research by focusing on a less-explored student demographic in Malaysia, offering a comprehensive understanding of behavioural and knowledge-based factors that influence cybersecurity awareness. The outcomes not only fill a research gap but also support the development of strategic initiatives to strengthen the nation’s resilience against cyber threats. Keywords: Cybersecurity awareness, student knowledge, password security management, social media, gender Subject area: Q300-390 Cybernetic