UTAR Institutional Repository (Universiti Tunku Abdul Rahman)
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Parental psychological control and subjective well-being in school among Malaysian adolescents: Social competence as a mediator
Get PDFAdolescence represents a critical developmental phase in which parenting practices and social functioning significantly influence overall well-being. Parental psychological control (PPC) is frequently associated with adverse outcomes; however, its impact may differ based on cultural context and the social resources available to adolescents. Hence, this study investigated the mediating role of social competence (SC) in the relationship between PPC and adolescents' subjective well-being in school (SWBS) in Malaysia. A quantitative, correlational, and longitudinal design was employed, with self-administered questionnaires administered in two phases. Multistage cluster random sampling produced a final sample of 277 Malaysian secondary school students (M = 14.84 years, SD = .74; 46.2% males, 53.8% females) from five states in Malaysia. Instruments included the Parental Psychological Control Scale-Youth Self-Report, the Perceived Social Competence Scale, and the Brief Adolescents' Subjective Well-Being in School Scale. Results indicated that PPC did not directly predict SWBS but was positively associated with SC, which in turn positively predicted SWBS. Mediation analysis confirmed that SC fully mediated the PPC and SWBS relationship, suggesting that PPC influenced adolescents' SWBS only indirectly through its effect on SC. These findings contribute to the growing literature by extending the understanding of PPC and adolescents’ SWBS in the Malaysian cultural context. Practical implications include integrating social-emotional learning programs in schools, guiding parents toward less controlling strategies, and fostering family-school partnerships to enhance adolescents' well-being in the Malaysian context
Determinants of private health insurance adoption in Klang Valley
Get PDFPrivate Health Insurance (PHI) plays an increasingly important role in Malaysia’s dual healthcare system, providing an alternative to public healthcare services that often suffer from overcrowding and long wait times. Despite the rising cost of healthcare and growing demand for timely and high-quality services, PHI adoption rates in Malaysia remain stagnant. This study investigates the determinants influencing the adoption of PHI among residents in Klang Valley, a densely populated urban region with high access to private healthcare facilities and insurers. The study adopts a quantitative research design, surveying 385 residents aged between 22 and 60 through structured questionnaires. Five categories of independent variables were examined: demographic factors, socioeconomic status, health variables, financial and insurance knowledge, and awareness and accessibility. The study employed descriptive statistics, reliability testing, and multiple linear regression analysis using SPSS to determine the influence of these variables on PHI adoption. Grounded in the Health Insurance Demand Theory and the Theory of Planned Behaviour, the findings suggest that income, education, health condition, insurance literacy, and access to information significantly impact individuals' decisions to purchase PHI. Moreover, behavioural elements such as trust in insurers, perceived affordability, and exposure to health insurance information were found to shape purchasing behaviour. This research contributes to the academic discourse by integrating a multi-dimensional framework and provides practical insights for policymakers, insurers, and healthcare providers seeking to improve PHI uptake. Keywords: Health Economics / Insurance Behaviour / Public Policy Subject Area: RA410–410.9 Medical economics. Economics of medical care. Employmen
The moderating role of Malaysian and Indonesian palm oil production in the carbon emissions-income nexus
Get PDFThis study is to examine whether the Environmental Kuznets Curve (EKC) hypothesis exist in Malaysia and Indonesia, and the palm oil production moderate the relationship between economic growth and CO2 emission in Malaysia and Indonesia. Our dependent variable is carbon dioxide emission per year. The independent variables are GDP and palm oil production, and our control variables are primary energy consumption, trade and foreign direct investment. By applying the ARDL approach, our results reveal two notable findings. First, the EKC hypothesis is not supported in Malaysia due to increasing scale of industrialization in higher income level. while it is supported in Indonesia due to adoption of more environment friendly technologies when economy further grows. Second, palm oil production reduces CO2 emissions but the interaction terms between palm oil production and GDP bring positive impact to CO2 emissions. Therefore, this study suggests that palm oil production does not moderate the impact of economic development on environmental degradation. When economy continues to grow, the increasing scale of industrialization on palm oil production leads to higher emissions. Malaysia is suggested that government can provide supports in terms of loan and adopt tracking technologies, while Indonesia can invest in technologies that can convert palm oil residues to bioenergy to support EKC hypothesis. For our second findings, sustainable palm oil practices and technologies based on palm oil are suggested for both countries
One-class classification for ginger plant growth monitoring
Get PDFThis project introduces a ginger plant monitoring system, that trace the ginger plant growth
from week 1 to week 20 which is the lifespan of the ginger plant growth in yield. I present a
anomaly detection framework for ginger plant growth monitoring, integrating precise
segmentation via YOLOv8n-seg with a prototypical few-shot learning model for one-class
classification. To address the challenges of limited labelled anomalous data and environmental
variability, the system focuses on learning representations of healthy plant growth and
identifying deviations without the need for extensive anomaly datasets. The preprocessing
stage isolates plant regions to reduce background interference, enhancing feature extraction for
the classification task. The prototypical network, trained exclusively on normal samples,
enables anomaly detection by measuring feature space distances to learned prototypes,
facilitating the identification of subtle growth irregularities such as stress or disease. A Python
Flask-based deployment platform allows for both manual image uploads and real-time
monitoring, providing immediate feedback for agricultural interventions. Experimental results
demonstrate that the proposed method outperforms traditional convolutional neural network
classifiers and unsupervised clustering approaches in terms of robustness and accuracy under
varying environmental conditions. This work contributes a scalable, efficient, and practical
solution for improving crop yield, reducing labour dependency, and advancing sustainable
precision agriculture
Evaluate the performance of university course timetabling problem with different combinations of genetic algorithm
Get PDFUniversity course timetabling problem (UCTP) is a scheduling problem that requires courses
to be assigned to the limited time slots, classrooms, and lecturers, while adhering to a set of
predefined constraints. Due to the effectiveness of genetic algorithm (GA) in optimisation
problems, it has been widely discussed in numerous research to address UCTP. Nonetheless,
the performance of GA in terms of operation techniques has not been studied enough, as the
researchers have often focused on using a single GA combination or hybrid approaches to solve
UCTP case studies. Therefore, this project aims to analyse the performance of different
combinations of GA operation techniques and identify the best GA model. A flexible GA
framework is developed, which allows alternative techniques to be integrated and executed
easily. 64 combinations, involving 4 selection, 4 crossover, 1 mutation, and 4 replacement
techniques, are evaluated on a partial mock dataset. In addition, this project proposes a new
soft constraint, which requires consecutive classes for a student to be held in the same building.
This constraint targets to reduce students’ travel distance, thus producing a more student
friendly timetable. Experimental results shows that GA44 model which comprises of binary
tournament selection, uniform crossover, swap mutation, and weak chromosome replacement
is the best GA combination. In conclusion, the proposed constraint demonstrates clear benefits
to student experience on campus and offers a fresh idea for future research with alternative
approaches
Moral value affects purchase intention of sustainability clothes: A survey on Selangor’s university students
Get PDFIn recent years, the increasing of the awareness about environmental issues has increased consumers' interest in sustainable fashion choices. Among the Malaysia university students particularly those in Selangor, the purchase intention of sustainable clothing is influenced by various factors. This study will ground in the Theory of Planned Behavior and tends to investigate the effect of environmental concern, social media influence, and perceived financial affordability on purchase intention for sustainable clothing, with moral value introduced as a mediating variable. Data were gathered quantitatively from 200 students via online surveys, and descriptive and multiple regression analyses were performed using IBM SPSS Statistics 30. The findings show that the three independent variables are significantly affect the purchase intention. Moral value plays a partial mediating role between perceived financial affordability and purchase intention and fully mediates the effects of environmental concern and social media influence. These results highlight that the importance of integrating moral considerations when encouraging sustainable purchase behavior. Keywords: Sustainable Fashion, Purchase Intention, Moral Value, Environmental Concern, Social Media Influence, Perceived Financial Affordability, Theory of Planned Behavior Subject Area: HF5410-5417.5 Marketing, Distribution of product
Deep Learning Based image segmentation for expensive soil desiccation crack recognition and qualification
Get PDFExpansive soils undergo significant volume changes due to moisture fluctuations, which lead to desiccation cracks formation that affect soil properties and engineering performance, compromising the safety of geo structures. The analysis of these cracks was essential for mitigating their
impact; however, traditional quantification methods were labour intensive and imprecise, highlighting the need for more robust and automated techniques. This study investigated the feasibility and effectiveness of image-based techniques using advanced deep learning algorithms to quantify desiccation cracks in expansive soils. The objectives of the study included designing soil desiccation experiment setup for desiccation crack image acquisition, evaluating crack imaging analysis based on deep learning algorithms, and
quantifying desiccation cracks through image processing techniques. Laboratory experiments were conducted using a custom-built image acquisition tool to capture crack images under simulated soil desiccation conditions. Crack images obtained were processed and annotated to produce a
dataset of 820 images for the training and testing of deep learning models. Deep learning models, including U-Net, Res-UNet, and DeepLabv3+ with pre-trained backbones such as MobileNetV2, ResNet-18, ResNet-50, and Xception, were trained and evaluated along side a traditional Otsu's thresholding method as the baseline for crack detection and segmentation. The evaluation considered segmentation performance using evaluation metrics (precision, recall, F1 score, IoU), computational efficiency, and crack geometrical parameters quantification (surface crack ratio, crack width, crack
length, and crack segment). Results demonstrated that DeepLabv3+ variants consistently outperformed other methods, with MobileNetV2 backbone offering the best balance of computational efficiency, segmentation accuracy,
and robustness across case-wise performance conditions. Compared to traditional approaches, deep learning models, particularly with DeepLabv3+ variants, produced more reliable crack segmentation masks, thus enabling more accurate quantification of crack geometrical parameters, as demonstrated by lower error rates. This study validates the effectiveness of deep learning based segmentation methods for automated soil crack recognition and quantification, contributing to engineering applications with improved
methodologies for analysing desiccation behaviour in expansive soils.
Keywords: Civil engineering, Photographic processing, Quantitative methods, Automation, Deep Learnin
Development of a stationary upper and lower extremities rehabilitation system
Get PDFRehabilitation for individuals with upper and lower limb impairments such as those resulting from stroke, spinal injuries, and neurological conditions remains a prolonged, resource-intensive process. Traditional therapy methods often rely heavily on the availability of skilled therapists and in-person clinical visits, leading to high treatment costs, inconsistent therapy sessions, and limited access,
especially in low- and middle-income regions. Furthermore, current robotic rehabilitation systems are often prohibitively expensive, and many lack the flexibility to accommodate both upper and lower extremity rehabilitation in a single platform. These gaps highlight a critical need for a cost-effective, accessible, and versatile rehabilitation system that delivers programmable, repeatable, and safe therapeutic exercises. To address these challenges, this
project presents the development of a stationary rehabilitation system designed to support the rehabilitation of both upper and lower limbs. The system utilizes stepper motors, linear actuators, and interchangeable limb supports to facilitate joint movements, while an ESP32 microcontroller enables system control and IoT connectivity. An MPU6050 sensor module (accelerometer and gyroscope) was integrated for real-time monitoring of acceleration and angular velocity
during rehabilitation exercises. The prototype was fabricated using CNC machining and 3D printing and tested on eight healthy participants performing exercises at different speeds and across varied range of motion (ROM) profiles for both elbow and knee joints. Validation of motor rotational speed showed a low overall error of 2.82% across high (29.25 °/s) and low (13.50 °/s) speeds. ROM evaluations confirmed system repeatability, with a 4.75% average difference across varying speeds. Joint motion tracking using the MPU6050 sensor showed an average percentage error of 8.79% when validated against a
Trigno IMU, demonstrating acceptable accuracy with slight variations depending on movement dynamics. In addition to ROM and motion tracking, dynamic kinematic data (acceleration and angular velocity) were captured for real-time motion analysis and rehabilitation performance assessment. The complete system was developed at an estimated cost of RM1600, providing a significantly lower cost alternative to commercial robotic rehabilitation devices. This project demonstrates promising potential to deliver accessible, quantifiable, and remotely monitored therapy for upper and lower extremity impairments, suitable for use in clinical settings and at home.
Keywords: Rehabilitation, Biomechanics, Upper Extremities and Lower Extremities, Extension and Flexion, Accelerometer and Gyroscope.
Subject Area: R856-857 Biomedical engineering. Electronics. Instrumentati
Deep learning for histopathological image cancer detection
Get PDFHead and neck cancers (HNC) are among the most prevalent cancers globally, with high mortality and poor prognosis often resulting from late-stage diagnoses. However, diagnostic difficulties are compounded by the histological complexity of HNCs and the subjective nature of manual histopathological analysis, which is prone to human error and inter-observer variability. Therefore, this study proposed a deep learning approach to assist in the classification of HNC from histopathological whole slide images, aiming to improve diagnostic accuracy and reduce observer bias. This study adopted the Head and Neck Squamous Cell Carcinoma dataset from the Clinical Proteomic Tumor Analysis Consortium,
which consists of 390 whole slide images from various head and neck cancer sites, including 122 benign and 268 tumor slides. Convolutional neural network (CNN) models were trained using a transfer learning strategy, incorporating
variants from the DenseNet, EfficientNet, MobileNet, ResNet, and VGG families. These models were fine-tuned using pre-trained weights and further evaluated for classification performance at three magnification levels (1.25×, 2.5×, and 5×). The top-performing CNN models were then combined using
ensemble learning techniques to improve overall accuracy and robustness. The ensemble approach, particularly the majority voting with five models ensemble, outperformed individual models, achieving an accuracy of 96.09%, along with
improved performance in sensitivity, precision, and F1-score. Visual interpretability tools, such as Gradient-weighted Class Activation Mapping, were employed to provide insights into the models' decision-making processes, enhancing the transparency and trustworthiness of the artificial intelligence predictions. The study also compared the CNN-based models to Vision Transformer models, showing that CNN ensembles achieved superior performance in classification tasks. This research highlights the potential of deep learning, particularly ensemble methods, in histopathological image analysis, with significant applications in computer-aided diagnosis for cancer detection. Further work should focus on addressing class imbalance, integrating the models into a clinical pipeline, and exploring multimodal learning to enhance model performance and clinical applicability.
Keywords: deep learning, convolutional neural networks, ensemble learning, whole slide image, head and neck cancer,
Subject Area: Q300-390 Cybernetic
An integrated web application for efficient restaurant management
Get PDFObesity is a condition characterized by an excess accumulation of body fat which is a major health problem prevalent in millions of people across the world and there is a consistent rise in prevalence in both adults as well as youths. In this project we propose the creation of a Personalized Nutrition and Obesity Intervention System which is investigated by providing the user with personalized diet options based on personal profiles in Mobile Application. This app is made with a primary focus on personalized nutrition, in contrast to many other apps that consider nutrition as a secondary feature. Some of its features include a menu that can be adjusted depending on the end user, features that allow users to monitor their health and features that help to promote engagement and continued use of the application. With the help of an integrated smart nutrition calculator in the mobile application, users may input personal information like age, weight, gender, activity level, and health goals to receive personalized recommendations for calorie intake. The dietary planner ensures that consumers may modify their meal plans as their health needs change by providing flexible customization based on dietary choices, allergies, and real-time feedback. There is also a diet and health option on the software where the user can record the food they take and other health aspects such as weight and Body mass index (BMI). Charts and graphs are used to show users daily activity and goals progress. Like in most mobile applications, motivational features such as daily check-ins, challenges, and point-based reward systems present in the app to enhance the users’ experience. The basic features of such applications are returning points to the activity in hope of receiving vouchers or discounts to enhance retention. In addition, multilingual support ensures that the users with different languages and culture can easily access the application thus allowing it to be used by a global population of users. Java was the main programming language used in Android Studio. To ensure its reliability and scalability other tools such as Git version control and XAMPP control panel were used for backend services. The project addresses several of the fundamental limitations of current mobile health solutions, such as the difficulty of sustaining long-term good eating habits and the lack of customization in obesity intervention tools. Through the aid of the app, the target is to fill a gap in the market by offering specialized nutrition which will empower and encourage user to achieve their desired wellness and health in nutrients