UTAR Institutional Repository (Universiti Tunku Abdul Rahman)
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Parenting styles (authoritarian and authoritative) and childhood traumatic experiences as predictors of emotion regulation among young adults in Malaysia
Get PDFThe insufficient ability of emotional regulation is a rising concern among Malaysian young adults. This has led them to a level of vulnerability when facing stressful events, where they struggle with mental health issues and suicidal attempts. Therefore, the present study examines the predictive role of parenting styles (authoritative and authoritarian) and childhood traumatic experiences on emotion regulation among young adults in Malaysia. A quantitative, cross-sectional design was employed to study 145 Malaysian participants, aged 18-29, recruited for the study. Parenting Style Questionnaire, Childhood Trauma Questionnaire-Short Form (CTQ-SF), and Difficulties in Emotion Regulation Scale-16 (DERS-16) were utilised to measure the variables of the study. Multiple linear regression analysis revealed that authoritarian parenting (β = −.220, p < .001) and childhood trauma (β = −.271, p = .012) significantly predicted poorer emotion regulation. However, authoritative parenting (β = −.051, p = .586) was not significantly associated with emotional regulation. The findings align with Social Cognitive Theory, emphasising how early environmental influences shape emotional self-regulation. To take note, authoritarian parenting's negative impact may also be exacerbated in Malaysia's collectivist culture, where emotional suppression is reinforced. Understanding these factors may be beneficial for developing effective interventions, including parenting workshops to reduce punitive practices and school-based programs to enhance emotional awareness. Limitations of the study include gender and ethnic imbalances in the sample (95.5% Chinese; 68.3% female) and reliance on cross-sectional data. Recommendations for future studies were suggested to improve generalizability and avoid possible bias
Fertilizer types on corn yields (zea mays l.) in Kampar, Perak
Get PDFThe global population is increasing, and optimizing food production has become a crucial way to address this challenge. Sweet corn (Zea mays L.) is a staple crop because it is both nutritious and affordable. However, in Malaysia, sweet corn production has not been self-sufficiency due to a paucity of knowledge in fertilizer management. This study investigated the effects of NPK 12:12:17, calcium boron (Ca/B), and biostimulant on the yield and anthesis timing of different sweet corn varieties commonly grown in Kampar, Perak, Malaysia. A split-plot design was done in UTAR Agriculture Park B, with two varieties, which were King Corn F1 316 and Sweetcorn King Raja SC 9001, and five different treatments: control (no fertilizer), NPK 12:12:17, NPK 12:12:17 + Ca/B, NPK 12:12:17 + biostimulant, and NPK 12:12:17 + Ca/B + biostimulant. The corn variety of F1 316 performed statistically significantly better than SC 9001 for cob’s fresh weight (g), cob length (cm), cob girth (cm), and the number of kernels per column. Both varieties showed statistically insignificant in 100-kernel weight (g). For fertilizer treatment, NPK 12:12:17 was statistically significant than control in producing a heavier 100-kernels weight (g). Moreover, a significant interaction effect was observed between variety and fertilizer type for 100-kernel weight (g), with the combination of variety F1 316 and NPK 12:12:17 yielding the heaviest kernels (g). These highlights the importance of distinguishing between apparent yield (whole cob) and actual yield (only kernels), suggesting that farmers should prioritize the corn varieties and types of fertilizer that may lead to a higher actual yield. NPK 12:12:17 can improve corn yield, while Ca/B supplementation is essential when deficiency is detected, and glycine-based biostimulants are beneficial when plants experience drought stress. Future studies should investigate how different fertilizer types affect the quality traits (i.e., shelf-life, physicochemical properties and nutrient content) of sweet corn
Association between ultra-processed food consumption and sleep quality among students in Universiti Tunku Abdul Rahman (UTAR)
Get PDFThis cross-sectional study investigated the association between ultra-processed food (UPF) consumption and sleep quality among 324 undergraduate students at Universiti Tunku Abdul Rahman (UTAR) using the NOVA food classification system. NOVA categorizes foods into four groups based on processing level: unprocessed or minimally processed foods (UMPF), processed culinary ingredients (PCI), processed foods (PF), and ultra-processed foods (UPF). UPF are industrial formulations containing multiple artificial additives and minimal whole foods, typically high in refined sugars, unhealthy fats, and sodium including items like instant noodles, packaged snacks, and sugar-sweetened beverages. Using the NOVA classification system and the Pittsburgh Sleep Quality Index (PSQI), we assessed dietary patterns and sleep quality. Results revealed that UPF contributed a median of 521.84 kcal (27.87%) in daily caloric intake, which is the second largest contributor. A significant majority (n=221, 68.2%) of students exhibited poor sleep quality (PSQI > 5). Spearman’s correlation analysis demonstrated a moderate positive association between UPF consumption and poorer sleep quality (ρ = 0.48, p < 0.001), with female students reporting significantly worse sleep than males (p = 0.044, <0.05). Key mechanisms linking UPF to sleep disturbances included gut-brain axis dysregulation, systemic inflammation, and neurotoxic additives. Despite limitations such as self-reporting bias and a predominantly Chinese sample, this study highlights UPF as a modifiable risk factor for sleep disorders in Malaysian university students. Interventions targeting dietary habits and sleep hygiene are recommended to improve student well-being
Cybersecurity digital consultant
Get PDFCybersecurity Digital Consultant (CDS) is GPT-based digital assistant designed to generate and design solutions for networking and cybersecurity in diverse IT environments. As network threats evolve and the complexity of cybersecurity paradigms, platforms, tools, and stacks increases, ensuring robust protection for on-premises infrastructure becomes a formidable challenge. CDS is a multi-agent LLM designed to automate the workflow of deploying cybersecurity solutions. CDS define agents involved throughout the deployment pipeline, such as security analyst, cybersecurity consultant and penetration tester, each with a specialized function to deal with different problems. Each individual agent possesses distinct traits, personalities, responsibilities, and roles. A dedicated storage system will be established for each agent to archive their historical data, aiding in future output generation. Inter-agent communication enables a cohesive response to threats by collaboratively generating comprehensive solutions and network designs tailored to the user’s network environment. CDS provides a user-friendly, context-aware approach to protecting networks against an evolving threat landscape
Inventory system for retail using blockchain technology
Get PDFThis project is to create an inventory system that would be reliable, secure and operate in a private blockchain setting. Not all conventional methods for inventory management face issues, but they nevertheless have shortcomings that include unclear data presentation, confusing users and inefficient processes, applicable to some methods for inventory control. This work presented blockchain solution as a technological implement to solve the challenges outlined. The project involves a web application development strategy. The system is developed with a mindset that embraces security, scalability, interoperability, performance and user friendliness. Key functionalities include Product Registration, Inventory Tracking and Data Visualization. The system interacts with the chosen blockchain network through smart contracts and stores the data on the blockchain. The project successfully showed the creation of a fully working blockchain-based inventory management system prototype. The system endorses improved security via unalterable data storage and safeguarded transactions on the blockchain. High transparency level makes it possible to monitor the entire supply chain in real time. Moreover, the system achieves efficiency by automating tasks and making inventory management processes easier. The research suggests that in future inventory management procedures, blockchain technology can be a key element of revolution. The developed platform supplies many advantages to businesses like higher trust, operational efficiency and low costs. Along with the investigation of the possibility of implementing advanced functionalities such as disruptive analyzes, decentralized marketplaces
Predictive risk assessment credit scoring using supervised learning
Get PDFThis study explores the application of supervised learning models within credit scoring, aiming to revolutionize risk assessment in lending decisions. The primary goal involves comparing these advanced methodologies against conventional credit assessment techniques to ascertain their effectiveness in determining creditworthiness. In response to the escalating complexity of financial transactions and the wealth of available data, this research seeks to elevate the precision and efficiency of credit risk evaluation. Supervised learning, known for its ability to learn from labelled datasets, presents an opportunity to redefine credit scoring by leveraging historical credit information.
The core focus is on assessing the predictive capabilities of supervised learning algorithms—specifically Logistic Regression, Random Forest, K-Nearest Neighbours, Support Vector Machines and Gradient Boosting—against established credit scoring methods. By harnessing the power of these modern techniques and analysing intricate credit patterns, this research endeavours to deliver more accurate credit risk assessments. It strives to surpass the existing industry norms by using machine learning models to refine credit evaluation processes
Rock-paper-scissors game using real-time object detection
Get PDFThis project introduces an innovative Rock-Paper-Scissors (RPS) game that integrates real-time hand gesture recognition within a Flutter-based mobile application, leveraging advanced machine learning techniques. Utilizing MobileNetV2, a lightweight convolutional neural network, the system reliably classifies rock, paper, and scissors gestures from live camera feeds. Developed through an evolutionary prototyping methodology, the project iteratively refined a TensorFlow Lite-deployed model and a user-friendly interface featuring tutorial screens, game history tracking, and celebratory animations. OpenCV ensured robust dataset preprocessing, enabling high-quality training data, while Flutter facilitated seamless cross-platform performance. Extensive testing confirmed the system’s effectiveness across diverse lighting conditions and device specifications, achieving consistent gesture detection and rapid UI responsiveness. By addressing challenges such as gesture variability and real-time processing latency through model optimization and efficient camera handling, the project delivers an immersive gaming experience without physical controllers. This work advances interactive gaming by demonstrating the feasibility of deploying sophisticated machine learning models on resource-constrained mobile devices. The framework offers potential for applications in educational tools and assistive technologies, contributing to further developments in computer vision and human-computer interaction
Physical to digital services profiling app
Get PDFIn this project, a blockchain-based e-commerce system is proposed to transform the
online marketplace by addressing key challenges found in traditional e-commerce
platforms. This project’s main objective is to eliminate middlemen or intermediaries to
streamline transactions, reduce transaction costs, and enhance transparency and trust
between buyers and sellers. By utilizing blockchain technology, the system provides
decentralized transaction management, user identity verification, and product
authenticity. This will provide a secure and efficient platform for e-commerce activities.
On the other hand, the user interface is designed to be intuitive and user-friendly to
facilitate seamless interaction with the smart contract functionalities. This system will
not only redefine the e-commerce experience but also aligns with the sustainable
practices by optimizing logistics and minimizing environmental impacts caused by ecommerce
activities. This project aims to create an innovative e-commerce platform
that is cost-effective, secure and environmentally responsible, which will enhance the
user satisfaction and introducing a new era of digital commerce
Financial distress detection using ensemble learning
Get PDFFinancial distress prediction is a crucial role, as an “early warning” for a company to address with the financial risk including restructuring the financial strategies and managing the operating costs effectively. Over time, several approaches have been developed for financial distress predictions, which are methods based on the financial ratios, single classification model and ensemble learning. However, few challenges have been found out from the previous approaches such as the imbalance datasets, limitations on the financial ratios and the auditor biases on selecting financial ratios. In this thesis focuses on ensemble learning are known to capture large and complex datasets and provide more robust result. The aim of the project is to identify the optimal ensemble learning technique in detecting financial distress risk
Use AI to detect defect pin in electrical connector
Get PDFThis project aims to develop an intelligent inspection model capable of detecting defects in small electrical connector pins, which are critical components in many electronic systems. The work is structured into two primary components: data preparation and model development. In the data preparation phase, a custom dataset will be generated, featuring images of electrical connectors with three common types of pin defects: missing, shifted, and rotated pins. High-quality image data is essential for accurate model training and reliable detection outcomes. The model development phase leverages the YOLOv8 object detection algorithm, selected for its balance of speed and accuracy in real-time applications. Image processing techniques are employed to enhance dataset quality, and the dataset is annotated manually to ensure precision in model training. Performance evaluation will be conducted using several key metrics—accuracy, recall, precision, and F1 score—to assess the model's capability in identifying defective pins effectively. This project ultimately seeks to offer a practical and automated solution for improving quality control in electrical connector manufacturing processes, reducing the need for manual inspection and minimizing human error