UTAR Institutional Repository (Universiti Tunku Abdul Rahman)
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Development of a green self-powered sensor
No full textThe rapid expansion of the Internet of Things (IoT) has fueled a growing demand for high-performance sensors that are not only efficient but also sustainable. This demand has accelerated the development of sensors that are compact, lightweight, and environmentally friendly. Traditional sensors, which are typically rigid and reliant on bulky power sources, no longer meet the needs of modern, sustainable technologies. Simultaneously, there has been a significant shift toward renewable energy and energy harvesting technologies. In this context, this project explores the use of Triboelectric Nanogenerators (TENG) as a cutting-edge solution to replace conventional sensors. TENGs leverage the triboelectric effect to convert mechanical energy into electrical energy, enabling them to operate as self-powered sensors. The device functions through the contact separation mechanism between two materials— polyvinyl chloride (PVC) and paper—producing a potential difference that serves as the electrical signal. This approach eliminates the need for external power sources, aligning with the principles of sustainability and energy efficiency. Moreover, the project emphasizes the use of green materials, designing the sensor for reuse, easy disassembly, and remanufacturing. Recyclable materials are prioritized, with Arabic gum-graphite composite being utilized as a key component for the electrode due to its rewettable and reusable properties, allowing it to be shaped and reshaped as needed. The fabricated sensor is then applied in a human-machine interface (HMI) context, specifically in controlling the movement of a paddle in a ping-pong game. The sensor enables precise control, allowing the paddle to move left and right, while the game score is displayed in real-time on the Blynk IoT platform. This integration of a self-powered sensor into an IoT application demonstrates the potential of TENG technology in creating sustainable, high-performance systems that meet the evolving needs of IoT-driven innovations
Intrusion detection system (IDS) using machine learning
Get PDFIntrusion Detection Systems (IDS) play a critical role in safeguarding organizational networks by identifying potential threats and anomalies. However, traditional IDS approaches often suffer from high false alarm rates, leading to unnecessary alerts for administrators. This research focuses on enhancing machine learning and deep learning-based IDS models to improve accuracy, precision, recall, and F1 score, ultimately aiming for a more balanced and effective performance. The study leverages the CIC-IDS2017 dataset for model training, employing Random Forest (RF), Deep Neural Network (DNN), and Deep Autoencoder (DAE) architectures. A rigorous pre-processing phase, including data cleaning and feature selection using Pearson’s Correlation, enhances the dataset's quality and relevance. Subsequently, the refined dataset undergoes model training and testing. Hyperparameter tuning, facilitated by grid search, fine-tunes key features to optimize model performance. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to assess the models' efficacy in binary and multi-class classification tasks. Results demonstrate significant improvements and balanced performance compared to previous research models, achieving an average performance of 99.5% across all models
Investigating language learning strategies (LLS) employed by ESL undergraduates in enhancing speaking skills in Universiti Tunku Abdul Rahman
Get PDFThis study investigated language learning strategies (LLS) employed by ESL undergraduates to enhance speaking skills. Around 30 participants from the four faculties of the Faculty of Business and Finance (FBF), Faculty of Arts and Social Science (FAS), Faculty of Science (FSc) and Faculty of Engineering and Green Technology (FEGT) who study at Universiti Tunku Abdul Rahman (UTAR) were involved in this study. This study adopted a quantitative design that employed the Oxford SILL questionnaire (1990). The result indicated that cognitive and compensation are the most preferred language learning strategies among the participants of the four faculties regardless of gender. There is a high significance level for the employment of affective strategies regarding the gender variable. As for good and poor proficiency learners, it has been shown that cognitive and compensation strategies have high employment frequency among both groups. However, the statistical analysis did not show that language proficiency significantly influences the employment of LLS. The results of the study have implications for modifying second-language pedagogy. It emphasises the need to enhance language learners' knowledge of the methods, so they are encouraged to employ more appropriate LLS at different stages of learning their second language. Aside from that, it raises awareness among language teachers on the importance of learning methods for language learners, as well as the impact of elements such as gender and degree of competence in the learner's choice of LLS as well as individual disparities among language learners in a learner-centered classroom
Network intrusion detection and alert system
Get PDFNetwork security has become a critical concern for organizations worldwide as
traditional security measures struggle to keep pace with the rapidly evolving landscape
of cyber threats. This project aims to develop an intelligent and comprehensive network
intrusion detection and alert system (NIDAS) to enhance network security and provide
real-time threat mitigation. NIDAS is security technology that enabling security
administrators to identify any abnormal or malicious network traffic in real-time.
NIDAS will consist of several key components, including deep network traffic packet
inspection, behavior analysis, a prevention rules intrusion detection engine, and an alert
prioritization and visualization module. The system will be trained on labeled datasets
to identify various types of network attacks and anomalies. By employing a multilayered
approach, NIDAS will be capable of detecting both known and unknown
threats, ensuring comprehensive protection against various attack vectors.
The intrusion detection component will utilize a combination of signature-based and
anomaly-based detection techniques. Signature-based detection compares network
traffic packets with a real-time updated database of known attack patterns, while
anomaly-based detection algorithms learn normal behavior patterns and identify
deviations. This dual approach will enable the system to effectively detect and respond
to both known and zero-day threats.
Upon detecting a potential intrusion, the alert system will generate real-time
notifications with relevant details such as the nature of the threat, affected network
segments, and recommended mitigation strategies. By integrate Zabbix with IDS
capabilities system, the system can reduce false positives and improve the accuracy of
threat detection.
This research project aims to create a comprehensive and robust network security
solution that provides greater visibility, transparency, and protection against potential
threats. By delivering real-time threat detection and actionable insights, the system will
significantly enhance an organization's ability to protect its critical assets and maintain
secure network infrastructure in the face of ever-changing network threats
Development of a real-time gesture recognition system for human-robot interaction
Get PDFIn the quickly developing field of robotics and human-robot interaction (HRI), it is crucial for robots enable to recognize and react to human gestures in real-time. This study describes the creation and application of a real-time hand gesture detection system intended using TurtleBot3 Burger in Humble version to improve HRI's effectiveness and naturalness by leveraging recent developments in Robot Operating System (ROS2), computer vision, sensing, machine/deep learning, and Internet of Things (IoT). The system supports navigation and delivery tasks, monitors environmental temperature and humidity, captures images or records videos for surveillance and security, and integrates with Telegram for remote monitoring and alerts.
To capture the finer details of hand gestures and ensure its supported functionality, the suggested system uses a multi-modal method that integrates data from laptop and raspberry pi cameras, and sensors such as LiDAR and DHT22. Robots can now understand a variety of gestures by detecting the number and sequence of open and closed fingers, thanks to the system's robust and accurate gesture detection, which is made possible by a carefully curated dataset and cutting-edge deep neural networks. Low latency between gesture input and robot reaction is made possible by effective model optimization and parallel processing, which gives the system its real-time characteristics. For fluid and interactive HRI situations including collaborative activities, assistive robotics, and entertainment applications, this real-time capacity is essential. The design architecture of the system, data pretreatment methods, and deep learning models used are discussed in the study, with an emphasis on the model's adaptation to various robot platforms and situations. Robots will be able to respond to human cues more contextually if natural language processing (NLP) techniques are incorporated to improve the contextual comprehension of gestures [2]. The system's great accuracy and robustness have been demonstrated through thorough testing in a variety of HRI settings. It has prospective applications in fields including home services, education, manufacturing, business, and entertainment where human-robot interaction must be natural and intuitive.
In summary, the created real-time hand gesture detection system is a significant development in the field of HRI, allowing efficient and smooth communication between people and robots to bridge the gap between them. Its versatility and precision enable a wide range of real-world applications, potentially transforming how humans and robots collaborate and interact
Factors that affect Malaysian companies' usage intention of artificial intelligence advertising
Get PDFThis study investigates the determinants that impact the inclination of Malaysian enterprises to utilise AI advertising. The study employs a complete statistical analysis technique, including descriptive analysis, ANOVA, correlation analysis, normality tests, independent t-tests, and multiple regression analysis, by determine the primary determinants of the adoption of AI advertising. The findings indicate that subjective norms, attitude towards AI advertising, and perception of behavioural control all have a significant impact on the intention to utilise AI
advertising. The regression model accounts for 57.1% of the variation in the intention to utilise AI advertising, suggesting a strong connection between the predictors and the dependent variable.
These findings have practical consequences for organisations operating in the AI advertising sector, indicating the necessity of customised strategies and improved training programs to boost the rate at which AI technology is used. The study enhances the current body of knowledge for researchers by offering empirical proof on the elements that impact the intention to use AI advertising. It proposes potential areas for future research, such as investigating new predictors and utilising longitudinal and qualitative research methods. In summary, this practice highlights significance of comprehending the elements that impact adoption of AI advertising. This understanding is crucial for devising effective strategies that may optimise the utilisation and benefits of artificial intelligence in advertising on the commercial environment in Malaysia
The influence of housing attributes on homebuyers’ satisfaction: a study on landed residential property
Get PDFIn the ever-evolving landscape of real estate, the pursuit of understanding homebuyer satisfaction has gained prominence, particularly within the context of landed residential properties. This research delves into the complex interplay between distinct housing attributes
and the levels of satisfaction experienced by homebuyers in this unique segment of the housing market. Landed residential properties, characterized by their ownership of both dwelling and land, present a distinctive avenue for exploration.
The study employs a multifaceted approach, combining qualitative analyses of individual homebuyer experiences with quantitative assessments of the diverse attributes that define these residential spaces. Architectural housing design, neighbourhood amenities and characteristics,
public infrastructure, environmental attributes, utilities services and various other factors are scrutinized to unravel their individual and collective impacts on overall satisfaction.
By leveraging the sampling, feedback from a diverse group of homebuyers, and utilizing the Analytic Hierarchy Process (AHP) data analysis techniques, this research seeks to provide nuanced insights into the factors that significantly influence the satisfaction of homeowners in landed residential properties. In this study, a total of 50 survey forms were distributed via face�to-face interactions between January 2024 and February 2024. However, only data from 31
completed surveys were included in the analysis. Both descriptive analysis and the Analytic Hierarchy Process (AHP) were employed to assess the significance of various housing attributes and to compare the findings with those reported in the existing literature.
As the housing market continues to witness shifts in preferences and demands, the outcomes of this study aspire to offer practical implications for real estate developers, urban planners, and policymakers. Understanding the dynamics of satisfaction within the context of landed residential properties is not only crucial for meeting the current needs of homeowners but also for shaping the future development and sustainability of residential communities. This research
serves as a comprehensive exploration, contributing valuable knowledge to the existing literature and fostering a deeper understanding of the intricate relationship between housing
attributes and homebuyer satisfaction in the context of landed residential propertie
A semantic analysis of clickbait news headlines on Malaysian alternative online news portals
Get PDFThe expanding realm of online media has provided fertile ground for the proliferation of clickbait techniques within the Malaysian media landscape. This paper is an investigation into the mechanics of clickbait headlines on Malaysian alternative news portals: World of Buzz, SAYS, and Free Malaysia Today using Biyani et al. (2016) and Pujahari and Sisodia (2020)’s Clickbait Categorization Framework. 30 clickbait headlines from articles published between January 2019 and December 2023 were selected, compiled, and semantically analyzed to identify recurring and prevalent patterns of linguistic features. The research findings reported that all headlines exhibit traits of multiple types simultaneously, and that “Teasing and Ambiguous” are the most common pairing identified. All other identified combined categories also originate from this foundational combination. It was also found that clickbait headlines are more prevalent in soft news compared to hard news. Moreover, findings conclude that the five main features of clickbait headlines, as deduced from this research, include (1) the use of the direct address technique, (2) integration of listicles, (3) dependance on emotional appeal, (4) utilization of rhetorical questions and (5) the application of capitalization. The implications of this study underscore the dynamic composition of clickbait headlines by local journalists, revealing both differences and similarities in writing styles between them and those from foreign countries. This enhances comprehension regarding the linguistic features used and the semantic relationship among these features in aiding the writers to convey the intended message and to entice readers to engage with the full article
Cultural factors of English language speaking anxiety among Malaysian undergraduate students in UTAR
Get PDFThis study investigates cultural factors that contribute to English speaking anxiety among undergraduate students at Malaysia's University Tunku Abdul Rahman (UTAR). Using a stratified sample strategy, 60 respondents from various faculties responded to a questionnaire delivered via Google Form to examine their English speaking anxiety and perceptions of cultural factors that influence English speaking anxiety. The data indicate that a sizable proportion of respondents experience moderate to high levels of anxiety when speaking English. Cultural factors such as language environment, social norms, parental methods of teaching, educational background, socioeconomic status, and language dominance in various regions are investigated for their influence on English speaking anxiety. The findings show an in-depth understanding of English speaking anxiety as impacted by cultural background. The study emphasises the need of addressing English speaking anxiety in order to enhance successful language acquisition and communication skills among undergraduate students. More research is needed to reduce English speaking anxiety and provide a supportive environment for language development
Perceived social support, job stress, and self-efficacy as predictors on employee engagement in Malaysia.
Get PDFThis study explores how perceived social support, job stress, and self-efficacy predict
employee engagement among university lecturers in Malaysia. It seeks to answer three
questions: whether perceived social support enhances engagement, whether job stress reduces
it, and whether self-efficacy positively influences it. These insights aim to deepen the
understanding of the factors that affect employee engagement, providing a foundation for
further research. The study applied a quantitative research design, and data were collected
through purposive and snowball sampling from academic staff aged 30 to 60 across various
Malaysian universities. The study utilised G*Power software to calculate the sample size and
SPSS version 29 for data analysis. The respondents, representing various ethnic groups,
provided 192 valid responses. The study validated that perceived social support positively
predicted employee engagement, job stress negatively predicted it, and self-efficacy also
positively predicted employee engagement in Malaysia. These findings underscore the
importance of fostering supportive environments and enhancing self-efficacy to boost
engagement while also recognizing the adverse effects of job stress. The study’s implications
suggest that higher education institutions should consider strategies to support employee
engagement, in line with Malaysia’s National Transformation 2050 (TN50) goals. However,
the study’s limitations, including response bias and the exclusion of external stressors, limited
generalisability highlight the need for future research to address these factors and extend the
investigation to other sectors