Designing For Novice Debuggers: A Pilot Study On An Ai-Assisted Debugging Tool,
2025
Singapore Management University
Designing For Novice Debuggers: A Pilot Study On An Ai-Assisted Debugging Tool, Oka Kurniawan, Erick Chandra, Christopher M. Poskitt, Yannic Noller, Kenny T.W. Choo, Cyrille Jegourel
Research Collection School Of Computing and Information Systems
Debugging is a fundamental skill that novice programmers must develop. Numerous tools have been created to assist novice programmers in this process. Recently, large language models (LLMs) have been integrated with automated program repair techniques to generate fixes for students' buggy code. However, many of these tools foster an over-reliance on AI and do not actively engage students in the debugging process. In this work, we aim to design an intuitive debugging assistant, CodeHinter, that combines traditional debugging tools with LLM-based techniques to help novice debuggers fix semantic errors while promoting active engagement in the debugging process. We present findings …
Simulated Interactive Debugging,
2025
Singapore Management University
Simulated Interactive Debugging, Yannic Noller, Erick Chandra, Srinidhi Chandrashekar, Kenny Choo, Cyrille Jegourel, Oka Kurniawan, Christopher M. Poskitt
Research Collection School Of Computing and Information Systems
Debugging software, i.e., the localization of faults and their repair, is a key activity in software engineering. Therefore, effective and efficient debugging is one of the core skills a software engineer must develop. However, the teaching of debugging techniques is usually very limited or only taught in indirect ways, e.g., during software projects. As a result, most Computer Science (CS) students learn debugging only in an ad-hoc and unstructured way. In this work, we present our approach called Simulated Interactive Debugging that interactively guides students along the debugging process. The guidance aims to empower the students to repair their solutions …
Editorial Preview For October 2025 Issue,
2025
Multimedia University, Malaysia
Editorial Preview For October 2025 Issue, Su-Cheng Haw, Hui-Ngo Goh
Journal of Informatics and Web Engineering
The October 2025 issue of the Journal of Informatics and Web Engineering (JIWE) concludes the journal’s triannual publication cycle and precedes its transition to a quarterly schedule commencing in 2026. This issue presents 21 research papers within its regular section, covering a wide range of areas including Artificial Intelligence (AI), Machine Learning (ML), Software Engineering, Recommender Systems, Cybersecurity, and Web Technologies. The collection reflects JIWE’s ongoing effort to advance research in the informatics and web engineering domain. A special thematic section, guest-edited by Prof. Ts. Dr. Hairulnizam Bin Mahdin, centers on “AI-Enhanced Computing and Digital Transformation”. The featured studies examine …
Pavement Distress Analysis In Malaysia: A Novel Deepseg-Cracknet Model For Crack Detection And Characterization Using Real-World Data,
2025
Multimedia University, Malaysia
Pavement Distress Analysis In Malaysia: A Novel Deepseg-Cracknet Model For Crack Detection And Characterization Using Real-World Data, Arselan Ashraf, Ali Sophian, Teddy Surya Gunawan, Syed Asif Ahmad Qadri
Journal of Informatics and Web Engineering
Pavement distress analysis plays a big role in keeping roads in good shape, especially in busy spots like Selangor and Kuala Lumpur, where heavy traffic and tropical weather make them wear out fast. This work introduces DeepSeg-CrackNet, a fresh hybrid deep learning model that uses Deep Gradient ResNet to spot cracks and a Residual block with a Modified Attention Mechanism to sort them into types, making it simpler to detect and label pavement damage. The model was trained on real data collected from Malaysian roads, with the CRACK500 dataset added in to cover more situations, and captured using a GoPro …
Zero Trust - The Future Of Cybersecurity,
2025
Air University, Pakistan
Zero Trust - The Future Of Cybersecurity, Danial Haider, Shougfta Mushtaq, Hasnat Ali, Mazliham Mohd Su’Ud
Journal of Informatics and Web Engineering
The recent Zero-Trust Architecture (ZTA) is progressively adopted to the develop network security by assuming no implicit trust within or outside an organization’s boundary. Though, ZTA faces substantial challenges in detecting sophisticated and developing cyber threats, particularly due to its trust on traditional security mechanisms that struggle to manage internal threats and sophisticated attack techniques. To report these shortcomings, the proposed study discovers the combination of advanced machine learning (ML) and deep learning (DL) performances to improve the anomaly detection proficiencies within ZTA environments. The study develops the CICIDS2017 dataset, which contains diverse and realistic network traffic patterns, to assess …
The Role Of Generative Ai In E-Commerce Recommender Systems: Methods, Trends And Insights,
2025
Multimedia University, Malaysia
The Role Of Generative Ai In E-Commerce Recommender Systems: Methods, Trends And Insights, Kai-Ze Liau, Heru Agus Santoso
Journal of Informatics and Web Engineering
Recommender systems have existed for decades, shaping how people consume digital content, receive information, and engage in day-to-day activities, among others. Undoubtably, recommender systems also play a crucial role in e-commerce applications as well, with industry players like Amazon, AliBaba, eBay using recommender systems within their ecosystems to give suitable and value-driven insights. However, recommender systems face some main concerns such as data sparsity, cold-start problems and so on. As a result, research is currently ongoing to solve these issues and provide high-quality recommendations to consumers. This review aims to identify prevailing gaps surrounding these issues by analysing existing research …
Training The Brain: A Machine Learning Approach To Predicting Wellbeing Through Intentional Thought Pattern Modification,
2025
HELP University, Malaysia
Training The Brain: A Machine Learning Approach To Predicting Wellbeing Through Intentional Thought Pattern Modification, Sellappan Palaniappan, Rajasvaran Logeswaran, Kasthuri Subaramaniam, Oras Baker, Bui Ngoc Dung
Journal of Informatics and Web Engineering
This study provides a quantitative framework for wellbeing outcome prediction through intentional cognitive pattern alteration. We demonstrated 81.67% accurate prediction of wellbeing states, in a three-level classification (Low, Medium, High), using a Random Forest classifier with 16 features from psychological, physiological, and behavioural metrics. Our model singles out the gratitude cultivation (21.3%) and peace duration (23.7%) as the strongest predictors of positive well-being outcomes, which provides empirical support to traditional approaches of cognitive training with empirical evidence. Analysis of 1,000 synthetic cases shows that consistent practice of positive thought patterns over 3-6 months can strongly shift wellbeing states, with key …
Hybrid-Based Movie Recommender System: Techniques, Case Studies, Evaluation Metrics, And Future Trends,
2025
Multimedia University, Malaysia
Hybrid-Based Movie Recommender System: Techniques, Case Studies, Evaluation Metrics, And Future Trends, Cheng-Yung Lai, Noramiza Hashim, Lucia Dwi Krisnawati
Journal of Informatics and Web Engineering
The necessity for sophisticated recommender systems in the movie recommendation sphere has become particularly pronounced, generating a more personalized movie recommendation due to people nowadays who like to watch movies online. Efficient recommender systems make use of advanced machine learning (ML) techniques in the pursuit of accurate and meaningful recommendations. This paper endeavours to give a comprehensive overview of technologies known as recommender systems, concentrating on ML methods found at the base. Different strategies have been applied in this work, which include collaborative filtering (CF), content-based filtering (CB), hybrid approaches, Generative AI and so on. The merits and demerits of …
Hybrid Filtering For Personalized And Health-Conscious Recipe Recommendations In Unieats,
2025
Universiti Sains Malaysia, Malaysia
Hybrid Filtering For Personalized And Health-Conscious Recipe Recommendations In Unieats, Hui Lek Liew, Xinying Chew, Khai Wah Khaw, Shiuh Tong Lim
Journal of Informatics and Web Engineering
This research paper introduces UniEats, a recipe recommendation website designed to help university students better organize their meals and adopt healthier eating habits. The system aims to address common challenges students face, including time constraints, limited cooking skills, and insufficient nutritional awareness, which often lead to unhealthy food choices. At the core of UniEats is its recommendation engine, which employs a hybrid filtering approach, combining content-based and collaborative filtering techniques to provide personalized recipe suggestions based on users' dietary preferences, rating history, and recipe attributes. UniEats offers a range of features, including recipe search, weekly meal planning, nutritional analysis through …
Implementation Of Lightweight Machine Learning Models For Real-Time Text Classification On Resource-Constrained Devices,
2025
Multimedia University, Malaysia
Implementation Of Lightweight Machine Learning Models For Real-Time Text Classification On Resource-Constrained Devices, Marwah Zaid Mohammed Al-Helali, Naveen Palanichamy, K. Revathi
Journal of Informatics and Web Engineering
This paper addresses the growing need for implementing intelligent Natural Language Processing (NLP) systems on low-power, memory-limited devices such as Raspberry Pi, mobile phones, and IoT edge hardware. As edge computing and smart devices proliferate, there is an urgent need for more advanced NLP technology that does not require constant cloud access and is efficient in computing and provides results in real time. While deep learning and cloud-based models typically offer high text-classification accuracy and have demonstrated exceptional performance across a range of NLP tasks, they are often too resource-intensive for real-time deployment in constrained environments. To overcome these limitations, …
Review On Secure And Efficient Iot-Based Healthcare System With The Integration Of Machine Learning And Firewalls,
2025
Capital University of Science and Technology, Pakistan
Review On Secure And Efficient Iot-Based Healthcare System With The Integration Of Machine Learning And Firewalls, Muhammad Awais, Syeda Samar Fatima, Jawaid Iqbal
Journal of Informatics and Web Engineering
The integration of the Internet of Things (IoT) into healthcare would mean a revolutionized approach in patient monitoring, diagnosis, and treatment, making this quite some development in healthcare delivery. This review has focused on how the integration of IoT with Machine Learning (ML) and stringent security measures tackle the challenging situation of data privacy and cyber threats in healthcare. Current methodologies point toward how essential advanced sensors, cloud computing, and wireless technologies for IoT-based healthcare systems necessary to secure patient data. patient record kept in files and now forward to the cloud database system so that in any case of …
Addressing Iot Security Challenges Through Advanced Machine Learning And Encryption,
2025
International Islamic University Malaysia, Malaysia
Addressing Iot Security Challenges Through Advanced Machine Learning And Encryption, Ahmad Aziem Khushairi Anuar, Ahmad Anwar Zainuddin, Ahmad Adlan Abdul Halim, Dek Rina
Journal of Informatics and Web Engineering
The rapid growth of Internet of Things (IoT) devices, including smartwatches, home assistants, and connected appliances, has brought significant convenience to daily life, but it has also introduced serious security challenges. These devices often transmit sensitive data, making them vulnerable to theft, misuse, and unauthorized access. Current security measures are insufficient to address the complex and evolving nature of IoT systems, leaving many of them exposed to potential breaches and cyberattacks. This review explores recent developments in IoT security, focusing on how advanced technologies, such as machine learning, can be utilized to enhance the protection of IoT systems. The main …
Research Article Recommender Systems: A Comprehensive Review Of Models, Approaches And Evaluation Metrics,
2025
Multimedia University, Malaysia
Research Article Recommender Systems: A Comprehensive Review Of Models, Approaches And Evaluation Metrics, Sir-Yuean Lim, Noramiza Hashim, Lanh Le Thanh
Journal of Informatics and Web Engineering
With the advent of the current digital era, individuals across the developed world are commonly equipped with devices that can access vast amounts of information at their fingertips. What was considered an impossible feat was realized through remarkable technological advancements. This positive transformation has had a profound impact on education, where traditional knowledge management, such as libraries, are no longer a primary determinant of a student’s academic success. Instead, it has been replaced by the internet as a medium for learning, practicing, and topic exploration. However, the sheer volume of the ever-increasing information available online can easily overwhelm a user, …
Optimised Heterogeneous Handover In Mobile Ipv6 Using Enhanced Predictive Fast Proxy With Media Independent Handover (Mih) Support,
2025
Umaru Musa Yaradua University Katsina, Nigeria
Optimised Heterogeneous Handover In Mobile Ipv6 Using Enhanced Predictive Fast Proxy With Media Independent Handover (Mih) Support, Aliyu Aminu Abdulhadi, Nura Mukhtar
Journal of Informatics and Web Engineering
In wireless networks, handover performance is essential for enabling real-time traffic applications. Long handover delays make it impossible for a Mobile Node (MN) to send and receive data packets, which is very undesirable for real-time applications like video conferencing and VoIP. Therefore, in order to guarantee better handover performance, decreasing handover duration is crucial. The Internet Engineering Task Force (IETF) has standardized Fast Proxy Mobile IPv6 (FPMIPv6) as an enhancement to the novel Proxy Mobile IPv6 (PMIPv6) to attain better handover performance. FPMIPv6 functions in two modes: predictive and reactive, using a link-layer triggering mechanism. The predictive mode uses link-layer …
Developing A Predictive Model For Football Players’ Market Value Using Machine Learning,
2025
Multimedia University, Malaysia
Developing A Predictive Model For Football Players’ Market Value Using Machine Learning, Muhammad Afif Jazimin Idris, Sew Lai Ng
Journal of Informatics and Web Engineering
Football is the world’s most popular sport, and evaluating the market value of players is crucial for clubs and managers in making informed decisions regarding transfers, contracts, and financial planning. This study aims to develop a predictive model to estimate the market value of football players using machine learning (ML) algorithms and real-life statistics performance data from the top five European leagues such as English Premier League, Italian Serie A, Spanish La Liga, German Bundesliga, and French Ligue 1 between the 2017/18 and 2019/20 seasons. By reviewing past research, various ML methods such as Random Forest, LightGBM, XGBoost, and Gradient …
Deep Learning Approaches To Autocorrelation Function And Signal-To-Noise Ratio Estimation In Noisy Images,
2025
Multimedia University, Malaysia
Deep Learning Approaches To Autocorrelation Function And Signal-To-Noise Ratio Estimation In Noisy Images, Kai Liang Lew, Kok Swee Sim, Shing Chiang Tan
Journal of Informatics and Web Engineering
Accurate estimation of signal-to-noise ratio (SNR) in Scanning Electron Microscopy (SEM) is crucial because it evaluates the image quality. SEM images faced a challenge whereby Gaussian noise commonly appears in the images. Thus, researchers have developed several methods to estimate the SNR value. With the introduction of deep learning, most of the limitations in the classical methods can be addressed. This paper proposes a novel deep learning, CNN-based Calibration Map Network (CalibNet) to estimate the SNR value from SEM images using a calibration map between classical SNR and autocorrelation function SNR. The architecture consists of convolutional layers, rectified linear unit …
Loan Default Prediction Using Machine Learning Algorithms,
2025
Universiti Sains Malaysia, Malaysia
Loan Default Prediction Using Machine Learning Algorithms, Zhi Zheng Kang, Sin Yin Teh, Samuel Yong Guang Tan, Wei Chien Ng
Journal of Informatics and Web Engineering
Financial institutions constantly face at the risk of default by borrowers which can result in significant financial losses. It is essential to develop an appropriate predictive model for loan default to reduce these risks and minimise financial losses. The objective of this study is to identify the most suitable machine learning model to predict loan default by comparing four models which are Random Forest, Decision Tree, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Additionally, it also examines the key features influencing loan default prediction. The dataset used in this study is sourced from Kaggle and it consists …
Indonesian Language Sign Detection Using Mediapipe With Long Short-Term Memory (Lstm) Algorithm,
2025
University of Krisnadwipayana, Indonesia
Indonesian Language Sign Detection Using Mediapipe With Long Short-Term Memory (Lstm) Algorithm, Wargijono Utomo, Yogasetya Suhanda, Harun Ar-Rasyid, Andy Dharmalau
Journal of Informatics and Web Engineering
People with disabilities mostly communicate using sign language, but the public still has little understanding of the Indonesian Sign Language System (ISLS). This causes obstacles in daily interactions. Advances in artificial intelligence technology, especially artificial neural networks, open opportunities in sign language recognition, but are still in the development stage. This study aims to build a ISLS sign language recognition model using the LSTM approach and MediaPipe Hands. The method of collecting hand keypoint data, 25 sequences per gesture, and 36 alphabetic and numeric gestures. The dataset is divided into three categories, namely 80% training, 10% validation, and 10% testing. …
Mapping Relational Database To Full-Text Xml For Open Journal System Cross-Platform Article Distribution,
2025
Multimedia University, Malaysia
Mapping Relational Database To Full-Text Xml For Open Journal System Cross-Platform Article Distribution, Chee-Xiang Ling, Kok-Why Ng, Heru Agus Santoso
Journal of Informatics and Web Engineering
In academic publications, the automation of full-text eXtensible Markup Language (XML) is increasingly essential, as generating full-text XML for article distribution is a complex and time-consuming process that requires metadata extraction from a relational database and transformation into hierarchical structures such as Journal Article Tag Suite (JATS). The lack of automation in this transformation process may cause inconsistencies and inaccuracies and may cause errors due to human error. The primary aim is to develop an automation system for transforming metadata from a relational database to full-text XML by reducing errors and speeding the process of generating full-text XML. This is …
Exploring Generative Ai Recommender Systems In E-Commerce: Model, Evaluation Metric, And Comparative Review,
2025
Multimedia University, Malaysia
Exploring Generative Ai Recommender Systems In E-Commerce: Model, Evaluation Metric, And Comparative Review, Wan-Er Kong, Tong-Ern Tai, Palanichamy Naveen, Kok-Why Ng, Lucia Dwi Krisnawati
Journal of Informatics and Web Engineering
Generative Artificial Intelligence (GAI) is changing what can be done with Recommender Systems (RS) in e-commerce by allowing much more interactive, situationally aware, and highly tailored experiences for users. The purpose of this paper is to provide overall insight into how GAI, including Large Language Models (LLMs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other emerging methods, is affecting the building and running of modern e-commerce RS. This paper classifies generative models into groups based on the type of models used, data modality, and specific domain of application. Their involvement in tasks such as personalized product ranking, content generation, …
