Singapore Management University
St. Mary's University
Ai Failures In The Eyes Of The Downstream Developer: A First Look At Concerns, Practices, And Challenges,
2027
Singapore Management University
Ai Failures In The Eyes Of The Downstream Developer: A First Look At Concerns, Practices, And Challenges, Haoyu Gao, Mansooreh Zahedi, Wenxin Jiang, Hong Yi Lin, James C. Davis, Christoph Treude
Research Collection School Of Computing and Information Systems
With the advancement of AI models, more software systems are adopting AI as a component to facilitate automation. Pre-trained models (PTMs) have become a cornerstone of AI-based software, allowing for rapid integration and development with lower training cost. However, their adoption also introduces failure modes such as data leakage and biased outputs, that may require careful handling by downstream developers. While previous research has proposed taxonomies of these technical concerns and various mitigation strategies, how downstream developers address these issues during the development of general AI-based software when reusing PTMs remains unexplored. Understanding downstream developers’ perspectives is essential because they …
Logupdater: Automated Detection And Repair Of Specific Defects In Logging Statements,
2027
Chinese University of Hong Kong
Logupdater: Automated Detection And Repair Of Specific Defects In Logging Statements, Renyi Zhong, Yichen Li, Jinxi Kuang, Wenwei Gu, Yintong Huo, Michael R. Lyu
Research Collection School Of Computing and Information Systems
Developers write logging statements to monitor software runtime behaviors and system state. However, poorly constructed or misleading log messages can inadvertently obfuscate actual program execution patterns, thereby impeding effective software maintenance. Existing research on analyzing issues within logging statements is limited, primarily focusing on detecting a singular type of defect and relying on manual intervention for fixes rather than automated solutions.To address the limitation, we initiate a systematic study that pinpoints four specific types of defects in logging statements (i.e., statement code inconsistency, static dynamic inconsistency, temporal relation inconsistency, and readability issues) through the analysis of real-world log-centric changes. We …
Analyzing Developer Discussions On Eu And Us Privacy Legislation Compliance In Github Repositories,
2026
Singapore Management University
Analyzing Developer Discussions On Eu And Us Privacy Legislation Compliance In Github Repositories, Georgia M. Kapitsaki, Maria Papoutsoglou, Christoph Treude, Ioanna Theophilou
Research Collection School Of Computing and Information Systems
Context: Privacy legislation has impacted the way software systems are developed, prompting practitioners to update their implementations. Specifically, the EU General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have forced the community to focus on users’ data privacy. Objectives: Relying on the vast amount of data on developer issues available in GitHub repositories, our aim is to gather empirical evidence on the issues developers of Open Source Software discuss to comply with privacy legislation. Method: We examined such discussions by mining and analyzing 32,820 issues from GitHub repositories. We partially analyzed the dataset automatically to identify …
An Intelligent Sdn-Enabled Framework For Real-Time Ipv6 Ip Spoofing Detection And Prevention,
2026
Multimedia University, Malaysia
An Intelligent Sdn-Enabled Framework For Real-Time Ipv6 Ip Spoofing Detection And Prevention, Navaneethan C. Arjuman, Ahmad Fikri Sharudin, Zar Chi Hlaing, Supriyanto Praptodiyono
Journal of Informatics and Web Engineering
With the increase in the adoption of Internet Protocol version 6 (IPv6), the issue of Internet Protocol (IP) spoofing has remained a significant risk that damages the integrity of networks. Although some countermeasures, like Secure Neighbor Discovery (SEND) and Router Advertisement (RA) Guard, are available, these solutions are complex to deploy, consume high resources, and can be bypassed by advanced attacks. This paper provides a critical review of the available IPv6 spoofing prevention methods and proposes an enhanced multi-layered framework that uses improved ingress and egress filtering, Artificial Intelligence (AI)-based anomaly detection, and Software-Defined Networking (SDN) to automatically mitigate threats …
Mask-Resilient Multi-Biometric Authentication For Healthcare,
2026
Multimedia University, Malaysia
Mask-Resilient Multi-Biometric Authentication For Healthcare, Caleb Yeo, Siew-Chin Chong, Kian-Ming Lim, Lee-Ying Chong
Journal of Informatics and Web Engineering
The fast integration of digital healthcare platforms, including telemedicine and electronic health records, has created a significant need for secure, contactless authentication methods. Traditional login methods such as passwords and PINs are increasingly insufficient for ensuring the security of sensitive patient data. This paper presents a robust, lightweight Healthcare Authentication System that five biometric modules to address major challenges in facial recognition: spoofing attacks, liveness detection, and facial occlusion due to mask-wearing. The system leverages RetinaFace for face detection, a MobileNetV2 model for mask classification, a lightweight CNN for blink-based liveness verification, and MiniFASNet for spoof detection. For identity matching, …
Structured Data Preprocessing Techniques For A Multilingual Healthcare Dataset In Machine Learning Applications,
2026
Multimedia University, Malaysia
Structured Data Preprocessing Techniques For A Multilingual Healthcare Dataset In Machine Learning Applications, Md. Abu Nishan, Choo-Yee Ting, Chih-Yang Pee, Helmi Zakariah, Thanh-Trung Nguyen
Journal of Informatics and Web Engineering
Data preprocessing is a foundational stage in any machine learning (ML) workflow, especially when dealing with complex, multilingual, and real-world healthcare datasets. The quality, consistency, and structure of input data strongly influence the accuracy, fairness, and interpretability of predictive models. This paper presents a comprehensive and structured preprocessing framework designed for a large multilingual healthcare dataset containing 19,536 patient records with 67 features encompassing demographic, clinical, and socioeconomic information. The framework systematically addresses data heterogeneity, linguistic diversity, and structural inconsistencies through multiple stages of cleaning and transformation. The preprocessing process includes translation of Malay text into English to ensure linguistic …
Comparative Analysis Of Time Series Forecasting Models For Stock Price Prediction: Arima, Lstm, And Prophet,
2026
Politeknik Negeri Banjarmasin, Indonesia
Comparative Analysis Of Time Series Forecasting Models For Stock Price Prediction: Arima, Lstm, And Prophet, Muhammad Syahid Pebriadi, Tino Kemal Fattah, Putriana Salman, Rizky Amelia
Journal of Informatics and Web Engineering
Stock price forecasting with a uniform approach is a challenging process due to nonlinearity, regime changes, and seasonality. The following paper would like to address the practical problem of selecting an appropriate model through comparing three widely used models such as Auto Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Prophet based on how they are applied to different attributes of data and forecast-related tasks. The structured direct comparison will be our proposal, as to be able to identify the strengths and shortcomings in abiding by each model. Each of the model is methodologically trained and tested on …
Haptic Technology In Networked Digital Applications: A Literature Review,
2026
Sunway University, Malaysia
Haptic Technology In Networked Digital Applications: A Literature Review, Tharanitharan Chandran, Ngu War Hlaing, Athirah Mohd Ramly
Journal of Informatics and Web Engineering
Haptic-enhanced network applications provide a breakthrough merger of haptic technology with digital environments, allowing for immersive and responsive interactions. In contrast to traditional interfaces that rely primarily on visual and auditory feedback, these applications add tactile sensations, boosting user engagement in domains such as telemedicine, Virtual Reality (VR), Augmented Reality (AR), gaming, and remote collaboration. This study adopts a systematic literature review methodology to identify, filter, and analyse relevant academic sources published between 1998 to 2024, to examine accomplishments, challenges, and future directions of haptic technology in network applications. The study investigates actuator technology, tiny haptic devices, and adaptive algorithms …
A Comparative Study Of Machine Learning Algorithms For Customer Churn Prediction In Ecommerce,
2026
Multimedia University, Malaysia
A Comparative Study Of Machine Learning Algorithms For Customer Churn Prediction In Ecommerce, Tan Yi Qi, Lew Sook Ling, Ram Avtar
Journal of Informatics and Web Engineering
Customer churn rate is one of the main challenges that e-Commerce businesses must address, as it may affect revenue and long-term growth. In this paper, predictive analysis was used to identify potential customers who would be lost. It uses two different types of data to evaluate and compare the performance of five Machine Learning (ML) models, namely logistic regression, decision tree, support vector machine, gradient boost and random forest. Accuracy, precision, recall rate and F1 score are used as indicators of model performance. Among them, random forest achieved the highest performance across both datasets, demonstrating superior predictive power and consistency. …
Recycle Tracker Application: A Gamified Mobile Platform To Enhance Recycling Behaviour,
2026
Multimedia University, Malaysia
Recycle Tracker Application: A Gamified Mobile Platform To Enhance Recycling Behaviour, Yi Han Ng, Pey Yun Goh, Lee Yeng Ong, Kian Ming Lim
Journal of Informatics and Web Engineering
Malaysia's recycling efforts have long been challenged by limited accessibility, low motivation, and insufficient environmental awareness among the public. Despite various national campaigns, recycling participation rates remain inconsistent, highlighting the need for more engaging and user-centred approaches. To address these challenges, this paper presents the Recycle Tracker Application, a gamified mobile solution designed to encourage consistent recycling behaviour and strengthen environmental responsibility among Malaysians. The application integrates multiple core features, including a real-time interactive map of recycling centres, an educational waste-sorting and DIY recycling guide, personalized eco-goal setting, and an innovative Dino Evolution gamification system that rewards users as they …
Sql Optimization In E-Commerce Platforms Using Large Language Models: A Multi-Pattern Query Empirical Study,
2026
Sunway University, Malaysia
Sql Optimization In E-Commerce Platforms Using Large Language Models: A Multi-Pattern Query Empirical Study, Min Ming Lee, Nabhan Salih
Journal of Informatics and Web Engineering
The rapid expansion in e-Commerce domains has presented an unprecedented challenge to database systems that support the execution of complex analytic queries on multi-billion transaction data stream while keeping queries’ response latency under one second. It is commonly observed that Large Language Models (LLMs) are very good at processing natural language queries, rather than struggling with syntax or semantics. This feature is instrumental to various tasks like analytics acceleration, transaction speed-up and handling large workloads across different domains. In this paper, a detailed examination of how LLMs can be employed as intelligent assistants for Structure Query Language (SQL) query optimization …
Leveraging Meaningful Intrusion Detection Mechanism For Voice Traffic Patterns,
2026
Multimedia University, Malaysia
Leveraging Meaningful Intrusion Detection Mechanism For Voice Traffic Patterns, Aizam Nazmien Alinoordin, Liana Ameerah Rosli, Sin-Ban Ho, Said Suleiman Naseer Al Harthy, Ian Chai
Journal of Informatics and Web Engineering
Voice over IP (VoIP) communication has emerged as an important way of communicating as a modern digital medium but it has been facing a lot of cyber threats because of the protocols involved and the real-time aspect associated with it. conventional Intrusion Detection Systems (IDS) do not cope well with VoIP traffic, as signature-based solutions fail to handle various types of intrusions, while anomaly-based approaches tend to produce a high false positive rate. In this research, a hybrid intrusion detection mechanism is integrated, a lightweight and application-based system built around signature matching and anomaly detection to offer advanced protection to …
A Web-Based Multi-Modal Ai Framework For Intelligent Content Moderation With Constructive Feedback,
2026
Multimedia University, Malaysia
A Web-Based Multi-Modal Ai Framework For Intelligent Content Moderation With Constructive Feedback, Marwah Zaid Mohammed Al-Helali, Naveen Palanichamy, Su-Cheng Haw, Subhashini Gopal
Journal of Informatics and Web Engineering
Traditional content moderation systems primarily rely on strict measures that focus on removing offending or inappropriate content without offering users any form of educational feedback or guidance. This lack of constructive interaction often leads to a poor user experience, leaving users frustrated and uninformed regarding the reasons behind content removal. Moreover, such a system fails to encourage long-term positive behavioural changes within online communities, as users do not gain insights on how to improve their conduct. This paper proposes a new constructive Artificial Intelligence (AI) intervention system that integrates a number of AI models in order to conceptualize content moderation …
Empirical Evaluation Of Feature Selection Strategies For Interpretable And Robust Healthcare Machine Learning,
2026
Multimedia University, Malaysia
Empirical Evaluation Of Feature Selection Strategies For Interpretable And Robust Healthcare Machine Learning, Md. Abu Nishan, Choo-Yee Ting, Chih-Yang Pee, Helmi Zakariah, Thanh-Trung Nguyen
Journal of Informatics and Web Engineering
Feature Section (FS) is a fundamental step in developing accurate, robust, and interpretable Machine Learning (ML) models, especially when working with high-dimensional structured datasets commonly found in healthcare, finance, and other dataintensive domains. Selecting the most relevant subset of features not only improves predictive performance but also enhances computational efficiency and model transparency. This paper presents a comprehensive and systematic study of widely adopted feature selection strategies, including filter, wrapper, embedded, model-agnostic methods, and dimensionality reduction techniques. Each category is examined through experimental evaluations across multiple classifiers and benchmark datasets to understand how different feature selection mechanisms affect model performance, …
Benchmarking Hybrid Learning Models For Personalized Risk Classification And Prediction In Oncology,
2026
University of Notre-Dame of Kasayi, Democratic Republic of the Congo
Benchmarking Hybrid Learning Models For Personalized Risk Classification And Prediction In Oncology, Tshikutu Bikengela Anaclet, Nkongolo Kenda Simon, Biduaya Symphorien, Mabela Matendo Rostin, Mbuyi Mukendi Eugène
Journal of Informatics and Web Engineering
In the field of oncology, and specifically regarding prostate cancer, recent breakthroughs in artificial intelligence have opened fascinating possibilities for risk classification and prediction. However, moving from theoretical success to actual clinical practice remains a significant challenge. In this study, we evaluated several architectures (generative, discriminant, and hybrid models) to assess their real-world relevance in the context of longitudinal medical data, which is often noisy or incomplete. Our analysis does not merely measure raw performance; it seeks to understand the critical balance between statistical precision and the interpretability that clinicians require for daily decision-making. By examining the inherent strengths and …
Next-Gen Fitness: Ai-Based Diet Plans And Real-Time Exercise Monitoring With Gradient Boosting, Gaussian Mixture Models And Mediapipe,
2026
SRM Valliammai Engineering College, India
Next-Gen Fitness: Ai-Based Diet Plans And Real-Time Exercise Monitoring With Gradient Boosting, Gaussian Mixture Models And Mediapipe, I. Kavitha, B. Vanathi, K. Revathi, K. Sanjaikumar
Journal of Informatics and Web Engineering
Global shifts toward sedentary habits and refined dietary choices are magnifying public-health pressures from obesity, metabolic dysregulation, and cardiovascular disease, positioning targeted, personalized fitness coaching tools at the forefront of preventive-care strategy. By embedding tailored nutritional and physical-activity plans into daily life, these tools reinforce adherence, enhance intrinsic motivation, and catalyze sustained wellness achievements—each moderated by real-time analytics, feedback loops, and progressive adaptation to evolving behavior and physiology. Their leading benefits include prolonged engagement, diminished risk of relapse, and deep integration of healthful routines, culminating in lowered lifetime healthcare costs and improved individual life satisfaction. Current machine-learning approaches deliver commendable …
Calibrated Probabilistic Predictions For Phishing Email Detection Using Sentence-Bert With Machine Learning Models,
2026
Nigerian Defence Academy, Nigeria
Calibrated Probabilistic Predictions For Phishing Email Detection Using Sentence-Bert With Machine Learning Models, Oluwasegun Abiodun Abioye, Abraham Evwiekpaefe, Philip Odion
Journal of Informatics and Web Engineering
The growing sophistication of phishing attacks requires better detection than classic conventional filters. Previous work has focused on the accuracy, precision and recall of classifiers but there is a lack in testing well-calibrated probability predictions, which are crucial for reliable decision-making using cybersecurity systems. To fill this gap, we present in this work a phishing email detection framework that combines semantically rich Sentence-BERT all-mpnet-base-v2 model embeddings with a wide variety of machine learning classifiers. A total of nine model configurations were tested, including Sentence-BERT combined with GRU, SVM, Random Forest, Logistic Regression, k-Nearest Neighbours, Gaussian Naïve Bayes, Multi-Layer Perceptron, LightGBM …
Iot Applications In Fire Safety Management: Bibliometric Study On Resilience And Emergency Response,
2026
Universiti Teknologi MARA (UiTM) Shah Alam Campus, Malaysia
Iot Applications In Fire Safety Management: Bibliometric Study On Resilience And Emergency Response, Nur ‘Amirah Mhd Noh, Nor Rima Muhamad Ariff, Ahmad Amru Mohamad Zaid, Rabeah Md Zin
Journal of Informatics and Web Engineering
In the past few years, the Internet of Things (IoT) has been used more often in fire safety. Simple devices like smoke sensors, alarms, and connected networks are now able to detect signs of fire earlier and send alerts faster. These systems are not perfect, but they do make it easier to react before a small incident becomes serious. This paper reviews how research in this area has developed. The study looked at three main databases—Scopus, Web of Science (WoS), and IEEE Xplore. At first, the searches gave thousands of results, most of which were not directly related to fire …
Designing A Predictive Expense Tracking Mobile Application: A Comparison Of Random Forest, Linear Regression, And Prophet-Based Models,
2026
Multimedia University, Malaysia
Designing A Predictive Expense Tracking Mobile Application: A Comparison Of Random Forest, Linear Regression, And Prophet-Based Models, Li Wei Tai, Li Zhe Poh, Md Ismail Hossen
Journal of Informatics and Web Engineering
In today’s fast-paced world, people engage in various forms of daily consumption, yet many lack the habit of tracking their expenses due to low financial awareness. Without proper expense recording and budgeting, individuals often struggle to understand their consumption habits and are at a disadvantage when it comes to financial planning, especially during emergencies. To address these issues, this project proposes the development of a user-friendly expense tracking mobile application designed to help adults build healthy financial habits and utilize predictive algorithms to forecast future expenses. The application enables users to record daily transactions, analyse their spending patterns, plan budgets, …
Web Accessibility And Search Engine Optimization: Best Approach For Building Corporate Websites,
2026
Birmingham City University, United Kingdom
Web Accessibility And Search Engine Optimization: Best Approach For Building Corporate Websites, Jeremiah Charles, Khaled Mahbub
Journal of Informatics and Web Engineering
The rapid digitization of products and services necessitates the development of safeguards to enforce equitable access to internet resources. Although legislative measures have been implemented to compel public-sector organizations to provide accessible web and mobile applications, most websites are still not fully accessible. There is an urgent need for organizations to integrate accessibility into current and future developments. The integration of solutions derived from decades of accessibility research into organizational processes and initiatives is beneficial to all users. This study provides a guide for businesses and organizations to improve their web accessibility and Search Engine Optimization (SEO). This research covers …
