Physical Sciences and Mathematics Commons^™

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, 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 …

Predictive Test Case Prioritization In Agile Workflows: A Machine Learning Approach With Smote-Enn And Pca, Kavinraj Baskaran, Junaidi Abdullah, Sin-Ban Ho, Said Suleiman Naseer Al Harthy, Ian Chai

Journal of Informatics and Web Engineering

Test Case Prioritization (TCP) is an essential part of agile software development, allowing for the identification and execution of critical test cases early in the process to maximize fault detection efficiency. Traditional TCP techniques often depend on static metrics, like code complexity and historical pass/fail rates, that do not conform with the dynamic nature of Agile workflows. To overcome this, the proposed research presents a discriminative machine learning-based predictive prioritization model that incorporates historical defect data, execution history, as well as requirement criticality to improve auspiciousness of TCP. The study assesses a range of machine learning models like Logistic Regression, …

Deep Learning For Skin Cancer Classification Using Data Augmentation And Pre-Trained Cnn Models, Pa Pa Min, Fang Yong Lim, Jashila Nair Mogan, Astie Darmayantie

Journal of Informatics and Web Engineering

Skin cancer is one of the most common forms of cancer worldwide, and its early detection is critical for effective treatment and improved survival rates. In recent years, advancements in Artificial Intelligence (AI), particularly deep learning, have shown great promise in enhancing the accuracy and efficiency of medical image analysis. This study aims to develop a non-invasive, AI-powered diagnostic system for the early detection and classification of skin cancer using dermoscopic images. The proposed approach leverages deep learning techniques to automatically recognize and differentiate between various types of skin lesions, including both malignant and benign cases. The project focuses on …

Adaptive And Learned Query Optimization: Techniques, Architectures, And Emerging Trends, Rehab Mohammed Abdelhalim Mokhtar, Palanichamy Naveen

Journal of Informatics and Web Engineering

As databases continue to grow and complexity there is an urgent need to achieve more efficient and faster query processing. This paper presents a technical review of methods that work towards optimization of various types of queries. It progresses by focusing on basic approaches like rules-based optimization and cost-based optimization. Based on this basis, the paper presents a synthesized reference architecture for adaptive and learned query optimizers as a generic framework derived from prior system. It explains on how the classical optimizer components can be enhanced through runtime monitoring integration and feedback mechanisms. From this perspective, it becomes possible to …

An Intelligent Tourism Recommender System Using Retrieval-Guided Large Language Models, Wei-Jun Lim, Nabhan D. Salih, Hairulnizam Mahdin, Ammar Alazab

Journal of Informatics and Web Engineering

Travel planning has become more complex nowadays due to the information overload from numerous online platforms causing travelers to be overwhelmed when selecting destinations, accommodations, and activities that match their preferences and constraints. Traditional tourism recommender systems usually provide static and generic suggestions that fail to match users’ needs and cannot understand natural user language. This research develops and evaluates a Generative AI Chatbot for Tourism Recommendations that use Large Language Models (LLMs) to generate personalized travel itineraries through natural language interaction to solve these limitations. The system can process user natural language inputs including travel origin and destination, trip …

A Framework For Pre-Pages Generator And Visualiser For Journal Publishing Systems: A Case Study, Karrthik Nadarajan, Jiaheng Lu

Journal of Informatics and Web Engineering

In the domain of academic publishing, publishers must establish an efficient editorial workflow for managing their publication schedule while producing quality content, as these two processes will ultimately provide a good experience to readers. Many independent publishers utilizing Open Journal Systems (OJS) rely on a manual compilation of "pre-pages" (front matter), including the Issue Cover, the Editorial Board List, and the Table of Contents (TOC), through a manual workflow that is labourintensive and prone to formatting and metadata errors. This study presents a web-based Pre-Pages Generator and Visualiser that automates the pre-publishing process, developed using Python and the Streamlit framework. …

The Application Of Time Series Forecasting Model For Carbon Emissions Prediction In Electrical Grid: A Step Toward Climate Action, Khim Chen Jason Tan, Keat Soon Tioh, Poh Soon Joseph Ng, Felix Pasila

Journal of Informatics and Web Engineering

This study evaluates the forecasting performance of three models of Autoregressive Integrated Moving Average (ARIMA), Prophet, and Bi-directional Long Short-Term Memory (BiLSTM) in predicting CO₂ emissions from the electricity grid in the United States. Using a real dataset from the California Independent System Operator (CAISO) that records CO₂ emissions at five-minute intervals between 2018 and 2020, the data underwent preprocessing steps including outlier removal, missing value imputation, and feature selection before model training. ARIMA parameters were determined using statistical tests such as ACF, Partial Autocorrelation Function (PACF), and Augmented Dickey Fuller (ADF), while Prophet addressed seasonality and calendar effects. BiLSTM, …

Intelligent Telemedicine Systems: Machine Learning For Remote Diagnosis And Patient Management, Oras Baker, Kasthuri Subaramaniam, Asiah Lokman

Journal of Informatics and Web Engineering

Telemedicine has experienced significant evolution in the past 10 years from a communication-driven health service to an intelligent, data-driven environment driven by AI (artificial intelligence), ML (machine learning), IoMT (Internet of Medical Things) and cloud–edge computing. This development has been further advanced through ongoing global health challenges that have seen an ageing population, rising burden of chronic, multi-morbid conditions, healthcare specialist shortage, soaring cost of healthcare and major disruptions like the COVID-19 pandemic. Modern telehealth services are now integrating more machine learning algorithms for remote diagnosis, real-time monitoring the patient, predictive risk stratification and proactive treatment. This paper offers a …

Reading, Writing, Speaking & Listening: Leveraging Ai Tools For Effective Pre-Service Teacher Education, Brittany E. Kiser, Kelly A. Burlison

College of Education Faculty Publications & Presentations

This interactive roundtable examines how AI tools can enhance pre-service teacher education and support multilingual learners in K–6 classrooms. Participants will explore practical applications for lesson planning, language scaffolding, differentiation, and assessment through real-world ESOL teaching experiences. Discussion will focus on aligning WIDA standards with content instruction and integrating evidence-based ESOL strategies across reading, writing, speaking, and listening. Attendees will leave with actionable approaches for the effective and ethical use of AI to support ELLs.

Buffeted By Change, Researchers Seek Continuity And Investment: Results From The Ithaka S+R Us Researcher Survey, Chelsea Mccracken, Dylan Ruediger, Ruby Macdougall

Copyright, Fair Use, Scholarly Communication, etc.

Executive summary

The research enterprise in the United States, established after the second world war, is experiencing massive disruption. US colleges and universities are grappling with federal and state disinvestment and intervention at a time when AI and other technologies are reshaping how research is conducted and its findings are communicated. As a result, researchers at US higher education institutions are operating in an unstable environment. Against this backdrop, the findings from our survey of 4,003 researchers, fielded in late 2025, demonstrate that they remain committed to carrying out essential work and that they are doing so in innovative ways—through …

Complementary Global–Local Feature Fusion And Ensemble Refinement For Facial-Expression Recognition On Fer2013, H. M. Shahzad, Hassan A. Ahmed

Business Faculty Publications

Facial-expression recognition (FER) on FER2013 remains challenging because of low-resolution images, class imbalance, and label ambiguity. This study presents a global–local feature-fusion framework that integrates complementary representations with validation-based ensemble refinement. A frozen DINOv2 ViT-Base captures global facial semantics, while EfficientNetB3 extracts complementary local texture features. Their fused representation is used for seven-class facial-expression classification. The classification head is first trained with targeted feature-space SMOTE, and the EfficientNetB3 branch is then partially fine-tuned. Five-view test-time augmentation (TTA) is further incorporated at inference, together with an independently trained ConvNeXt-Tiny branch to provide additional architectural diversity. Ensemble weights are selected using a …

Navigating Text-To-Speech (Tts): Ethical Leadership In The Use Of Generative Ai For Extension, Xue A Dong, Paul A Hill

Journal of Extension

Text-to-speech (TTS) AI technology transforms written content into natural-sounding speech, offering a useful tool to enhance accessibility and inclusivity in Extension work. This article examines the role of TTS AI in bridging communication gaps, particularly for diverse and multilingual communities, and demonstrates the importance of ethical leadership in its adoption. By prioritizing diversity, equity, and inclusion, Extension professionals can leverage TTS AI to foster greater connection and engagement. Practical applications and examples are provided to guide the integration of TTS AI into programs. The article also offers recommendations for experimenting with innovative technologies to improve educational outcomes and increase the …

Interrater Reliability Of Software Optimized Movement Assessment With Traditional Methods And Video-Based Functional Movement Screen Scoring And Compensatory Movement Identification, Joshua Paul Verdillo, Nj Ermina, Tanya Mariel Capilla, Russel James Balane, Evriel Prince Apura, Daryl Reymon Apla-On, Ressyl Love Salvador

Philippine Journal of Physical Therapy

Introduction: The Functional Movement Screen (FMS) is a seven-part movement assessment used to identify injury risks caused by faulty biomechanics. There are three barriers in traditional FMS assessments that could affect the tool’s validity: the subjectivity of human scores that could cause bias, the need for in-person evaluations which limit access for remote patients, and the requirement of specialized training to use the tool, which makes it less accessible. This study investigates the effectiveness of SOMA, an AI-based web application that uses the MediaPipe framework to automatically assess (FMS) performances.

Methods: This study employs a quantitative, cross-sectional, comparative design to …

Voltage-Related Power Quality Issues And Impacts On Distribution Networks With Sensitive Loads - A Review, Godwin Elinazi Mnkeni, Jackson Justo, Aviti Thadei Mushi, Bakari M. M. Mwinyiwiwa

Tanzania Journal of Engineering and Technology (TJET)

Voltage disturbances are the most important power quality (PQ) complications that customers and power utilities face in this smart era. The growing adoption of sophisticated electronic equipment and integration of renewable energy sources (RES) into power grids has increased the susceptibility of power distribution networks (PDNs) to voltage sags, swells, interruptions, flicker, and voltage imbalance. These disturbances, mainly caused by upstream faults, switching operations, and RES integration, compromise voltage PQ and system reliability. Consequently, they accelerate equipment degradation, increase electronic waste (e-waste), raise reactive power demand and maintenance costs, increase power losses, and impose substantial economic losses on customers and …

Rent-A-Rag: Embedding-Space Watermarks For Auditing Third-Party Rag, Alexandr Goultiaev Tolstokorov, Kyriakos Mouratidis, Javad Dogani, Nikolaos Laoutaris

Research Collection School Of Computing and Information Systems

Third-party retrieval-augmented generation (RAG) marketplaces create a new auditing problem: data providers may license corpora to a RAG operator, yet later have no visibility into whether their documents are being reused without compensation. Auditing this misuse is difficult because the operator is non-cooperative, answers are paraphrased by the generator, and one response may combine evidence from many providers. We propose DirBucket, a provider-side semantic watermarking and black-box auditing framework for document-level reuse in multi-provider RAG. DirBucket watermarks documents by meaningpreserving paraphrases whose embeddings are biased toward provider-bucket secret directions, enabling detection from black-box answers while preserving retrieval utility. On a …

Compiling Code Llms Into Lightweight Executables, Jieke Shi, Junda He, Zhou Yang, Chengran Yang, Mykhailo Klymenko, Thong (James) Hoang, Sherry (Xiwei) Xu, Zhenchang Xing, David Lo

Research Collection School Of Computing and Information Systems

The demand for better prediction accuracy and higher execution performance in neural networks continues to grow. The emergence and success of Large Language Models (LLMs) have produced many cloud-based tools for software engineering tasks such as code suggestion. Although effective, cloud deployment raises concerns over privacy, latency, and reliance on network connectivity. Running LLMs locally on personal devices such as laptops would address these issues, because it enables offline use and reduces response time. However, local deployment is challenging, since commodity devices lack high-performance accelerators such as GPUs and are constrained by limited memory and compute capacity, which makes it …

User-First Approach To Designing A Library Ai Chatbot, Samantha Seah, Zhenyan Li

Research Collection Library

At SMU Libraries, after a year of experimentation, user testing, and preparation, we have just launched an AI-assisted chatbot to handle Tier 1 questions – will the library be open this weekend, where can I find printers, what workshops does the library offer, etc. While this can be thought of as a technology-forward project, we hope to illustrate how the human elements were essential to elevate the project. Throughout this journey, while learning the ins and outs of new technologies, we placed more emphasis on user-centred service design and we will show how other libraries can do so as well. …

Sex-Based Disparities In Artificial Intelligence For Cardiovascular Disease: A Scoping Review, Maurgan Lee, Tarek Atasi, Michael Mclellan, Yodit Beru, Jason Booza

Population, Patient, Physician and Professionalism Projects

No abstract provided.

Mutation-Based Multi-Agent Test Case Update, Dawei Tian, Jiakun Liu, Yun Peng, Yichen Zhang, Jianlei Chi, Jun Sun, Xiaohong Su

Research Collection School Of Computing and Information Systems

Modern software systems evolve rapidly under CI/CD practices, where tests are critical for quality. However, substantial code changes often render existing test cases obsolete, causing pipeline disruptions, reduced productivity, and compromised quality. Recent automatic test update approaches leverage LLMs to refine test cases via execution feedback and exact-matching context retrieval, prioritizing executability and line coverage but suffering three limitations: (1) neglecting test assertion adequacy, weakening fault detection; (2) relying on coarse line coverage instead of specific uncovered lines/branches; (3) using exact-matching retrieval, which fails for LLM hallucinated queries. To address these, we propose MuMuTestUp, a mutation-guided multi-agent framework with three …

Ddor: Delta Debugging For Explainable Overrefusal Testing And Repair, Qinyan Zhou, Peixin Zhang, Jun Sun, Haonan Zhang, Dongxia Wang

Research Collection School Of Computing and Information Systems

While safety alignment and guardrails help large language models (LLMs) avoid harmful outputs, they can also induce overrefusal, i.e., unwarranted rejection of benign queries that merely appear risky. We present DDOR (Delta Debugging for OverRefusal), a fully automated and explainable framework for overrefusal testing and repair in a black-box setting, where only model inputs and outputs are accessible and internal safety mechanisms remain opaque. DDOR applies delta debugging to localize minimal refusal-triggering fragments (mRTFs) that provide phrase-level, explainable evidence for why a refusal occurs. Conditioned on these mRTFs, DDOR generates diverse, context-rich prompts and performs multi-oracle validation to filter intrinsically …

Probguard: Proactive Runtime Monitoring For Llm Agent Safety Via Probabilistic Prediction, Haoyu Wang, Christopher M. Poskitt, Jiali Wei, Jun Sun

Research Collection School Of Computing and Information Systems

Large Language Model (LLM) agents increasingly operate across domains such as robotics, virtual assistants, and web automation. However, their stochastic decision-making introduces safety risks that are difficult to anticipate during execution. Existing runtime monitoring frameworks, such as AgentSpec, primarily rely on reactive safety rules that detect violations only when unsafe behavior is imminent or has already occurred, limiting their ability to handle long-horizon dependencies. We present ProbGuard, a proactive runtime monitoring framework for LLM agents that anticipates safety violations through probabilistic risk prediction. ProbGuard abstracts agent executions into symbolic states and learns a Discrete-Time Markov Chain (DTMC) from execution traces …

Mg-Fuzz: Model-Guided Fuzzing For Unsafe Scenario Discovery In Autonomous Driving Systems, Yulong Lyu, Ruiqi Hong, Jiawan Wang, Jun Sun, Lei Bu

Research Collection School Of Computing and Information Systems

As autonomous driving systems (ADS) are increasingly deployed in real-world environments, discovering diverse unsafe driving scenarios remains a fundamental yet difficult problem. Existing scenario generation and testing approaches often rely on black-box exploration or externally-observed heuristic feedback, which struggle to effectively guide the search toward high-risk scenarios induced by complex decision-making behaviors. A key difficulty stems from the fact that unsafe behaviors in ADS often arise from internal decision-making logic, which can induce structured and discontinuous responses that are hard to effectively explore using purely black-box guidance. Consequently, current tools tend to repeatedly discover a narrow set of similar unsafe …

Chatgpt, Can You Generate Multiple-Choice Questions For An Undergraduate Computing Course? An Evaluation Of Its Effectiveness Using Classical Test Theory, Eng Lieh Ouh, Kar Way Tan, Siaw Ling Lo, Benjamin Gan, Yuhao Zhang

Research Collection School Of Computing and Information Systems

Educators are increasingly adopting large language models such as ChatGPT to generate multiple-choice questions (MCQs), yet empirical evidence on the quality of these items in computing education assessments remains limited. This study compares 30 ChatGPT-generated and 30 manually crafted items from an undergraduate software design course, and evaluates item difficulty, discrimination, and distractor quality using Classical Test Theory. We triangulated two evaluations using within-subjects and between-subjects designs. Our quantitative findings show that all three metrics in both evaluations point in the same direction. ChatGPT-generated items are easier, less discriminating, and exhibit poorer distractor quality than manually crafted items. Distractor quality …

Service Robots With Low Anthropomorphism In Restaurants: Consumer Reactions And Implications, Ferhat Eren, Volkan Genc

Journal of Global Hospitality and Tourism

This study investigates consumer responses to low-anthropomorphic service robots in restaurant front of-house roles using the AIDUA (Artificially Intelligent Device Use Acceptance). Data from 1,268  participants were analysed using PLS-SEM. The results revealed that social impact and  anthropomorphism significantly influenced both performance and effort expectancy, while hedonic  motivation influenced only performance expectancy. Performance expectancy strongly influenced  emotions, which in turn significantly influenced both the willingness to use service robots and objections  to their use. However, effort expectancy did not significantly influence emotions. The findings validate the  AIDUA model in this context and offer practical insights for robot design and implementation.

Love And Artificial Intelligence: A Research Proposal, Bryanna M. Deatherage, Necdet Gurkan, Sandra J.E. Langeslag

Undergraduate Research Symposium

What happens when people fall in love with Artificial Intelligence (AI)? This study seeks to examine individuals who are in love with AI companions to gain a deeper insight in the cognitive and affective consequences. In addition, this study will examine the most effective forms of intervention regarding growing or reducing feelings of love toward AI. The first part of this study will be a questionnaire about the social and emotional impact of being in love with an AI companion. Three hundred participants will be recruited through online communities related to AI companions. The second part of this study will …

Disturbance-Learning Inertia Estimation Using Artificial Neural Networks For Power System Stability, Sospeter Igaanja Gabriel, Francis Mwasilu, Peter Makolo Dr.

Tanzania Journal of Engineering and Technology (TJET)

ABSTRACT

Power systems are progressively shifting towards low inertia as a result of incorporating significant amounts of intermittent and converter-based renewable energy sources, such as wind and solar power, into the current power grid network. This integration poses considerable problems to inertia and frequency control within the network due to a reduction in the proportion of synchronous generators. Furthermore, rapid frequency deviations occur due to the disparity between supply and demand during contingencies, complicating the maintenance of frequency stability within the power system. The disturbance-learning inertia estimation method for power system stability is presented. The simulation analysis is performed using …

Gambaran Generasi Z Yang Kesepian Dalam Penggunaan Chat Ai Sebagai Pemenuhan Kebutuhan “Someone To Talk”, Ikhwanul Ihsan Armalid, Febty Zahra Arsiwi, Qisthi Fathiyyah, Ratri Mayzakky Afra Syahida

Jurnal Psikologi Sosial

This study aims to understand the experiences of Generation Z individuals who experience loneliness in utilizing Chat AI to fulfill their need for someone to talk to within a socio-emotional context. The study employed a qualitative approach using a phenomenological method involving six Generation Z participants aged 18 to 25 who had used Chat AI for emotional sharing or venting. Data were collected through semi-structured interviews and analyzed using thematic analysis. The thematic analysis yielded six main themes: the dynamics of Generation Z social interactions, experiences of loneliness in social life, patterns of Chat AI usage, Chat AI as a …

Thermal Displacement Quantification And Dynamic Disparity Compensation For Automotive Stereo Cameras, Bisheng Wang, Xiyuan Hu, Ran Meng, An Jiang

Journal of System Simulation

Abstract: This paper presents a full-field thermal displacement inversion and dynamic disparity compensation method to address the disparity drift issue in mass-produced automotive-grade stereo cameras induced by thermal expansion and rigid-body displacement of image sensors under wide-temperature-range operating conditions. The proposed method leverages a high-precision static speckle pattern and digital image correlation (DIC) technique to achieve quantitative characterization of the nonuniform horizontal thermal deformation fields of the left and right image sensors. On this basis, a dynamic disparity compensation model is established, integrating a temperature-dependent quadratic polynomial translation term and a radial thermal expansion correction term. Experiments were conducted by …

Vehicle Cross-Modal Trajectory Prediction Model Based On Driving Style Hypergraphs, Guozhu Cheng, Changyuan Wang, Wenzhi Wang, Yifan Zhang, Tianjun Feng

Journal of System Simulation

Abstract: To address the shortcomings of existing motorway vehicle trajectory prediction methods which overlook differences in driving styles and the future interactions between trajectories of different modes. This paper proposes a cross-modal trajectory prediction model based on a dual-hypergraph Transformer for driving styles. The method first quantifies driving styles by extracting vehicle kinematic features and calculates a similarity matrix between vehicles; it then constructs a dual-hypergraph Transformer, using a homogeneous hypergraph to model the group interactions of vehicles with similar driving styles, and a heterogeneous hypergraph to capture the interaction relationships between spatially neighbouring but stylistically dissimilar vehicles; Finally, a …