Databases and Information Systems Commons^™

Ai-Driven Malware Analysis And Detection: A Comprehensive Survey Of Techniques, Trends And Challenges, Salman Khan, Hasnat Raza, Mansoor Alam

Journal of Informatics and Web Engineering

Malware represents the most critical threat in cybersecurity, meant to compromise the security for any individual or any organization. These are covert software, designed to perform malicious act like data theft, data alteration, and to interrupt a normal operation of the services. The persistent evolution of malware has called for more sophisticated techniques in its detection and prevention, resulted into direct need of Artificial Intelligence in cybersecurity. Artificial intelligence, using machine learning techniques and rising concepts like neural networks has greatly improved the traditional static and dynamic ways of detecting malware. Advances in AI-driven solutions have made them much more …

Optimised Data Integration Using Transformer Model And Resource Description Framework, Jerome Aondongu Achir, Muhammad Abdulkarim, Mohammed Abdullahi

Journal of Informatics and Web Engineering

Organizations have become highly reliant on a range of data sources that span structured, semi-structured, and unstructured data types. These repositories allow large-scale storage for faster ingestion and analytics but pose tremendous challenges of integration owing to schema and contextual differences. Traditional data integration methods, such as the ontology-based Resource Description Framework (RDF), are often inadequate when dealing with these challenges. They specifically struggle with the dynamic evolution of the schema of data sources, context-aware interpretation, and achieving interoperability across heterogeneous data sources. This paper presents an integrated system that augments resource description knowledge with token embeddings using the attention …

Xai -Driven Explainability For Cardiovascular Diseases Prediction, Jacqueline Dike, Jarutas Andritsch

Journal of Informatics and Web Engineering

The adoption of artificial intelligence (AI) in cardiovascular disease prediction has significantly improved risk stratification, offering new avenues for early diagnosis and preventive care. With the growing availability of electronic health records and structured clinical datasets, machine learning (ML) and deep learning (DL) models have demonstrated strong predictive capabilities. However, despite their performance, its adoption in healthcare is often constrained by the lack of transparency and interpretability in many ML and DL models. This lack of explainability undermines clinical trust and raises ethical concerns. In high-stakes domains such as CVD prediction, clinicians require not only accurate outputs but also clear …

Sentiment-Based Music Recommendation System Using Natural Language Processing For Emotion-Aware Song Suggestions, Yu Bui Xuan, S. Prabha Kumaresan, Naveen Palanichamy, Mohamed Uvaze Ahamed Ayoobkhan

Journal of Informatics and Web Engineering

Music plays a vital role in influencing emotions, mood, and mental health. However, classical music recommendation systems mostly rely on listening to history, genre preference, or popularity, ignoring the listener’s mood. Motivation to go further in this area gave birth to this study, where a new sentiment-based music recommendation system is being designed by incorporating Natural Language Processing (NLP) and Machine Learning (ML) techniques to provide emotion-aware song recommendations. The system collects various audio features such as valence, energy, tempo, and danceability from music distribution platforms such as Spotify, which are well-known indicators for classifying the emotional tone of a …

A Unified Cloud-Based System For The Tyre Retail Industry: Design And Implementation Of Tracktive, William Theo Wei Loon, Siew-Chin Chong, Kian-Ming Lim, Lee-Ying Chong, Kuok-Kwee Wee

Journal of Informatics and Web Engineering

In the Malaysian concurrent tyre retailing industry, most of the business activities are carried out in the traditional labour-intensive ways. This manual process is prone to inefficiencies, increases operating costs and increases the likelihood of error. The current paper outlines Tracktive, a unified and cloud-based solution that was conceptualized and built to automate the industry by centralizing the ordering of tyres, inventory and order management to a single cloud-based platform. Tracktive is built on microservice architecture and event-driven architecture and deployed with the help of a simulated cloud environment. All the core functional modules are independent, scalable modules, each having …

Enhancing Fraud Detection In Financial Transactions Using Lightgbm And Random Forest, Wan-Ping Khor, Kah-Ong Michael Goh, Check-Yee Law, Connie Tee, Yong-Wee Sek, Riasat Khan

Journal of Informatics and Web Engineering

In recent years, the frequency and complexity of financial fraud have been rising and have become an urgent challenge for the global financial system. Traditional rule-based detection methods struggle to cope with new types of fraud, especially in terms of real-time detection, generalization ability, and accuracy. To overcome these limitations, machine learning techniques have gradually emerged as a promising solution for identifying fraudulent transactions with better flexibility and scalability. Based on the publicly available European credit card fraud transaction dataset, this study proposes a hybrid model that combines the advantages of LightGBM and Random Forest, aiming to improve the accuracy, …

Real-Time Posture Monitoring For Effective Exercise Using Mediapipe Python, Wan Izzul Wafiq Wan Noor Asmawi, S. Prabha Kumaresan

Journal of Informatics and Web Engineering

Maintaining proper posture during exercise is crucial for preventing injuries and maximizing workout efficiency. This project aims to develop a real-time posture monitoring system using MediaPipe and OpenCV to provide instant feedback on the exercise form. The system captures video input through a webcam, processes it using OpenCV, and utilizes MediaPipe’s pose-estimation model to detect key body landmarks. By analysing the joint angles and comparing them to predefined optimal postures, the system evaluates the user’s form and provides corrective feedback in real time. This approach eliminates the need for expensive wearable sensors, making posture monitoring more accessible and user friendly. …

Design And Implementation Of A Web-Based Lecture Timetable Scheduling System, Suraj Abubakar, Mohammed Kabir Dauda, Maryam Abdullahi Musa, Bala Muhammad Muhammad

Journal of Informatics and Web Engineering

Timetable management is a controversial but core administrative activity at tertiary learning institutions, normally saturated with timetabling clashes, resource wastage, and communication breakdowns. This study describes the development and validation of a Web-Based Lecture Timetable Scheduling System (WLTSS) for Abubakar Tafawa Balewa University (ATBU) Department of Computer Science. The system was implemented to automatically and effectively build, modify, and publicize lecture timetables. On Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), JavaScript, and React as frontend technologies, the system provides an adaptable interface with real-time data updates through the Reacts component model foundation. MySQL and Node.js are used to run …

Test Case Prioritization Using Ant Colony Optimization To Improve Fault Detection And Time, Nurezayana Zainal, Muhammad Sh Salleh, Nur Atiqah Wahidah Sulaiman, Ammar Alazab, Nur Liyana Sulaiman

Journal of Informatics and Web Engineering

Regression testing plays a critical role in ensuring the reliability and quality of software following continuous integration and development. However, executing all test cases during regression testing can be time-consuming and resource-intensive. Test Case Prioritization (TCP) addresses this challenge by determining an optimal execution order of test cases that maximizes early fault detection while minimizing execution time. Optimization algorithms contribute significantly to enhancing the effectiveness of TCP while utilizing limited resources. This study proposes an Ant Colony Optimization (ACO) algorithm to address the TCP problem, leveraging its strength in navigating complex search spaces inspired by the foraging behavior of real …

Flood Disaster Preparedness And Response Using A Web-Based Integrated Flood Management System (Ifms), Mohamed Abdiraman Abdirahim, Abd Samad Bin Hasan Basari, Malik Bader Alazzam, Muhammad Shukri Bin Che Lah, Rabiah Binti Ahmad, Aida Binti Mustapha

Journal of Informatics and Web Engineering

Floods are one of the most frequent and damaging natural hazards in Malaysia, especially in low-lying places, such as Batu Pahat and Johor. National flood monitoring systems, such as InfoBanjir and Malaysian Meteorological Department (MET) Malaysia, have disjointed data pools, late updates, and inadequate public access. In this paper, we present an IFMS, which is a contemporary web platform developed to integrate national flood management systems through data collection, automatic processing, dynamic visualization tools, and others. The system architecture consists of three main layers: IoT-enabled flood sensors, centralized web server, and responsive user interfaces. Backend processing is performed using Laravel, …

Evaluating Accuracy Latency And Robustness Of Face Recognition Models For Real-Time Web Applications, Alani Fatini Sharzizi, Nur Afiqah Sahadun, Abdulkadir Hassan Disina, Harinda Fernando

Journal of Informatics and Web Engineering

Face-recognition technology is one of the most important advancements in the field of computer vision. They play a crucial role in many applications, including biometric authentication, surveillance, online security, and interactive web systems. The use of web-based solutions is increasing continuously. Therefore, accurate and fast recognition models employing few resources are required in real-world applications. However, because of the challenges related to such environments, including the lighting, occlusion, pose, and computing power of client devices, it is difficult to ascertain which model will be most successful in a real-life scenario. The purpose of this research is to compare four deep …

An Enhanced Glowworm Swarm Optimization For Minimizing Surface Roughness In Die Sinking Electrical Discharge Machining, Nurezayana Zainal, Muhammad Ammar S.M. Shahrom, Mohamad Firdaus Ab. Aziz, Salama A. Mostafa, Anis Farhan Kamaruzzaman, Nor Bakiah Abd. Warif

Journal of Informatics and Web Engineering

Electric Discharge Machining (EDM) is a non-traditional machining process that utilizes electric sparks between an electrode and a workpiece submerged in a dielectric fluid to ablate a material. It is commonly used in die-making, aerospace, automotive manufacturing, and medical manufacturing, because it can machine hard and complex materials with high precision. In this work, a Surface Roughness Optimization for EDM (SRO-EDM) model is proposed to investigate the machining performance of the die-sinking EDM process of titanium alloys. A regression-based combined approach of Glowworm Swarm Optimization (GSO) and a Two-Factor Interaction (2FI) model has been proposed to investigate the impact of …

A New Tool For Handling Multiracial And Multi-Identity Data In Health Research, Gabriel J. Merrin

Population Health Research Brief Series

When surveys ask about race or ethnicity, a growing number of Americans select more than one category. The multiracial population now represents over 10% of the U.S. population and is the fastest growing racial group in the country. Yet researchers routinely collapse these individuals into an “other race” category for statistical analysis, rendering specific subgroups invisible. This brief introduces CATAcode, a free software tool that helps researchers systematically explore, document, and prepare check-all-that-apply demographic data for statistical modeling. In a demonstration with over 8,000 high school students, CATAcode revealed 85 distinct racial identity combinations from just eight response options. The …

Responsible Ai In Teaching And Learning, Asa B. Stone, Mark C. Stone, Derek M. Heeren, Santosh Pitla

PRAIRIE: Pioneering Responsible AI for Research, Innovation, and Education

The rapid adoption of generative artificial intelligence (AI) in higher education presents both transformative opportunities and significant pedagogical risks. While AI tools are becoming embedded in academic and professional environments, their integration into teaching and learning raises critical questions about cognitive engagement, academic integrity, equity, and skill development. This white paper proposes a principled framework for the responsible integration of AI in higher education, grounded in the dual commitment to AI literacy and the cultivation of durable skills.

The framework articulates six core principles: purposefulness; transparency; integrity and attribution; critical AI literacy; equity and access; and privacy and data protection. …

The Feelit System: Application Content-Aware Perspectives And Challenges On Understanding User Likes In Social Network Posts, Konstantinos Theocharidis, Hady W. Lauw, Panagiotis Karras

Research Collection School Of Computing and Information Systems

In a series of our prior works, we study influence and subscription maximization problems in social networks that are based on posts having influential content; as content we consider a set of features where each feature corresponds to a specific social network page, whereas influence and subscription relate to gaining the postlike and subscription-to-brand page of targeted users, respectively; subscription is conceptually achieved as repetitive influence on users. So, both influence and subscription depend on content that gains the likes of users; however, to be realistic, modeling and estimating such likes is a complex problem that has not been adequately …

Cellscout: Visual Analytics For Mining Biomarkers In Cell State Discovery, Rui Sheng, Zelin Zang, Jiachen Wang, Yan Luo, Zixin Chen, Yan Zhou, Shaolun Ruan, Huamin Qu

Research Collection School Of Computing and Information Systems

Cell state discovery is crucial for understanding biological systems and enhancing medical outcomes. A key aspect of this process is identifying distinct biomarkers that define specific cell states. However, difficulties arise from the co-discovery process of cell states and biomarkers: biologists often use dimensionality reduction to visualize cells in a two-dimensional space. Then they usually interpret visually clustered cells as distinct states, from which they seek to identify unique biomarkers. However, this assumption is often this assumption often fails to hold due to internal inconsistencies in a cluster, making the process trial-and-error and highly uncertain. Therefore, biologists urgently need effective …

Removed: When Taxi Drivers Meet Dynamic Pricing: A Lesson From Singapore's Justgrab Program, Shih-Fen Cheng, Wen-Tai Hsu, Jing Li

Research Collection School Of Economics

This paper studies how dynamic pricing influences taxi drivers’ behaviors using a unique event, the inception of the JustGrab program in Singapore in 2017, which introduces dynamic pricing to some, but not all, taxi drivers. This is the first time in history that traditional taxi drivers have access to dynamic pricing. Using data covering the universe of taxi trips before and after the inception of JustGrab, we find that there is spatial reallocation that directs more taxi drivers to the previously less-served areas, that there is also a temporal reallocation that directs more taxi drivers to rush hours, as well …

G-Trac: Graph-Textual Representations Alignment For Cold-Start Recommendations, Li Yang Chang, Yuan Fang, Ming Feng Tsai, Chuan Ju Wang

Research Collection School Of Computing and Information Systems

The cold-start problem remains a significant challenge in recommendation systems, particularly for new users or unseen items with little to no historical data. Existing methods, including graph neural networks, often struggle in such scenarios. Inspired by the success of transformer models in natural language processing, we propose G-TRAC (Graph-Textual Representations Alignment for Cold-start Recommendations), a novel approach that integrates transformer-based textual modeling with graph neural networks. By effectively leveraging both textual and structural information, G-TRAC addresses cold-start challenges more effectively. Extensive experiments demonstrate its ability to enhance recommendation quality and generalize well across diverse scenarios.

Online Visual Query System For Real-Time Large-Scale Spatio-Temporal Data Explorations With Error Bounds, Xueqi Huang

Dissertations, Theses, and Capstone Projects

Modern datasets continue to grow in size, dimensionality, and heterogeneity, creating increasing tension between the need for responsive, interactive analysis and the computational cost of accessing, aggregating, and visualizing large volumes of data. Traditional database engines and visualization tools often assume that full data retrieval is feasible or that exact computation is necessary for meaningful insight. In practice, however, analysts frequently benefit from timely, uncertainty-aware approximations than from delayed and exact results. This thesis investigates how data summarization techniques, specifically mergeable sketches can be combined with progressive, out-of-core visualization methods to support interactive exploration of datasets that exceed main memory. …

Prompt Tuning Without Labeled Samples For Zero-Shot Node Classification In Text-Attributed Graphs, Sethupathy Parameswaran, Suresh Sundaram, Yuan Fang

Research Collection School Of Computing and Information Systems

Node classification is a fundamental problem in information retrieval with many real-world applications, such as community detection in social networks, grouping articles published online and product categorization in e-commerce. Zero-shot node classification in text-attributed graphs (TAGs) presents a significant challenge, particularly due to the absence of labeled data. In this paper, we propose a novel Zero-shot Prompt Tuning (ZPT) framework to address this problem by leveraging a Universal Bimodal Conditional Generator (UBCG). Our approach begins with pre-training a graph-language model to capture both the graph structure and the associated textual descriptions of each node. Following this, a conditional generative model …

Learnable Game-Theoretic Policy Optimization For Data-Centric Self-Explanation Rationalization, Yunxiao Zhao, Zhiqiang Wang, Xingtong Yu, Xiaoli Li, Jiye Liang, Ru Li

Research Collection School Of Computing and Information Systems

Rationalization, a data-centric framework, aims to build self-explanatory models to explain the prediction outcome by generating a subset of human-intelligible pieces of the input data. It involves a cooperative game model where a generator generates the most human-intelligible parts of the input (i.e., rationales), followed by a predictor that makes predictions based on these generated rationales. Conventional rationalization methods typically impose constraints via regularization terms to calibrate or penalize undesired generation. However, these methods are suffering from a problem called mode collapse, in which the predictor produces correct predictions yet the generator consistently outputs rationales with collapsed patterns. Moreover, existing …

Significance, Challenges, And Policy Recommendations For Strengthening Database Development To Support Ai For Science In China, Kaihua Chen, Hongxin Liu, Rui Guo

Bulletin of Chinese Academy of Sciences (Chinese Version)

With the rapid emergence of research intelligence driven by big data and artificial intelligence (AI), high-quality, openly shared scientific databases have become a strategic focal point for scientific innovation and enhancing technological competitiveness. Major countries around the world are increasingly recognizing the foundational role of scientific databases in advancing basic research. While continuously strengthening their own scientific data infrastructure through a series of initiatives, they have simultaneously imposed restrictions and suppression on the development of AI technologies in China, including those involving research data. Against this backdrop, building an autonomous and controllable scientific data ecosystem to support research intelligence is …

Data Altruism: Eu Solution And Path Of Localization In China, Teng Wu

Bulletin of Chinese Academy of Sciences (Chinese Version)

Data altruism transcends the profit-seeking, monopolistic and competitive nature of the market mechanism. It is driven by innovation and encourages data subjects and holders to share information guided by the public interest. As a new type of data application and service model, it aims to achieve a virtuous cycle of the data ecosystem while optimize the utilization of data resources. Tracing back to the source, the theoretical foundation of data altruism from the ethical theory of altruism, and the concept of its budding was supported by the data for good. Analysis shows that the specific scheme of the EU data …

Ransomware As Organization: A Comparative Analysis Of Corporate And Criminal Structures In Conti, George Urling

Theses, Dissertations and Capstones

Cybercriminal groups continue to pose major threats to global cybersecurity. One of the most common types of cybercriminal groups are, “Ransomware-as-a-Service (RaaS)" groups, who create and sell ransomware. While research is conducted into the development of ransomware, there is limited reporting on the organizational structure and habits of RaaS groups. In 2022, prominent RaaS group Conti had their chat logs leaked, with the logs ranging from 2020 to 2022. This study seeks to provide a deeper understanding of RaaS group structures by utilizing the Conti leaked logs as a case study. The study, entitled “Ransomware as Organization: A Comparative Analysis …

Open Scholarly Information Systems: Status Quo, Challenges, Opportunities, Hannah Bast, Guillaume Cabanac, Paolo Manghi, Jian Wu, Marcel R. Ackermann

Computer Science Faculty Publications

Over the past 30 years, a rich ecosystem of scholarly information systems has developed that openly provide their services to the scientific community. These systems include aggregators of bibliographic metadata (e.g., DBLP, OpenCitations, OpenAIRE Graph, OpenAlex, ORKG, Semantic Scholar, CiteSeerX, and CORE); publication, data, and software repositories (e.g., Arxiv.org, Figshare, Zenodo, Software Heritage, and Dataverse); and PID authorities (e.g., ORCID, ROR, Crossref, and DataCite). This interdisciplinary Dagstuhl Seminar "Open Scholarly Information Systems: Status Quo, Challenges, Opportunities" (25381) was the first of its kind to bring together practitioners from this ecosystem, as well as researchers investigating related questions or relying on …

Tackling The Societal And Regulatory Challenges Of Emerging Technologies: A Case Study Of Deepfake, Jingyao Li

2026

Governing emerging technologies such as Artificial Intelligence (AI) poses enduring challenges for policymakers, industries, and societies. Early-stage governance is often hindered by limited understanding of technological implications, rapid innovation cycles, and resistance from powerful industry actors who favor minimal oversight. Yet, timely and effective governance is essential, as new technologies are most malleable in their formative stages. This dissertation examines how emerging technologies can be governed effectively by using deepfakes technology as a focal case. This dissertation comprises three interrelated studies.

The first paper reviews the literature on deepfakes and emerging technology governance, identifying the distinct characteristics of deepfake technology …

Beyond The Lace Index: Benchmarking Machine Learning Architectures And Explaining 30-Day Hospital Readmission Risk With Shap Analysis, Carl E. Hughes Iii

Williams Honors College, Honors Research Projects

Unplanned 30-day hospital readmission remains a fundamental challenge in US healthcare, associated with increased risk to patient recovery and representing an estimated $52.4 billion in annual expenses (Beauvais et al., 2022). While the rigorously validated LACE index serves as the clinical standard for readmission modeling, its linear structure and four explanatory variables lack the complexity to capture the high-dimensional and interactive nature of patient risk. This study utilizes an admission granularity level cohort of the MIMIC-IV database to develop and compare machine learning architectures against the baseline LACE index. Due to the imbalanced prevalence of readmission, the penalized logistic regression, …

Toward Efficient And Scalable Scientific Data Management Through Quality-Oriented Data Compression, Pu Jiao

Theses and Dissertations--Computer Science

Scientific simulations and instruments now produce data at rates that overwhelm the storage, memory, and network subsystems of modern high-performance computing (HPC) facilities. Error-bounded lossy compression reduces data movement costs while bounding reconstruction error, yet three barriers limit its adoption in mission-critical workflows: existing compressors cannot guarantee the accuracy of domain-specific quantities of interest (QoIs) derived from compressed data; compression-induced artifacts such as posterization, blocking, and interpolation banding erode user confidence in decompressed fields; and significant compressibility in the quantization index arrays of interpolation-based pipelines remains unexploited. This dissertation addresses all three barriers through four contributions, with the artifact barrier …

Secure The Database: A Red Team, Blue Team Analysis Of Sql Injection, Andrew N. Miller

Williams Honors College, Honors Research Projects

SQL injection (SQLi) attacks are a type of cyberattack that seeks to bypass website logins and gain entry to sensitive information. These pose a significant danger to organizations holding confidential user information. Personally Identifiable Information (PII) like physical addresses, emails, phone numbers, social security numbers are at risk of theft. Login credentials like usernames, passwords, and other sensitive information like financial details and social security numbers are also exposed through SQLi attacks. SQLi attacks harm the confidentiality, integrity, and availability of people’s identity. Additionally, data breaches that reach public battention harm the reputation and trust of organizations. SQLi attacks rank …

Swimming In Uncertainty: Filling Data Gaps And Providing An Educational Platform For Beach Water Quality At Tybee Island, Georgia, Lukas Roberson

College of Graduate Studies: Theses & Dissertations

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Swimming in beaches water contaminated with high levels of bacteria can make you sick. Current monitoring at the public beaches on Tybee Island consists of weekly monitoring and enumeration of fecal indicator bacteria that takes 24 hours for results. If the number of bacteria exceed regulatory limits, a public health advisory is issued, and affected waters are retested until …