Databases and Information Systems Commons^™

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 …

Integrating Gis With Interim Payment Valuation In Road Construction Projects: A Conceptual Framework, Abdulrahman I. Iro, Juma M. Matindana, Julian Ijumulana

Tanzania Journal of Engineering and Technology (TJET)

Abstract

Interim Payment Valuation is a critical process in road construction contract administration, yet conventional valuation practices remain heavily dependent on manual measurements, fragmented documentation, spreadsheets, and professional judgement. These limitations can affect measurement accuracy, transparency, traceability, and the timeliness of payment certification. Although Geographic Information Systems have increasingly been applied to construction planning, quantity measurement, progress monitoring, infrastructure management, and decision support, their integration with contractual and financial processes for interim payment valuation remains insufficiently explored. This study therefore develops a conceptual framework for integrating GIS with Interim Payment Valuation in road construction projects. A PRISMA-guided structured literature review …

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 …

Digitizing Land Records For A More Inclusive Future, Md Sharif Hasan Rafi '30

Annual Student Research Poster Session

This project supported the Land Data Digitization for Inclusive Growth Project in Bangladesh by transforming paper-based land records into structured and accessible digital information. The work involved data entry, verification, quality control, standardization, and metadata management using a web-based system. More than 12,500 land records were processed, with 98% passing quality checks. The experience demonstrates how accurate and standardized digital land data can improve transparency, accessibility, government planning, and inclusive land administration while reducing errors and disputes.

Enviromental Burden And Financial Performance Of Manufacturing Companies On Borsa Istanbul: An Exploratory Circular-Economy-Aligned Assessment Using The Aroman Method, Sibel Fettahoglu, Ejder Ayçin, Büşra Karslı Günay

Journal of Environmental Science and Sustainable Development

Türkiye's 2053 net-zero emissions target and the critical role of the manufacturing sector in this process necessitate an urgent examination of the financial impacts of circular economy (CE) practices. This study provides an exploratory analysis of the relationship between environmental burden and financial performance using 2023 data from 26 manufacturing companies in Borsa Istanbul. Given the difficulty of measuring holistic circularity at the company level, this study operationalizes the environmental dimension of circular economy performance using selected environmental burden indicators, including energy and water consumption, greenhouse gas emissions, and waste generation. The study uses the alternative ranking order method accounting …

Semantic Shields: Automating Critical Infrastructure Defense Via Nlp-Driven Ransomware Profiling, Henry Trowbridge, Ian Zalcberg, Ryan Schley, Carter Yagemann, Natasha Phan, Srikar Maduposu, Vimal Buck

Military Cyber Affairs

Ransomware poses a growing threat to critical infrastructure, where successful attacks can disrupt operational technology (OT) and industrial control systems (ICS) with significant public safety consequences. However, attributing ransomware incidents to specific threat actors remains challenging due to ransomware-as-a-service ecosystems, actor rebranding, and the obfuscation of traditional indicators of compromise. This paper presents Semantic Shields, an NLP-driven attribution framework that leverages BERT-generated semantic embeddings and DBSCAN clustering to profile ransomware actors through the linguistic characteristics of ransom notes. Using a dataset of 295 ransom notes from 189 distinct threat groups, the framework achieved an 87.2% true positive clustering rate and …

Urban Spatial Development Control In Tanzania: Analysis Of Factors Influencing Gis Application Using Structural Equation Modelling (Sem)., Happiness Protas Mmanda, Nestory Yamungu

Tanzania Journal of Engineering and Technology (TJET)

Rapid urbanization in developing countries has intensified urban expansion, creating challenges for sustainable development. Geographic Information Systems (GIS) enhance spatial planning, but empirical evidence on factors influencing their effectiveness remains limited. This study examines determinants of GIS application in Urban Spatial Development Control (USDC). The objectives are to (1) identify and categorize factors affecting GIS use, (2) assess relative strength, and (3) develop a validated structural model explaining GIS adoption in USDC. Data were collected from 103 LGAs by a mixed sampling method. Exploratory and Confirmatory Factor Analysis classified influencing factors into technology-related (α = 0.869, CR = 0.881), process-related …

Decision Support Tool For The Maintenance Of Meter Gauge Railway Permanent-Way Infrastructures: A Concept Paper, Hamisi J. Maulid, Beatus A.T Kundi, Juma M. Matindana, Ismail W. R. Taifa Dr

Tanzania Journal of Engineering and Technology (TJET)

A Decision Support Tool (DST) represents a transformative way of modernising maintenance practice on the permanent-way infrastructure of Meter Gauge Railway (MGR). Maintenance practice remains largely reactive, as the MGR plays a strategic role in transporting both freight and passengers, resulting in inefficient resource allocation, high operational risk, and growing Lifecycle expenses. This concept paper explores the potential and the design challenges for a DST that combines fuzzy analytic hierarchy process (Fuzzy-AHP) modelling, multi-criteria decision analysis (MCDA), Geographic Information Systems (GIS) and predictive machine learning (ML) analytics for evidence-based, future-oriented maintenance management. The paper draws on peer-reviewed studies from …

From Data To Victory: The Race For Analytic Superiority In Warfare, Robert Grossman, Emily Goldman

Joint Force Quarterly

Artificial intelligence technologies have reached a tipping point after decades of development. They are diffusing widely across defense and national security applications. Twenty-first century warfighters rely on analytic models in all systems, at all echelons, and in all domains. As more powerful models built on ever larger data sets become ubiquitous, militaries are in a new competition to deploy artificial intelligence. Operational art must embrace “analytic superiority.” This is the operational advantage from collecting and ingesting data, building robust models and computing infrastructure, deploying the models into operational systems, and denying adversaries' ability to do the same

This article explains …

From Data To Decision-Making: The Role Of Local Digital Twins In Cross-Domain Management Within Municipalities – A Research-In-Progress Study In Veenendaal, Diana M.E. Boekman, Koen Smit, Guido Ongena, Rob Peters

Communications of the IIMA

Municipalities are facing increasingly complex, interconnected challenges in areas like housing, climate adaptation, mobility, and social policy. Local Digital Twins (LDTs) are seen as a promising tool to make this complexity more understandable and support decision-making. At the same time, both literature and practice show that few initiatives get past the pilot phase, even though getting through that phase is essential for successful long-term adoption.

This paper presents a research-in-progress study on the development and application of an implementation method for LDT technology within the municipality of Veenendaal, based on human values rather than driven by technological possibilities. Based on …

Facevalue: Exploring Real-Time Self-View Overlays To Prompt Meaning-Oriented Self-Awareness In Remote Meetings, Gun Woo (Warren) Park, Anthony Tang, Fanny Chevalier

Research Collection School Of Computing and Information Systems

In remote video meetings, visual non-verbal cues, such as facial expressions or head movements, are seen continuously but often only partially. This increases ambiguity compared to in-person settings and can cause misinterpretation or misalignment between intended and perceived meaning. Motivated by communication theories, we designed FaceValue, a technology probe that augments the self-view with private, real-time overlays. These overlays are subtle, suggestive prompts intended to help attendees reflect on how their cues might be interpreted by others. To invite personal interpretation, FaceValue avoids behavioral labeling and instead aims to support meaning-oriented self-awareness: recognizing when visible cues may unintentionally (mis)communicate intent. …

Sequential Causal Architecture For Multimodal Aviation Accident Prediction, Kaitlyn Cavanaugh, Isaac Morrison

Discovery Day - Daytona Beach

Aviation accidents are rarely the result of a single failure but rather from a complex causal chain of latent failures. While traditional data mining models often predict incident occurrence, they frequently overlook the sequential mechanics defined by known accident causation theoretical frameworks like the Swiss Cheese Model and the FAA's HFACS. This project addresses the need for interpretable, reliable, multi-stage forecasting by proposing a Sequential Causal Architecture that transforms theoretical causation models into a structured Directed Acyclic Graph (DAG) for multimodal accident causation chain prediction. Data from the NTSB and DOT is used and connected together in a meaningful way …

Dcat - Distributed Computing And Analysis Tool, Asher Zwickel, Jacob Burdge

Discovery Day - Daytona Beach

This project uses distributed computing to process and analyze large datasets related to cyber breaches and attacks. Its main goal is to find patterns between initial cyber incidents and what happens next. It looks at whether responses tend to escalate, calm down, or stay about the same over time. Understanding this helps explain how digital conflicts develop and whether they follow predictable paths. The project was built as part of university research and runs on custom software across a cluster of 17 Chromebooks. While the system can study many topics, it is currently focused on cyber activity. The software uses …

Technical Reliability As Foundation Of Mobile Self-Checkout Experience, Pumezile O. Dyantyi, Fazlyn Petersen

African Conference on Information Systems and Technology

Although mobile self-checkout (MSCO) apps are becoming more common in retail stores, their long-term success in emerging markets depends heavily on the system’s technical reliability. This qualitative thematic study of 888 user reviews for four established MSCO apps investigates how technical reliability shapes the user experience (UX) after app adoption. Using an integration of the UX Honeycomb model and the UTAUT2 framework, this paper demonstrates that technical reliability is a core UX element that affects usability, performance, trust, value, satisfaction and continued use. Technical reliability problems such as system outages, login challenges and transaction difficulties negatively impact several core UX …