Digital Commons Network^™

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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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 …

Diabetes Mortality In Georgia: Disparities, Social Determinants, And Rural-Urban Differences (2019-2021), Gwendolyn H. Cattledge, Temilola N. Luwoye, Gloria Elizabeth B. Ogionwo, Nyshanti B. Ross, Zachary M. Bremmer

Journal of the Georgia Public Health Association

Background: Diabetes mellitus remains a significant public health challenge globally and, in the U.S., notably impacts mortality. Georgia specifically faces a substantial burden, with disparities observed across populations and counties. Understanding the patterns and underlying drivers of diabetes-related deaths statewide is crucial for effective public health intervention.

Methods: This cross-sectional study analyzed age-adjusted diabetes mortality rates in Georgia from 2019-2021, using data from the Georgia Department of Public Health's OASIS and the CDC's Social Vulnerability Index (SVI) 2020. The study aimed to detail the distribution of mortality rates across counties, explore the role of social determinants of health, and analyze …

Supporting The Adoption Of Digital Technology On-Farm: Ten Tips For Extension, David Rose, Kayleigh Crouch, Lisa Blix Germundsson, Myriam Gaspard, Onno Giller, Livia Ortolani, Robert Strong

Journal of Extension

We held a symposium to exchange lessons from U.S. and European experts on how Extension can be best designed to support adoption of digital agricultural innovation. We highlight 10 tips for an Extension audience in the United States: (a) ensure connectivity between all actors involved in agricultural innovation, (b) utilize the private sector, (c) advocate for adoption costs, (d) shape an enabling digital ecosystem, (e) create a service, not just a technology, (f) update Extension agents' digital skills, (g) support farmer-led innovation, (h) capture everyday farmer practices, (i) monitor and evaluate progress, and (j) offer long-term support.

Contextualizing Trends In Television Viewing And Computer Use Among U.S. Children Aged 5–11 Years, 2001–2018, Ashley W. Kranjac, Dinko Kranjac

Sociology Faculty Articles and Research

Introduction

Excessive screen time among children is linked to adverse physical, mental, and developmental outcomes. The putative drivers of increasing screen time among school-aged children in the United States remain unclear. Population-level changes may arise from intracohort change (IC)—change in average screen time across survey cycles—or cohort replacement (CR), as later-born cohorts with different levels of screen time replace earlier-born cohorts.

Methods

We analyzed nine 2-year National Health and Nutrition Examination Survey (NHANES) cycles from 2001 to 2002 through 2017–2018, including 9,841 children aged 5–11 years old. Screen time was measured as the sum of proxy-reported estimates of daily hours …

Spartan Daily, October 14, 2026, San Jose State University, School Of Journalism And Mass Communications

Spartan Daily, 2026

Volume 167, Issue 23

Examining How The Frequency Of Artificial Intelligence Use In The Workplace Relates To Employee Self-Efficacy And Job Satisfaction, Dalton R. Vail

Doctoral Dissertations and Projects

Artificial intelligence is a prominent topic in society. Its adoption in the healthcare sector has occurred rapidly. Nonetheless, much existing research focuses on clinical healthcare professionals, thereby overlooking those in non-clinical roles. Non-clinical roles make up a small portion of the healthcare industry, but they account for a large share of burnout in the field. This quantitative study aimed to explore the relationships among artificial intelligence, job satisfaction, and self-efficacy. It posited that these variables are interconnected concerning artificial intelligence in a non-clinical context. Furthermore, the study sought to determine whether job satisfaction and self-efficacy are influenced by how often …

The Impact Of Active Killer Defense Training Preparations And The Church, Michael Edward Howell

Doctoral Dissertations and Projects

The purpose of this grounded theory (GT) study was to develop and understand the training and prevention tactics used in defending the church against active killer violence. Exploring the Routine Activity Theory (RAT) developed by Cohen and Felson (1979) in relation to active killer violence aimed to contribute significantly to building a more robust understanding of related problems associated with active killer violence in the world today in terms of a motivated offender, suitable target, and a lack of a capable guardian. The central phenomenon of this GT study focused on the understanding of the lethal threat from active killer …

Exploring Potential Psychological, Behavioral, And Physiological Predictors Of Human Flourishing In Division 1 Athletes: Toward A More Comprehensive Mental Health Screening, Derek Aaron Wheeler

Doctoral Dissertations and Projects

The National Collegiate Athletic Association (NCAA) has created numerous task forces and guidelines to address the rise in mental health challenges faced by student-athletes. However, these efforts have largely focused on identifying psychopathology rather than promoting flourishing. The purpose of this study was to examine the predictive utility of self-report and objective measures of human flourishing among NCAA Division I student-athletes at initial screening (i.e., preparticipation exam). This retrospective, cross-sectional, correlational study analyzed archival data from 92 student-athletes from 13 different sports. Measures included the Secure Flourishing Index (SFI), Patient Health Questionnaire-9 (PHQ-9), Athlete Sleep Behavior Questionnaire (ASBQ), and Power …

Preparing Nurses For Behavioral Health Crisis, Juila Harte, Tori Bates, Ugochinyere Duruaku, Kateri Cincurak

Cohort 37

No abstract provided.

Clinical Social Workers’ Process Of Diagnosing Black Women With Body Dysmorphic Disorder, Deilia M. Mclaughlin

Walden Dissertations and Doctoral Studies

Body dysmorphic disorder (BDD) is a mental health condition characterized by the persistent preoccupation with perceived or actual flaws in one’s physical appearance and is often underdiagnosed or misdiagnosed among Black women. Grounded in social learning theory, the purpose of this generic qualitative study was to explore the diagnostic process executed by licensed clinical social workers when assessing Black women expressing symptoms of BDD. Five licensed clinical social workers with experience diagnosing and treating mental health disorders participated in in-depth semistructured interviews, which were analyzed using thematic analysis. Six themes emerged: (a) diagnosis of BDD requires longitudinal clinical assessment, (b) …

Perceptions Of Jail Administrators Regarding Implementation Of State Jail Standards, Penelope Dawn Sapp

Walden Dissertations and Doctoral Studies

Jails have increasingly become de facto institutions for managing individuals with severe mental illness (SMI) and opioid use disorder. Yet, jail administrators often lack resources and treatment capacity needed to adequately serve this population, which was supported by existing literature that highlighted resource insufficiencies including clinical resources. Since the late 1990s, the population of SMI individuals held in jails has tripled, a trend attributed to declining community mental health infrastructure and limited alternatives to arrest. the purpose of this qualitative ethnographic study was to examine the challenges Washington State jail administrators faced in managing these individuals while balancing priorities among …

Improving Accessibility For Individuals With Neurological Diseases: Stakeholder Strategies For Effective Legislation In U.S. Aviation, Teresa Thurtle

Walden Dissertations and Doctoral Studies

Individuals with neurological diseases and their caregivers may encounter inconsistent accessibility practices, inadequate staff support, and physical design barriers during U.S. commercial air travel, despite disability protections. Although the extant literature documents accessibility challenges for travelers with disabilities, less is known about how this population experiences the implementation of aviation policy, staff training, and design. The purpose of this generic qualitative study was to understand what participants described as needed in effective laws and policies to improve accessibility on commercial aircraft. Policy feedback theory and evidence-based policymaking provided the conceptual framework. The central research question asked what participants described as …