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Exploring New Managerialism In Human Resource Membership Organizations, Pamela J. Green Oct 2026

Exploring New Managerialism In Human Resource Membership Organizations, Pamela J. Green

Doctoral Dissertations and Projects

Nonprofit membership organizations increasingly adopt new managerialism and for-profit-style business practices to strengthen sustainability, improve performance, and respond to changing revenue conditions. However, these practices may create tension when they appear to conflict with member-focused mission, collaborative decision-making, knowledge sharing, volunteer capacity, and leadership loyalty. The purpose of this flexible, multiple-case study was to explore the challenges nonprofit human resource membership organizations faced when adopting new managerialism and to investigate how this business model influenced organizational mission, leadership values and practices, knowledge sharing, organizational conflict, and leadership loyalty to the nonprofit mission. Guided by a constructivist paradigm, the study explored …

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Pandemic-Driven Mediatization Of The Seventh-Day Adventist Church In Jamaica, Gervon Garcia Marsh Oct 2026

Pandemic-Driven Mediatization Of The Seventh-Day Adventist Church In Jamaica, Gervon Garcia Marsh

Doctoral Dissertations and Projects

The Seventh-day Adventist Church in Jamaica experienced a radical shift in its relationship with information and communication technology (ICT) due to the COVID-19 mandatory lockdowns imposed by the Government of Jamaica. Prior to the pandemic, ICT use was less pronounced in the life and ministry of the Church, suggesting limited susceptibility to mediatization. This shift accelerated the mediatization of the Church, leading to a heavy reliance on ICT that has become entrenched in its life and ministry post-pandemic. This qualitative exploratory case study examined how pandemic-driven mediatization shaped the collective agency of the Seventh-day Adventist Church in Jamaica through ICT, …

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Cultivating A Culturally Relevant And Inclusive Elementary Music Classroom, Elisabeth Virginia Marie Buzzell Oct 2026

Cultivating A Culturally Relevant And Inclusive Elementary Music Classroom, Elisabeth Virginia Marie Buzzell

Doctoral Dissertations and Projects

While programs such as the Diversity, Equity, and Inclusion Act (DEI) and the practice of Culturally Responsive Education (CRE) existed for much of the last decade, representation of marginalized and intersectional identities in traditional music education is often limited to world culture units, rather than being consistently integrated throughout the curriculum. The social understanding of CRE and DEI often limits their purpose to racial diversity; however, these philosophies affirm people from any marginalized class, including those based on gender, disability, race, culture, community, orientation, socioeconomics, or simply bringing one’s lived experiences into a valued perspective. The lack of integration happens …

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A Wicked Humanity Original Sin In The Theology Of John Wesley And Jonathan Edwards, Vincent Andrew Sullivan Oct 2026

A Wicked Humanity Original Sin In The Theology Of John Wesley And Jonathan Edwards, Vincent Andrew Sullivan

Doctoral Dissertations and Projects

The source of human wickedness is not in a fault in our education, but a fault in our nature. Ignorance is not the root of human depravity; rather, there is an innate propensity in the human soul to gravitate, not towards the light, but towards the darkness. This truth, i.e., the doctrine of Original Sin, was fiercely maintained in the 18th century by John Wesley, the Father of Methodism, and by Jonathan Edwards, the last of the Puritans. Both of these men each wrote a large treatise on this same topic matter, that of Original Sin, and for the same …

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Understanding The Lived Experiences Of Gun Violence On Psychological Functioning And Perceived Psychosocial Supports In St. Louis’S Marginalized And Underserved Communities, Desirae Mecole Martinez Oct 2026

Understanding The Lived Experiences Of Gun Violence On Psychological Functioning And Perceived Psychosocial Supports In St. Louis’S Marginalized And Underserved Communities, Desirae Mecole Martinez

Doctoral Dissertations and Projects

The Centers for Disease Control and Prevention (CDC) identifies gun violence as a leading public health concern in the United States. Marginalized and underserved communities are disproportionately impacted by poverty and crime, including firearm violence. Gun violence exposure has been linked to complex trauma, anxiety, substance use disorders, and depression. St. Louis, Missouri, has one of the highest rates of gun violence and mortality in the nation; however, research lacks an understanding of the psychological toll. Limited phenomenological research has examined how adults residing in marginalized and underserved communities experience gun violence exposure psychologically and perceive psychosocial supports. This study …

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A Phenomenological Study: Life During The Pandemic As An Adolescent In Hidalgo County, Texas, And Life Today, Melissa Guevara-Pérez Oct 2026

A Phenomenological Study: Life During The Pandemic As An Adolescent In Hidalgo County, Texas, And Life Today, Melissa Guevara-Pérez

Doctoral Dissertations and Projects

The COVID-19 pandemic disrupted the lives of adolescents during a critical part of social, emotional, and identity development. Although previous research has noted the immediate effects of the pandemic on adolescents, limited research has examined the long-term impact on individuals who experienced the pandemic during adolescence and are now transitioning into adulthood. The purpose of this qualitative phenomenological study was to explore the lived experiences of Hispanic young adults who experienced the COVID-19 pandemic as adolescents and to understand how those experiences influenced their social and personal development as they transitioned into adulthood. Participants included 10 Hispanic young adults who …

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Building A Defense Economy: World War Ii, Federal Investment, And Georgia’S Economic Transformation, 1940-1965, William Heath Taylor Oct 2026

Building A Defense Economy: World War Ii, Federal Investment, And Georgia’S Economic Transformation, 1940-1965, William Heath Taylor

Doctoral Dissertations and Projects

This dissertation examines how World War II transformed Georgia’s economy from a predominately rural, agricultural economy into a more diversified economy increasingly tied to federal investment, military activity, industrial production, transportation, and commerce. It argues that wartime mobilization accelerated this transformation by directing federal funds into military installations, defense industries, infrastructure, and public services while drawing workers into expanding centers of employment. Using federal records, census data, military publications, corporate reports, newspapers, and oral histories, the study examines Fort Benning, Robins Field, Camp Toccoa, Bell Aircraft, the Brunswick Shipyards, Delta Air Lines, and The Coca-Cola Company. These cases show how …

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Interpreting Visibility: Leadership And Employee Perspectives On Performance, Trust, And Commitment In Hierarchical Organizations, Joshua A. Hart Oct 2026

Interpreting Visibility: Leadership And Employee Perspectives On Performance, Trust, And Commitment In Hierarchical Organizations, Joshua A. Hart

Doctoral Dissertations and Projects

The purpose of this qualitative multiple-case study was to examine how leaders and employees interpreted workplace visibility and presence within hierarchical organizations. The researcher conducted semistructured interviews with 30 participants, including 15 leaders and 15 employees, across nongovernmental, civilian government, and military cases. The researcher organized 43 codes into seven themes. Participants described workplace visibility as selective access to employee activity rather than complete evidence of performance, commitment, reliability, motivation, or engagement. They described attendance, responsiveness, communication, and visible output as more perceptible aspects of work, while preparation, technical reasoning, informal coordination, and internal experience were less apparent. Participants depicted …

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Provorov Fights Pride Night: Hockey, Homophobia, And Reactionary Athlete Activism, Matthew R. Meier Oct 2026

Provorov Fights Pride Night: Hockey, Homophobia, And Reactionary Athlete Activism, Matthew R. Meier

Communication & Theatre Faculty Publications

The 2022–2023 NHL season witnessed a spate of reactionary protests to team-sponsored Pride Nights by players set off by Philadelphia Flyers defensemen Ivan Provorov. In this essay, I consider the reactionary side of athlete activism through an analysis of these protests. Drawing on a critical rhetorical perspective, I critique the discourse of players, coaches, teams, and the league to outline the discursive features of athlete activism that advocates for the hegemonic status quo. I argue that these reactionary protests feature a refusal to engage in public discourse, a reliance on individual beliefs and personal choices, and tacit institutional approval from …

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International Nurse Leaders' Journeys Of Engagement, Belonging, And Growth In Professional Nursing Organizations: A Qualitative Study, Jihane Frangieh, Edmund J. Walsh, Kay Kennedy, Kim Crawford, Cindy Zellefrow, Beverly Hancock, Mary A. Dolansky Oct 2026

International Nurse Leaders' Journeys Of Engagement, Belonging, And Growth In Professional Nursing Organizations: A Qualitative Study, Jihane Frangieh, Edmund J. Walsh, Kay Kennedy, Kim Crawford, Cindy Zellefrow, Beverly Hancock, Mary A. Dolansky

Individual Open Access Articles

Introduction: Professional nursing organizations play a central role in connecting nurse leaders across borders, advancing leadership science, and addressing shared challenges, including workforce sustainability, burnout, and policy influence. Little is known about how specialized professional nursing organizations with a primarily national membership base can build and sustain meaningful international membership. Barriers to and facilitators of international membership in professional nursing leadership organizations have received limited attention in the literature, particularly from the perspectives of interna-tional members themselves. The aims of the study are to explore the perspectives of international nurse leaders enrolled in the Association for Leadership Science in Nursing …

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Sixth-Grade Inclusive Chorus Curriculum, Lauren Rancourt Eubanks Oct 2026

Sixth-Grade Inclusive Chorus Curriculum, Lauren Rancourt Eubanks

Masters Theses

This curriculum is an eighteen week block plan for sixth-grade inclusive chorus. Emphasizing adaptive activities and specific strategies for students with disabilities, this curriculum can be used with any middle school repertoire concert plan. Through visual aids and color coding, students with disabilities can participate effectively in the performance of melodic contour, rhythm, and repertoire. This curriculum is for a sixth-grade chorus that is preparing for a concert, in which students rehearse two to three times weekly. Drawing from the Orff approach and Dalcroze techniques, this curriculum has adaptions to activities in which students can demonstrate their understanding of musical …

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An Intelligent Sdn-Enabled Framework For Real-Time Ipv6 Ip Spoofing Detection And Prevention, Navaneethan C. Arjuman, Ahmad Fikri Sharudin, Zar Chi Hlaing, Supriyanto Praptodiyono Oct 2026

An Intelligent Sdn-Enabled Framework For Real-Time Ipv6 Ip Spoofing Detection And Prevention, Navaneethan C. Arjuman, Ahmad Fikri Sharudin, Zar Chi Hlaing, Supriyanto Praptodiyono

Journal of Informatics and Web Engineering

With the increase in the adoption of Internet Protocol version 6 (IPv6), the issue of Internet Protocol (IP) spoofing has remained a significant risk that damages the integrity of networks. Although some countermeasures, like Secure Neighbor Discovery (SEND) and Router Advertisement (RA) Guard, are available, these solutions are complex to deploy, consume high resources, and can be bypassed by advanced attacks. This paper provides a critical review of the available IPv6 spoofing prevention methods and proposes an enhanced multi-layered framework that uses improved ingress and egress filtering, Artificial Intelligence (AI)-based anomaly detection, and Software-Defined Networking (SDN) to automatically mitigate threats …

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Mask-Resilient Multi-Biometric Authentication For Healthcare, Caleb Yeo, Siew-Chin Chong, Kian-Ming Lim, Lee-Ying Chong Oct 2026

Mask-Resilient Multi-Biometric Authentication For Healthcare, Caleb Yeo, Siew-Chin Chong, Kian-Ming Lim, Lee-Ying Chong

Journal of Informatics and Web Engineering

The fast integration of digital healthcare platforms, including telemedicine and electronic health records, has created a significant need for secure, contactless authentication methods. Traditional login methods such as passwords and PINs are increasingly insufficient for ensuring the security of sensitive patient data. This paper presents a robust, lightweight Healthcare Authentication System that five biometric modules to address major challenges in facial recognition: spoofing attacks, liveness detection, and facial occlusion due to mask-wearing. The system leverages RetinaFace for face detection, a MobileNetV2 model for mask classification, a lightweight CNN for blink-based liveness verification, and MiniFASNet for spoof detection. For identity matching, …

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Structured Data Preprocessing Techniques For A Multilingual Healthcare Dataset In Machine Learning Applications, Md. Abu Nishan, Choo-Yee Ting, Chih-Yang Pee, Helmi Zakariah, Thanh-Trung Nguyen Oct 2026

Structured Data Preprocessing Techniques For A Multilingual Healthcare Dataset In Machine Learning Applications, Md. Abu Nishan, Choo-Yee Ting, Chih-Yang Pee, Helmi Zakariah, Thanh-Trung Nguyen

Journal of Informatics and Web Engineering

Data preprocessing is a foundational stage in any machine learning (ML) workflow, especially when dealing with complex, multilingual, and real-world healthcare datasets. The quality, consistency, and structure of input data strongly influence the accuracy, fairness, and interpretability of predictive models. This paper presents a comprehensive and structured preprocessing framework designed for a large multilingual healthcare dataset containing 19,536 patient records with 67 features encompassing demographic, clinical, and socioeconomic information. The framework systematically addresses data heterogeneity, linguistic diversity, and structural inconsistencies through multiple stages of cleaning and transformation. The preprocessing process includes translation of Malay text into English to ensure linguistic …

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Comparative Analysis Of Time Series Forecasting Models For Stock Price Prediction: Arima, Lstm, And Prophet, Muhammad Syahid Pebriadi, Tino Kemal Fattah, Putriana Salman, Rizky Amelia Oct 2026

Comparative Analysis Of Time Series Forecasting Models For Stock Price Prediction: Arima, Lstm, And Prophet, Muhammad Syahid Pebriadi, Tino Kemal Fattah, Putriana Salman, Rizky Amelia

Journal of Informatics and Web Engineering

Stock price forecasting with a uniform approach is a challenging process due to nonlinearity, regime changes, and seasonality. The following paper would like to address the practical problem of selecting an appropriate model through comparing three widely used models such as Auto Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Prophet based on how they are applied to different attributes of data and forecast-related tasks. The structured direct comparison will be our proposal, as to be able to identify the strengths and shortcomings in abiding by each model. Each of the model is methodologically trained and tested on …

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Haptic Technology In Networked Digital Applications: A Literature Review, Tharanitharan Chandran, Ngu War Hlaing, Athirah Mohd Ramly Oct 2026

Haptic Technology In Networked Digital Applications: A Literature Review, Tharanitharan Chandran, Ngu War Hlaing, Athirah Mohd Ramly

Journal of Informatics and Web Engineering

Haptic-enhanced network applications provide a breakthrough merger of haptic technology with digital environments, allowing for immersive and responsive interactions. In contrast to traditional interfaces that rely primarily on visual and auditory feedback, these applications add tactile sensations, boosting user engagement in domains such as telemedicine, Virtual Reality (VR), Augmented Reality (AR), gaming, and remote collaboration. This study adopts a systematic literature review methodology to identify, filter, and analyse relevant academic sources published between 1998 to 2024, to examine accomplishments, challenges, and future directions of haptic technology in network applications. The study investigates actuator technology, tiny haptic devices, and adaptive algorithms …

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A Comparative Study Of Machine Learning Algorithms For Customer Churn Prediction In Ecommerce, Tan Yi Qi, Lew Sook Ling, Ram Avtar Oct 2026

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. …

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Recycle Tracker Application: A Gamified Mobile Platform To Enhance Recycling Behaviour, Yi Han Ng, Pey Yun Goh, Lee Yeng Ong, Kian Ming Lim Oct 2026

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 …

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Sql Optimization In E-Commerce Platforms Using Large Language Models: A Multi-Pattern Query Empirical Study, Min Ming Lee, Nabhan Salih Oct 2026

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 …

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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 Oct 2026

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 …

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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 Oct 2026

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 …

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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 Oct 2026

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, …

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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 Oct 2026

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 …

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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 Oct 2026

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 …

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Calibrated Probabilistic Predictions For Phishing Email Detection Using Sentence-Bert With Machine Learning Models, Oluwasegun Abiodun Abioye, Abraham Evwiekpaefe, Philip Odion Oct 2026

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 …

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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 Oct 2026

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 …

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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 Oct 2026

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, …

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Web Accessibility And Search Engine Optimization: Best Approach For Building Corporate Websites, Jeremiah Charles, Khaled Mahbub Oct 2026

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 …

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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 Oct 2026

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, …

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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 Oct 2026

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 …

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