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Measurements And Analysis Of Light Non-Methane Hydrocarbon Mole Fractions In 1982–1987 At Six Remote Sites Along The Pacific Rim, A. M. Smith, C. L. Butenhoff, M. A. K. Khalil, A. L. Rice Oct 2026

Measurements And Analysis Of Light Non-Methane Hydrocarbon Mole Fractions In 1982–1987 At Six Remote Sites Along The Pacific Rim, A. M. Smith, C. L. Butenhoff, M. A. K. Khalil, A. L. Rice

Physics Faculty Publications and Presentations

Atmospheric non‐methane hydrocarbons (NMHCs) are important trace species with anthropogenic and natural sources including fugitive emissions from fossil fuel mining and distribution, biomass burning, biofuel combustion, and biogenic emissions from microbes and plants. We present previously unpublished whole air sample measurements of NMHCs collected at approximately weekly intervals from 1982 to 1987 at six stations spanning the Northern and Southern hemispheres, which sampled primarily Pacific Basin air masses as determined by back‐trajectory analysis. The four alkanes in this data set, ethane, propane, i‐ and n‐butane had a mean measured mole fraction of 712 ± 22 ppt, 245 ± 13 ppt, …

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On A Remark Of De Gennes Concerning Three-Dimensional Polyelectrolytes, Shiquan Li, Carl Mueller Oct 2026

On A Remark Of De Gennes Concerning Three-Dimensional Polyelectrolytes, Shiquan Li, Carl Mueller

Journal of Stochastic Analysis

This work is inspired by a remark of de Gennes [3] about polyelectrolytes, which are charged polymers. A common model for a polymer is a self-avoiding or self-repelling random walk or Brownian motion. For polyelectrolytes, the repelling potential is the Coulomb potential arising from pairs of charged particles. We show that in the continuous case of a Brownian motion in three dimensions, the spread of the polymer, in particular the radius of gyration of a polyelectrolyte of length T grows linearly with T, up to logarithmic corrections.

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How Wide Is The Screening Window For Aggressive Non-Small Cell Lung Cancers? The Cure Threshold Metric And Its Implications For Lung Cancer Screening Guidelines, Deborah Goldwasser Oct 2026

How Wide Is The Screening Window For Aggressive Non-Small Cell Lung Cancers? The Cure Threshold Metric And Its Implications For Lung Cancer Screening Guidelines, Deborah Goldwasser

Mathematics Colloquium Series

The entire NSU community, including students at all levels of mathematics, is invited and encouraged to attend. A reception with coffee and snacks will take place in the Department of Mathematics Suite Parker 320 from 12:00-12:30 just prior to the colloquium talk. Abstract: Lung cancer screening by low-dose computed tomography (LDCT) has demonstrated a mortality reduction in two large randomized controlled trials. However, LDCT also frequently identifies benign pulmonary nodules in high-risk but otherwise healthy individuals. The management of small pulmonary nodules is guided primarily by malignancy probability.  However, the surgical cure threshold, the maximum tumor size at which surgical …

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Hidden Thermal Signatures Reveal The Microclimatic Influence Of Scheduled Cave Tourism, Matt Rowberry, John Gunn, Andrew Smith Oct 2026

Hidden Thermal Signatures Reveal The Microclimatic Influence Of Scheduled Cave Tourism, Matt Rowberry, John Gunn, Andrew Smith

International Journal of Speleology

Tourist activity is widely recognised as a source of cave microclimate disturbance, yet visitor-induced variability is difficult to distinguish from natural environmental fluctuations. The coronavirus pandemic created a natural experiment at Poole’s Cavern, United Kingdom, in which periods of complete closure could be compared with contrasting visitor access regimes. Two years of high resolution temperature records from six monitoring points were analysed. Harmonic regression and a state space model removed deterministic diurnal cycles and lower frequency temperature variability. The remaining residual variability differed systematically among visitor access regimes and between daytime and nighttime conditions. Spectral analysis of the residuals identified …

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Stewards Of Data: A Practical Handbook For Undergraduate Researchers In Engineering And Applied Sciences, Şenay Purzer, Wei Zakharov, Carla B. Zoltowski Oct 2026

Stewards Of Data: A Practical Handbook For Undergraduate Researchers In Engineering And Applied Sciences, Şenay Purzer, Wei Zakharov, Carla B. Zoltowski

Purdue University Press Books

Filled with actionable guides and real-world examples, Stewards of Data: A Practical Handbook for Undergraduate Researchers in Engineering and Applied Sciences allows readers to utilize the provided templates and guides almost immediately. In today’s world, data is ubiquitous and AI is growing exponentially. Practicing ethical data management is, therefore, crucial. Ensuring that data is findable, accessible, interoperable, and reusable is essential. This book is an interdisciplinary effort aimed at embedding ethical data life-cycle management education into undergraduate research. The authors present a Data Steward Framework, which serves as a guide for undergraduate researchers, the graduate students who mentor them, postdocs, …

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Carbamate Toxicosis In Carnaby’S Cockatoos (Zanda Latirostris) In A Drying Climate, A Le Souef, Z M. Kissane, David Forshaw, K J. Riley, K Payne, T Hawkins, J Coggins, J M. Shephard, R Vaughn-Higgins, K S. Warren Oct 2026

Carbamate Toxicosis In Carnaby’S Cockatoos (Zanda Latirostris) In A Drying Climate, A Le Souef, Z M. Kissane, David Forshaw, K J. Riley, K Payne, T Hawkins, J Coggins, J M. Shephard, R Vaughn-Higgins, K S. Warren

Climate Science Research Articles

Pesticide exposure poses a growing threat to the health of wild avian populations worldwide. This report documents the first detections of agricultural chemicals in the tissues of wild Carnaby’s cockatoos (Zanda latirostris) in Western Australia. The events, characterised by morbidity and mortality, occurred in a national park near Perth, following periods of elevated summer temperatures over two seasons. Toxicological analysis of liver and digestive tract contents identified multiple pesticides and an herbicide, including methomyl, a highly toxic carbamate insecticide. Hot weather conditions preceding the events potentially increased the cockatoos’ use of alternative water sources for drinking, such as those in …

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A Note On The Voronoi Congruences And The Residue Of The Fermat Quotient, Claire Levaillant Oct 2026

A Note On The Voronoi Congruences And The Residue Of The Fermat Quotient, Claire Levaillant

Communications on Number Theory and Combinatorial Theory

We prove a congruence on the residue of the Fermat quotient in base a which arises from a generalization of the Voronoi congruences and other congruences on sums and weighted sums of divided Bernoulli numbers. As an application in the base 2 case, we recover a congruence for the generalized harmonic number H2,(p-1)/2, a generalization originally due to Z-H. Sun of a classical congruence known for the harmonic number H(p-1)/2 as a special case of the Lerch formula. We find a sharpening of the Voronoi congruences that is different from the one of …

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Restructuring Our Agriculture Industry To Reduce The Eutrophication Of The Great Lakes, Kathryn B. Dunn Oct 2026

Restructuring Our Agriculture Industry To Reduce The Eutrophication Of The Great Lakes, Kathryn B. Dunn

Tredway Library Prize for First-Year Research

No abstract provided.

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Representational Power As A Framework For Fairness With Applications To Redistricting, Jeffrey Barton Oct 2026

Representational Power As A Framework For Fairness With Applications To Redistricting, Jeffrey Barton

Mathematics Sciences: Faculty Publications

We introduce a framework for assessing fairness in electoral outcomes based on a new notion of representational power that determines a group's power as a combination of its size and how advantageously its voters are situated given the electoral rules. The framework is adaptable to a variety of contexts, and it produces intuitive results. When applied to redistricting, we call the measure the geographic representational baseline. The new measure is deterministic, accessible to non-experts, and relatively simple to calculate. The results generated by the new method are overall mutually reinforcing with those of a variety of ensemble methods for partisan …

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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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Adaptive And Learned Query Optimization: Techniques, Architectures, And Emerging Trends, Rehab Mohammed Abdelhalim Mokhtar, Palanichamy Naveen Oct 2026

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 …

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An Intelligent Tourism Recommender System Using Retrieval-Guided Large Language Models, Wei-Jun Lim, Nabhan D. Salih, Hairulnizam Mahdin, Ammar Alazab Oct 2026

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 …

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