Digital Commons Network^™

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 …

Investigating The Relationship Between Locale, Transportation, And Chronic Absenteeism In Rural Schools, Dawn T. Cook

College of Education

Chronic absenteeism poses a severe threat to public education, directly eroding student achievement by compounding instructional time loss. This correlational study, grounded in bioecological systems theory, examined the relationship between student transportation access, regional isolation, and chronic absenteeism within public elementary and middle schools (grades 3–8) in Alabama. The study used primary self-reported data from a standardized parent survey administered across five public schools and secondary archival records covering every rural public school in Alabama. I analyzed the data using binary logistic regression models and a one-way Analysis of Variance (ANOVA). The results showed that primary transportation modality (bus versus …

Muscle Shoals Canal Nomination To The Alabama Engineering Hall Of Fame Correspondence, 7.15.1992, William L. Mcdonald

Correspondence

Correspondence from William L. McDonald to Mrs. Bette D. Jefcoat, Assistant Secretary and Treasurer of the State of Alabama Engineering Hall of Fame, concerning the nomination of the Muscle Shoals Canal.

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

Spartan Daily, 2026

Volume 167, Issue 23

Modelling The Financial Viability Of Offshore Aquaculture Using A Case Study Of A Sea Bass Farm In The Bay Of Lion, France, Jeremy Van Dyken, Houshang Habibniya, Suzan Dsouza, Tyler Sclodnick

Journal of Ocean and Coastal Economics

The extension of net pen aquaculture to open ocean sites provides many advantages to the industry including opportunity for expansion, utilization of high energy sites, and better resilience to certain near shore or surface-associated stressors. The industry has been slow to develop due to high perceived risks and a lack of operating farms to provide financial information for open ocean farm operations. A model was developed to evaluate the economic viability of a hypothetical sea bass aquaculture venture in the open French Mediterranean Sea that utilizes open ocean submersible farming systems. Advanced open ocean equipment, including submersible net pens, environmental …

Valuation Methods And Their Relevance In Achieving Marine Protected Areas Targets: A Systematic Review, Jacob Ankamah, Stephen Hynes, Daragh O'Leary

Journal of Ocean and Coastal Economics

The prolific human-induced pressures on marine ecosystems leading to negative externalities such as beach erosion, biodiversity loss, and climate change are matters of global concern. Marine protected areas (MPAs) have emerged as a tool to conserve marine biodiversity and support human livelihoods. This paper reviews the global research landscape, the evolution of concepts and the relevance of valuation techniques related to the assessment of the contribution of MPAs to societal welfare. The review indicates that less than 3% of the wider MPA literature explores the economic valuation of MPA designation. Of those, there is an obvious preference for non-market-based methods …

Sustainable Utilization Challenges And Opportunities Of The Blue Economy Resources In Bangladesh, Tasnia Bahar Chowdhury

Journal of Ocean and Coastal Economics

Bangladesh’s Blue Economy, emphasizing the sustainable use of ocean and coastal resources, presents both immense opportunities and pressing challenges. Following maritime boundary settlements with India and Myanmar, the country gained an exclusive economic zone of 118,813 km² and an extended continental shelf of 37,000 km² in the Bay of Bengal, offering substantial potential in fisheries, aquaculture, renewable energy, tourism, shipping, and marine biotechnology.

The study synthesizes secondary data to examine the sustainable utilization of resources under the blue economy. Marine fisheries contribute 17% of national fish production, supporting millions of livelihoods, while ecosystems such as the Sundarbans and Saint Martin’s …

Mission-Locked Cooperative Governance Architecture For Community-Owned Production Of Life Essentials, Joseph Sack

Defensive Publications Series

Abstract

This disclosure describes a proposed mission-locked cooperative governance architecture for the community-owned production of life essentials, including energy, food, water, housing, and healthcare. The architecture separates technology development from infrastructure ownership. A nonprofit research and development entity independently develops and stewards the underlying technology and releases it under open licensing terms. Production infrastructure is owned by local member cooperatives operating under democratic member voting.

A mission-locked legal framework connects these layers. It combines four protective mechanisms: (1) a mission-protected charter with amendment controls that restrict changes to the stated purpose; (2) dedicated charitable assets with dissolution provisions that direct …

Neighborhood Inference Using Partitioned Lsh And Probabilistic Sketches, Oleg Golubitsky, Dake He, Alexey Strokach, Misha Berlinkov, Kianoosh Abbasi

Defensive Publications Series

Classifying large sets of unlabeled entities in high-dimensional embedding spaces from a small number of labeled examples can present computational challenges, for example, when performing exhaustive nearest neighbor searches. Systems and methods can address these challenges by combining partitioned locality-sensitive hashing (LSH) with probabilistic data structures, such as multiset sketches. High-dimensional entity embeddings can be converted to partitioned LSH hashes, which may be used to build an index where each hash partition maps to a sketch aggregating information about labeled entities. For an unlabeled entity, corresponding sketches can be retrieved and merged. This final sketch can be queried to generate …

Automated Business Verification Using Multimodal Analysis Of Geospatial Imagery, Ashish Kumar, Satyam Kumar, Chandan Shanbhag

Defensive Publications Series

Conventional business verification techniques may be inefficient or limited by automated processes that can result in false negatives. A system and method for automated business verification can use a multi-stage data processing pipeline. For example, a multimodal large language model can perform a blind extraction of textual information from geospatial imagery associated with a business's claimed location. Subsequently, a semantic audit process may compare the extracted visual evidence against claimed business data, which can account for context, abbreviations, and synonyms. This approach can provide a programmatic method for verifying the physical existence of commercial entities and enriching data records with …

Strategic Visual Canvas: Context-Aware Ar Storefront Advertising, Vineet Verma, Harsh Singh

Defensive Publications Series

A spatial computing architecture and method may provide context-aware augmented reality (AR) digital overlays anchored to physical retail storefronts via wearable head-mounted displays (HMDs). The system may transform physical storefront facades into dynamic media spaces by coordinating visual positioning systems, on-device intent profiling across a sliding N-day search window, real-time backend inventory validation, and a gaze-activated progressive disclosure user interface.

A Farewell To Ohms - Reducing The Heat Signature Of Computing By <98%, Peter Stevenson

Defensive Publications Series

This proposal suggests an intermediate Memory segment between ROM and RAM that allows for the tokenization of calculation. The bridge would be rewritable to allow for changes over time but be effectively 'hardwired' as an adjunct of ROM .By totally avoiding duplication, the system runs on a streamlined basis

Technical Specification: Spec-208-Core Title: Solid-State Surface Acoustic Wave (Saw) Waveguides And Matrix Residue Separation Filters, Christopher L. Eckes

Defensive Publications Series

This specification establishes the mathematical and hardware criteria for Solid-State Surface Acoustic Wave (SAW) Waveguides and Matrix Residue Separation Filters. The system is engineered to manipulate localized high-frequency acoustic wave vectors across crystalline lattice matrices, utilizing an integrated multi-probe interdigital transducer (IDT) array to actively suppress boundary layer limit crises and eliminate structural data contamination at room temperature. By deploying an out-of-band matrix residue separation filter, this architecture partitions complex wave propagation paths row-wise across orthogonal virtual nodes to track phase velocities and isolate ancestral human priority art. This framework serves as a public-domain defensive barrier against the proprietary privatization, …

Cicis-X: Waste-Heat Amplified Autonomous Water-Positive Data Center Infrastructure Using Thermoacoustic Heat Elevation, Non-Membrane Water Regeneration, And Integrated Thermal-Hydraulic Monitoring, Michael Victor Caldwell Mr.

Defensive Publications Series

Modern artificial intelligence and hyperscale data centers increasingly face a dual resource limitation:

1. high-density thermal rejection requirements,

2. growing freshwater availability constraints.

Recent academic and industrial deployments demonstrate that waste-heat-driven desalination and wastewater regeneration are technically viable. However, widespread deployment remains limited due to six major barriers:

• insufficient waste-heat temperature,

• compressor energy penalties,

• membrane fouling,

• contaminant accumulation,

• cooling-loop corrosion,

• unfavorable capital economics.

This disclosure presents a unified open-hardware architecture designated CICIS-X.

CICIS-X integrates thermoacoustic thermal amplification, vortex-assisted contaminant destruction, multi-stage vacuum distillation, autonomous remineralization, structural health monitoring, thermal-hydraulic optimization, and closed-loop cooling infrastructure into a single water-positive compute facility.

Unlike conventional …