Digital Commons Network^™

Machine Learning For Employee Attrition Prediction: A Comparative Study, Kai Jia Low, Sook Ling Lew, Sri Winarno

Journal of Informatics and Web Engineering

Employee turnover remains a long-standing issue that can lead to significant economic losses and a decline in workplace productivity. Traditional methods for predicting natural attrition, such as surveys and rumour assessment, usually fail to capture the complexity and multi-factorial nature of natural attrition. To address this limitation, this paper proposes to develop an advanced prediction model using Machine Learning (ML) algorithms to enhance the accuracy and reliability of export prediction. These methods are applicable to a full set of data covering parameters such as demographics, workplace performance and survey scores. The objective of this model is to provide a data-driven …

Design Of A Cnn–Nlp Based Visual Chatbot For E-Commerce Systems, Sidra Kubra, Salma Jahan Nisha, Nbhan D. Salih, Amalina Ibrahim, Wan Noorshahida Mohd Isa

Journal of Informatics and Web Engineering

E-commerce platforms have limitations to assist interactive and highly personalized experiences using conventional text or voice-based chatbots, particularly in context-aware areas where product discovery and visual identification are necessary. Hence, we propose to integrate an image as a visual-based chatbot. We present a conceptual framework that combines Convolutional Neural Networks (CNN) for image recognition with Natural Language Processing (NLP) for dynamic, context-aware search in a chatbot. The CNN model enables the recognition of products from user-uploaded images, while the NLP component processes and generates appropriate responses to enhance the shopping experience by providing suggestions. This hybrid system will be developed …

Ai-Powered Threat Hunting For Email Phishing Attack Detection Using Natural Language Processing (Nlp), Mohd Azriy Akmalhazim Mohd Nazariee, S Prabha Kumaresan, Alaa Haddad, Mohamed Uvaze Ahamed Ayoobkhan

Journal of Informatics and Web Engineering

Phishing attacks remain as a significant cybersecurity threat, aiming to steal sensitive information by exploiting human vulnerability. Traditional phishing email detection often struggles to keep up with the latest attack strategies developed by the attackers which results in high false positive rates and the limited contextual understanding on the email contents. Therefore, to address these challenges, this research proposes a solution via an AI-powered threat-hunting model integrating Natural Language Processing (NLP) techniques for phishing email detection in English through the development of PhishGuard AI application. The application is developed as a web-based software solution designed to be accessible to both …

Ai-Powered Dynamic Encryption And Decryption Defense Model, Chyanne Wen Qian Lor, Ibrahim Yusof, Anang Hudaya Muhamad Amin

Journal of Informatics and Web Engineering

Static cryptographic systems that rely on fixed algorithms and predetermined keys have proven increasingly ineffective in addressing today’s adaptive and AI-powered cyber threats. This paper proposes an Artificial Intelligence (AI)-enabled dynamic encryption and decryption defence model designed to enhance cybersecurity through real-time threat classification and context-aware cryptographic response. The framework combines the Suricata intrusion detection engine with a Random Forest model developed using the CIC-IDS2017 dataset, allowing it to identify and categorize network anomalies into unified groups, including Denial-of-Service (DoS), Distributed Denial of Service (DDoS), Brute Force, and PortScan. Once threats are identified, the system dynamically selects an appropriate encryption …

Gamification By Design: Methodological Foundations Of The Gamespro Speaking Model For Primary Education, Mohd Zulhairi Che Abd Rahman, Hasiah Mohamed, Siti Zulaiha Ahmad, Aurelio P. Vilbar

Journal of Informatics and Web Engineering

This study presents the development and validation of the GAMESPro model, a structured gamification model designed to enhance English-speaking proficiency among Malaysian primary school students. Despite national reforms and policy initiatives aimed at improving communicative competence, oral English skills remain underdeveloped, particularly in rural and resource-constrained schools. Existing gamification practices in Malaysian classrooms often rely on isolated digital tools and lack pedagogical coherence. Addressing this gap, the study employed the Design and Development Research (DDR) methodology, encompassing three phases: Needs Analysis, Design and Development, and Evaluation. A Systematic Literature Review identified key gamification principles, while validation of ten experts confirmed …

The Development Of Decentralized Ticketing System Using Web3, Yu-Xian Cheng, Kah-Ong Michael Goh, Check-Yee Law, Connie Tee, Yong-Wee Sek, Md Ismail Hossen

Journal of Informatics and Web Engineering

Traditional ticketing systems often suffer from major drawbacks such as ticket fraud, duplication, inflated resale prices, lack of transparency, and centralized control over transactions. These issues result in reduced trust and limited flexibility for both event organizers and ticket buyers, especially in unregulated secondary markets. To address these gaps, this paper presents the design and development of a Decentralized Ticketing System (DTS) using Web3 technologies. The system leverages Ethereum blockchain, smart contracts written in Solidity, and NFT-based ticket issuance to ensure security, transparency, and verifiable ownership. Features include wallet-based login via MetaMask, multi-ticket purchasing, QR-based validation, controlled resale pricing, and …

Ai-Driven Intrusion Detection System For Network Security, Zhi Lin Sarah Teoh, Wee How Khoh, Hui Yen Yap, Pin Shen Teh

Journal of Informatics and Web Engineering

In the evolving landscape of cybersecurity, Intrusion Detection Systems (IDS) play a vital role in safeguarding computer networks against malicious activity. Traditional signature-based IDS approaches are increasingly ineffective in detecting novel, complex, or zero-day attacks due to their reliance on predefined rules. To overcome these limitations, this study proposes an AI-driven IDS that integrates both classical machine learning and modern deep learning techniques. The framework introduces and compares three models such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), and a hybrid CNN-SVM model. The system is designed to analyze network traffic patterns and classify them as either benign …

Deep Learning Model For Burn Injury Assessment With Enhanced Diagnosis Rate, C. Pabitha, K. Revathi, R. Krishna Priya

Journal of Informatics and Web Engineering

The skin is considered as the largest organ of our body specifically satisfies the environment. As a result, the skin is vulnerable to various injuries, especially burns. Burns are linked to high rates of morbidity and mortality and can be fatal. Effective diagnosis supported by precise evaluation of the burn area and the depth of wound is critical for enabling efficacy in clinical settings. The characteristics of a burn wound on the skin include: the wound is abnormal, the skin is infected, there is discomfort, the skin is tight, and there are raised areas of skin. To properly diagnose burn …

Editorial: Artificial Intelligence In Healthcare And Wellness — Emerging Frameworks, Intelligent Monitoring, And Precision Medicine, Sellappan Palaniappan

Journal of Informatics and Web Engineering

AI is changing our understanding of health and health-care systems in ways that go beyond the traditional one-click-fits-all approach to care. It is also making care more personalised than ever before, with delivery occurring on an ongoing basis and at all times. The six papers in this special issue of the Journal of Informatics and Web Engineering will give you a clear view of where we are headed. They examine several different areas, including: monitoring chronic illness through wearable technology; using deep learning techniques for contactless heart monitoring via rPPG; developing new cancer risk models; identifying neuroplasticity therapeutic pathways using …

Synthetic Data Generation For Healthcare And Wellness: Methods, Applications, And Future Directions, Sellappan Palaniappan, Kasthuri Subaramaniam, Oras Baker, Bui Ngoc Dung, Sumit Dhariwal

Journal of Informatics and Web Engineering

Artificial intelligence in healthcare relies heavily upon the availability of high-quality datasets, but very rigid privacy regimes, institutional silos, ethical issues, and heterogeneous data still serve to limit the availability of real-world clinical data. To combat these limitations, you focus on synthetic data generation as a privacy-preserving and scalable alternative to traditional data generation for healthcare and wellness studies. More relevant is a work on a broad and clear framework of synthesis methods based on statistical modelling, rule-based generation and domain-specific clinical logic, in contrast to recent studies that mainly target sophisticated methods for the deep learning architectures. The paper …

Ai-Powered Continuous Health Monitoring For Chronic Disease Management In Malaysia: Real-Time Risk Prediction Using Wearable Sensors And Digital Biomarkers, Umi Najiah Ahmad Razimi, Yong Yoke Leng, Nor Hapiza Mohd Ariffin, Mohammed Hazim Alkawaz

Journal of Informatics and Web Engineering

Chronic Non-Communicable Diseases (NCDs), such as diabetes mellitus, hypertension, and cardiovascular disease, make up a significant portion of the mortality cases in Malaysia, as well as impose consistent demands for costs related to health care expenditure. According to the cost reports at the national level, major NCDs (cardiovascular disease, diabetes, and cancer) are responsible for RM 9.65 billion annually as direct healthcare cost [3]. However, the current approach to managing chronic diseases focuses heavily on periodic assessments in clinic setting at weekly or monthly intervals. Here, a predictive analysis framework based on AI technology, leveraging continuous monitoring with patient data …

An Ai-Based Framework For Heart–Brain–Body Coherence In Wellness Monitoring: A Longitudinal Simulation Study, Asiah Lokman, Akalpita Tendulkar, Yujiao Zhang, Mantena Sireesha

Journal of Informatics and Web Engineering

Present wellness monitoring systems primarily emphasize single-modality metrics, neglecting the intricate, interdependent interactions among cardiac, neural, and behavioral regulatory systems. This paper introduces a machine learning framework that implements heart–brain–body coherence as a dynamic, longitudinally evaluated wellness metric. In contrast to previous simulation-based studies that utilized independently generated, cross-sectional data categorized by deterministic IF-THEN rules, the current study adopts a longitudinal simulation design featuring 80 virtual subjects monitored across 30 consecutive daily profiles (N=2,400 records). This design incorporates physiologically realistic inter-variable correlations and probabilistic coherence labelling derived from a continuous risk-scoring function. The subject-level train/test split (56 training subjects, 1,680 …

Intelligent Telemedicine Systems For Contactless Heart Rate Estimation Using Deep Learning-Based Rppg, Do Duc Thinh, Nguyen Duc Manh, Nguyen Thi Bich Ngoc, Vu Van Huan

Journal of Informatics and Web Engineering

Over the last ten years, telemedicine has undergone significant developments, from simple communication-based healthcare to smart and data-driven solutions. The solutions are now powered by Artificial Intelligence (AI), Machine Learning (ML), and Internet of Medical Things (IoMT) technologies. Physiological monitoring is traditionally done with contact-based sensors. The sensors include Electrocardiogram (ECG) and pulse oximeters. The sensors have various disadvantages, including hardware costs, difficulty in continuous usage, and discomfort for patients. Photoplethysmography (rPPG), a remote method of physiological monitoring, is a breakthrough technology. The method is used to estimate physiological signals, including Heart Rate (HR), from video streams captured by standard …

Chondrites From Northwestern Nevada: The Jungo Dense Collection Area, Nico Morrelli

University Honors Theses

Dense collection areas are regions where meteorites are more frequently found, and can help researchers study the meteorite flux to Earth. The Jungo dry lake bed is a dense collection area (DCA) in Humboldt County, Nevada and is currently the recovery location of eleven officially classified meteorites. Five of these samples were classified as part of this work. All five are ordinary chondrites, two of which are likely paired with previously classified Jungo samples. Jungo 021 is likely paired with Jungo 005, and Jungo 017 is likely part of the previously established pairing group of Jungo 002, 003, and 004. …

Digital Bodily Autonomy: Consent Issues, Labor Displacement, And Legal Understandings Of Ai Generated Deepfake Pornography, Mariah Barrett

Undergraduate Theses, Capstones, and Recitals

In the United States, nonconsensual pornographic deepfakes are becoming an increasingly prevalent problem as AI deepfake creation software improves and becomes widely available. Despite this, patchwork legislation across the country is inconsistent and conflicting regarding this issue. In this paper, I explore the background of pornography and obscenity laws and demonstrate how these frameworks are not properly constructed to apply to the digital sphere. Then, I address major themes within deepfake literature such as consent issues, bodily autonomy, labor displacement, and verifiable identity as a commodity through the case study of OnlyFans. I explore current and proposed legislation within the …

Phase-Resolved Measurement And Arbitrary Mode Conversion Of Structured Light, Somayeh Ghazanfarpour

Electronic Theses and Dissertations

This dissertation presents a single-shot phase and amplitude retrieval technique based on spatial light modulator (SLM)-based digital holography. The proposed method enables accurate reconstruction of complex optical fields from a single interferogram, reducing sensitivity to environmental fluctuations and improving experimental stability compared to conventional multi-shot approaches. In addition, a programmable arbitrary mode converter is developed using SLM-based wavefront shaping. By introducing controlled astigmatism and engineering the differential Gouy phase between orthogonal components, the system enables flexible transformation between structured light modes, including Hermite–Gaussian and Laguerre–Gaussian beams. Numerical results demonstrate reliable phase recovery and efficient mode conversion, validating the performance of …

Data Governance Maturity, Ai Integration, And Equity In Colorado K-12 Public Schools, John R. Curtin

Electronic Theses and Dissertations

Colorado's 179 K-12 public school districts operate as autonomous governance units, each responsible for securing and managing student data assets that span health, financial, residential, and academic records. The accelerating integration of artificial intelligence (AI) and machine learning (ML) tools into administrative workflows, productivity software, and instructional platforms has fundamentally altered the risk landscape for student data, yet governance frameworks at the state, district, and school levels have not kept pace. This dissertation investigates whether Colorado's decentralized educational governance structure is institutionally capable of producing equitable, secure, and sustainable data governance outcomes in the AI era.

Drawing on Institutional Theory …

Ka Here Tāngata:1 Mātauranga And Te Ao Māori In Biocultural Kākāpō (Strigops Habroptilus) Conservation Approaches, Sophie Perfetto

Electronic Theses and Dissertations

This master's thesis examines conservation as a form of living cultural heritage practice through the co-management of kākāpō (Strigops habroptilus), a critically endangered parrot and taonga species in Aotearoa New Zealand. Situated within ongoing histories of colonial dispossession and Western frameworks of land and species management, contemporary kākāpō conservation is shaped through the partnership between the Department of Conservation’s Kākāpō Recovery Programme and Ngāi Tahu. Drawing on semi-structured interviews collected during fieldwork from July to August 2025, this study examines how institutional partnerships structure decision-making, authority, and responsibility in contemporary conservation governance. Findings demonstrate that Māori knowledge systems …

Re: Comment Letter For The Revised Draft Final Butte Priority Soils Operable Unit (Bpsou) Butte Treatment Lagoon (Btl) System Annual Report – 2025 (Dated May 11, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Desert Dichotomies: Climatic Imaginaries Of The Arid+[Ant]Arctic, Amanda Aman

Mavs Open Press Open Educational Resources

Desert Dichotomies is a research-based architecture design studio that examines the impacts of climate change across arid and polar desert environments, revealing unexpected parallels between landscapes often understood as environmental opposites. Focusing on Antarctica, Arctic Alaska and the Sonoran Desert, the project investigates how climate change, resource extraction and human systems of control are transforming ecological processes, water systems and cultural landscapes. While arid deserts face increasing drought and water scarcity, polar deserts are experiencing accelerated warming, ice loss, permafrost thaw and flooding. Despite these contrasting conditions, both regions exhibit similar consequences, including declining water quality, biodiversity loss, infrastructural vulnerability …

Proposed Hay-Grading Standards Based On Laboratory Analyses For Evaluation Quality, D A. Rohweder, N Jorgensen, R F. Barnes

IGC Proceedings (1977-2023)

-The progress report of the Forage Analysis Subcommittee of the Hay Marketing Task Force organized by the American Forage and Grassland Council (Rohweder et al. 1978) proposed new hay standards that would express feed value more ade­quately and communicate this value more fully. The subcommittee studied the potential for expanding the standards to include legume-grass mixtures; however, individual legume and grass equations gave more consistent predictions of quality. Digesti­bility and dry-matter intake by cattle and sheep fed the same forages were compared. Digestibility was nearly identical in the two livestock species; however, cattle were somewhat more efficient than sheep in …

Employing Ac And Dc Electrolysis To Modulate Electroenzymatic Pathways For Efficient And Stereoselective H-D Exchange, Wassim El Housseini, Rokas Gerulskis, Nibedita Behera, Huaijun Guan, Rohit G. Jadhav, Zachary A. Nguyen, Egor Baiarashov, Michael A. Pence, Vamshi Krishna Kamaja, Trevor Larkin, Long Luo, Shelley D. Minteer

Chemistry Faculty Research & Creative Works

Stereoselective hydrogen isotope exchange (HIE) at chiral centers is an increasingly important strategy for preparing labeled molecules, yet its practical implementation depends on reliable control of nicotinamide cofactor regeneration. Here we introduce a redox-programmable electroenzymatic platform for stereoselective HIE based on electrode-controlled manipulation of the nicotinamide cofactor state. A wired ferredoxin–NADP⁺ reductase (FNR) electrode enables reversible electrochemical interconversion of NADP⁺ and its deuterated reduced form (NADPD) directly from D2O. Coupling this cofactor cycling with enantioselective alcohol dehydrogenases (ADHs) establishes a reversible alcohol–ketone redox manifold that drives efficient and stereoselective H-D exchange at chiral alcohols. Stereochemical outcomes are programmed …

Measurement Discipline For Sustainable Industrial Transition: Frontier Productivity Evidence From Shandong And Jiangsu Manufacturing, 2013-2023, Shaopu Wu, Jianguang Hou, Danlin Yu

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Sustainable industrial transition requires productivity evidence that separates real efficiency improvement from scale expansion, capital deepening, and reporting change. This study develops a reproducible frontier-productivity diagnostic for provincial leading industry policy, using official 2013-2023 sector panels for 23 two-digit manufacturing sectors in Shandong Province and a matched 2019-2023 benchmark against Jiangsu. The framework combines input-oriented Banker-Charnes-Cooper (BCC) data envelopment analysis (DEA), Simar-Wilson bootstrap bias correction, Malmquist total factor productivity change (TFPCH) decomposition, producer price index (PPI) deflation diagnostics, scale-productivity classification, and targeted sensitivity tests. Bootstrap correction lowers mean BCC efficiency from 0.77 to 0.69 in Shandong and from 0.79 to …

Ai In Education And Information: Tool, Threat, Or Teammate? How Academia Is Shaping The Future Of Intelligent Work, Essraa Nawar

Library Presentations, Posters, and Audiovisual Materials

Artificial intelligence is rapidly changing the future of intelligent work across education, healthcare, leadership, communication, workplace culture, and health information management. Yet while AI adoption continues accelerating, institutions and professionals are still trying to understand what this transformation actually means for people, learning, careers, ethics, trust, governance, and human judgment. This interactive and forward-thinking panel brings together voices from higher education and healthcare information management to explore how AI is reshaping classrooms, workplaces, healthcare systems, professional identity, and future workforce expectations across generations.

Rather than focusing only on technology itself, the conversation will examine the broader cultural and organizational shift …

Agrivoltaic Shading Reduces Soil Greenhouse Gas Fluxes In A Semi-Arid Vineyard, Marcelo Gomes Da Silva, Rafael M. Almeida, Carine Cocco, Engil Pereira

School of Earth, Environmental, & Marine Sciences Faculty Publications

Agrivoltaic systems are expanding in semi-arid regions, raising questions about how panel driven shading alters soil greenhouse gas exchange. We used automated chambers to measure high frequency soil CO2, N2O, and CH4 fluxes in a newly established vineyard agrivoltaic system in Texas across three shading intensities (0%, 35%, 88%) that represent the light environment beneath panels as shaped by panel tilt and solar trajectory. Shading primarily cooled the soil, reducing mean soil temperature from 35.9 °C (0%) to 32.5 °C (35%) and 30.5 °C (88%), while mean soil water content shifted modestly (0.125, 0.106, and 0.130 m3 m−3, respectively). Over …

Enhanced Uav Surveillance With Rf-Based Drone Identification Using Transfer Learning, Prajoy Podder, Maciej Jan Zawodniok, Sanjay Madria

Electrical and Computer Engineering Faculty Research & Creative Works

With the development of technology and the decrease in costs, drones are now becoming easily accessible to the public. As the accessibility of this technology continues to grow, the concerns of security and surveillance increase, and to ensure a sense of security, the need to have reliable drone detection and identification systems is more urgent than ever. Besides, many civilian applications have been found for drones, which play a huge role in modern security and warfare. Unauthorized drones can be very dangerous regarding security issues, as they can be used for spying, smuggling, or even attacks against critical infrastructure. We …

Data Science In The Utah Draft K-12 Standards Of Mathematics, Kate Webster Green

Theses and Dissertations

In today's data-driven world, the field of data science is becoming increasingly important. Research has shown that more job listings in Utah require applicants to have data science skills than ever before. Due to this growing demand, there is a push to begin data science instruction in grades K-12; however, what data science instruction is in grades K-12 is not yet clearly defined. Previous research has identified the skills and processes that make up data science in grades K-12, and several states and research groups have begun developing and implementing data science standards and learning progressions. This study analyzes the …

How Quickly Do Eastern Floodplains Store Carbon? Quantifying Reference Carbon Flux On The Dead Diamond River, Jacob Schnell

Earth Sciences Undergraduate Senior Theses

Terrestrial Earth systems sequester an estimated 30% of annual anthropogenic CO2 emissions. The largest share is stored in global soils which collectively store more than 3 times the amount of carbon as the atmosphere. Floodplains and river systems, while covering only 5–8% of land area, store 20–30% of the global soil organic carbon (SOC), yet, the rate at which floodplains sequester carbon remains highly uncertain. Here I constrain floodplain carbon sequestration rates for a model northeastern river that has not been impacted by development or significant human activity. I calculate carbon stock using total organic carbon and derive carbon flux …

Tracing The Seam: Machine Learning Models Of The Open–Closed Boundary From Upstream Solar Wind Drivers, Arnav Singh

Physics and Astronomy Undergraduate Senior Theses

The open–closed magnetic field line boundary (OCB) demarcates closed terrestrial field lines from those threaded into the interplanetary medium, and its latitude encodes the instantaneous balance between dayside and nightside reconnection that governs much of the high-latitude space-weather hazard. No single instrument resolves it globally in real time. In this thesis I close that gap empirically. Pairing nearly three decades (1983–2012) of Defense Meteorological Satellite Program particle-precipitation boundaries with the 5-minute OMNI solar-wind and geomagnetic-index record yields ∼9.1×10⁵ causally matched crossings, partitioned into six hemispheric/MLT sectors after a |MLat| ≥ 40° physics cut.

On this corpus I train three …

Predicting Cybermindfulness With The Cyber-Health Belief Model, James Robinson, Yan Tian, Thomas Skill

Journal of Cybersecurity Education, Research and Practice

This study describes the development of a Cyber-Health Belief Model (CHBM). The health belief model (HBM) is a message strategy that is widely and successfully used in public health research [1] and has been extended into phish training. Most phish training programs assume  end users are victimized because they have insufficient information to defend themselves. While near-term training effectiveness has shown to be effective, evidence for sustained behavioral change is thin [4]-[7].  This problem indicates that the traditional approaches need to be reconsidered and that new models are needed.  Recent research suggests attentional deficits, cyber-fatigue and fatalism and a sense …