Digital Commons Network^™

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 …

Performance Analysis Of Faster R-Cnn And Yolov8 Model For Mango Fruits Detection, Mohd Haris Lye, Marawan Ashraf Eldeib

Journal of Informatics and Web Engineering

Mango cultivation is a vital agricultural sector in many regions, providing significant economic benefits and contributing to environmental sustainability. Traditional mango detection and harvesting methods are prone to error and are labour-intensive. Recent advances in aerial imagery and Artificial Intelligence (AI) offer innovative solutions to these challenges. An automated yield estimation and fruit harvesting system will require an accurate fruit object detection model. This study explores the application of deep learning models for mango detection on two diverse datasets. This research focuses on evaluating two primary deep learning models: Faster Region-based Convolutional Neural Network (Faster R-CNN) and You Only Look …

A Review Of Cyber-Attacks On Web Applications And Their Countermeasures, Salaheddin Beskri, Kok Why Ng

Journal of Informatics and Web Engineering

Leaders have always wondered where and how precisely Information Technology (IT) can be of value in their organizations. However, the pacing rate of technology development and cutting-edge features that technologies can provide can cause the decision-maker of an organization to rush to invest in technology upgrades without taking risks into account. The risks that may cost the existence of organizations can be caused by an adversary who commits a cyberattack against the IT assets of an organization, seizing leaders' underestimations of the risks of IT upgrades. Inevitably, there will always be new vulnerabilities in IT assets, especially in information systems. …

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 …

Multi-Agent Ai Systems For Detecting Emerging Therapeutic Targets And Intervention Patterns In Neuroplasticity Research, Raza Hasan, Salman Mahmood

Journal of Informatics and Web Engineering

The exponential growth of neuroplasticity research presents profound challenges to the manual synthesis of literature, hindering the identification of emerging therapeutic targets and intervention patterns. While multi-agent Artificial Intelligence (AI) systems have been proposed conceptually to address this, concrete implementations demonstrating end-to-end utility are scarce. This paper details the successful implementation and application of a novel four-agent AI system designed to automate the discovery, extraction, analysis, and validation of patterns in neuroplasticity literature. Our platform consists of four parts: a Literature Discovery Agent (LDA) for corpus gathering, a Concept Extraction Agent (CEA) with a multi-level NLP strategy fallback mechanism for …

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 …

Beyond The Best Prompt: A Coverage View Of Multilingual Reasoning, Harshiv Mistry

University Honors Theses

Multilingual LLMs reason more accurately in English than in other languages, and recent work links part of this gap to reasoning behavior: native-language traces contain fewer cognitive behaviors (verification, backtracking, subgoal setting, backward chaining) that support effective problem solving. We test whether prompting for these behaviors at inference time narrows the gap, across seven conditions varying chain-of-thought, instruction and reasoning language, and cognitive-behavior descriptions, on two models, three languages. We find that English-scaffolded reasoning is the strongest single strategy on both models, closing the Hindi gap on Qwen, though the explicit scaffold's value over plain chain-of-thought is model-dependent. Beyond aggregate …

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 …

A Novel, Embedding-Based Approach To Longitudinal Survey Data Imputation, Julia Rezvani

University Honors Theses

Longitudinal surveys are ubiquitous in the social sciences as a means of tracking changes in behavior and opinions with time and identifying potential causal mechanisms. These surveys are frequently plagued by missing data and semantic drift, both of which limit their effectiveness and scientific utility. Imputation algorithms allow researchers to fill gaps in collected survey datasets, imperfectly reconstructing lost data. Although deep learning algorithms have been used in imputation to great success, approaches which simultaneously leverage the semantic and temporal structure of longitudinal surveys have not yet been developed. We propose a novel imputation architecture which is capable of leveraging …

Geospatial Governance Failures In The Department Of Defense: Contractor Noncompliance, Ai Adoption, And The Parallel Treatment Of Gis And Cybersecurity, Lyndsey Olmstead

Geography and the Environment: Graduate Student Capstones

The Department of Defense's treatment of geographic information systems and cybersecurity as parallel rather than integrated policy domains produces geographically predictable vulnerability patterns across its global military installation footprint. This capstone investigates that conclusion through original spatial analysis, constructing a five-variable composite geospatial vulnerability index across the six U.S. Combatant Command regions using publicly available unclassified data. EUCOM ranked highest overall, driven by GPS/PNT spoofing density, commercial satellite coverage, and cyber incident frequency; CENTCOM ranked second, driven by OSINT exposure incidents and governance risk. The null hypothesis of random geographic distribution is rejected. Findings confirm the structural governance gap documented …

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 …

Evaluation And Distillation Of Source Code Generation Tasks By Large Language Models, Danny Brahman

Electronic Theses and Dissertations

Large Language Models (LLMs) are predominantly assessed based on their common sense reasoning, language comprehension, and logical reasoning abilities. While models trained in specialized domains like mathematics or coding have demonstrated remarkable advancements in logical reasoning, there remains a significant gap in evaluating their code generation capabilities. Existing benchmark datasets fall short in pinpointing specific strengths and weaknesses, impeding targeted enhancements in models’ reasoning abilities to synthesize code.

To bridge this gap, this thesis introduces two novel contributions: CodeEval and CodeQual. CodeEval is an innovative, pedagogical benchmarking method that mirrors the evaluation processes encountered in academic programming courses. It comprises …

Medical Image Integrity Protection Through U-Net Based Roi Segmentation And Hybrid Integer Wavelet–Quadtree Embedding, Muna M. Jawad, Rasha F. Nadhim, Noor A. Yousif, Ashwaq T. Hashim

Journal of Intelligent Informatics, Networking, and Cybersecurity

For decades, secure techniques in medical images have focused on securing the sensitive information through limiting direct access to the images themselves. Nonetheless, these methods tend to cause distortion on the images that can affect the diagnostic value and cause loss of crucial information. There arises the fundamental problem of secure embedding of medical data in such a way that does not affect the diagnostic integrity of the image. To overcome this limitation, a secure yet hidden data-hiding mechanism is introduced that is exploited U-Net-based deep learning for accurate Region of Interest (ROI) segmentation. Therefore U-Net architecture is used for …

Uniform Stability Of Katyusha In Strongly-Convex Settings, Don Li

University Honors Theses

Acceleration of convergence and reduction of variance constitute a trade-off in the design of stochastic optimization machine learning algorithms. Katyusha was introduced to address this trade-off, synthesizing Nesterov Accelerated Gradient (NAG) and Stochastic Variance-Reduced Gradient (SVRG) into a single first-order optimizer with promising empirical performance. However, the generalization properties of Katyusha remain largely unexplored. We conjecture that, in the smooth quadratic regime (i.e., under assumptions of strong convexity and smoothness of the loss function, and boundedness of gradients), Katyusha is uniformly stable in the sense of Bousquet and Elisseeff. Instantiating our framework for NAG, we extend the use of Lyapunov …

Data-Driven Qoe Inference For Htttp Adaptive Streaming, Tisa Selma

Thesis/ Dissertation Defenses

In HTTP adaptive streaming, accurately inferring user Quality of Experience (QoE) remains challenging due to dynamic network conditions, content variations, end-to-end encryption, intrusive advertisement interruptions, and diverse user engagement behaviors. Conventional approaches, such as ITU-T P.1203, fall short in capturing real-time user perception, accounting for ad related disruptions, or adapting to model drift over time. This dissertation therefore aims to develop a multimodal face emotion recognition (FER) based inference model that predicts QoE under encryption and advertisement conditions while being interpretable, robust, resilient to drift, and highly accurate. To achieve these objectives, a unified framework is designed and implemented. The …

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 …

Comparative Analysis Of Leaflet Materials, Stent Materials, And Stent Cell Density For Bileaflet Transcatheter Mitral Valve Design, Joseph Chibuike Nwokeafor, Joshua D. Hofmeister, Breandan B. Yeats, Lakshmi Prasad Dasi, Charanjit S. Rihal, Juan A. Crestanello, Leigh Griffiths, Mohamad A. Alkhouli, Hoda Hatoum

Michigan Tech Publications

Background: The biomechanical performance of bileaflet transcatheter mitral valves (TMVs) depends on complex interactions between leaflet material behavior and stent design. However, the contributions of leaflet materials and constitutive models, stent materials, and stent geometry to valve function and durability remain poorly understood. Methods: A parametric finite element study was conducted using a CAD model of a bileaflet TMV subjected to physiological pressure loading. Five leaflet material models were evaluated: 3 glutaraldehyde-fixed tissues—bovine pericardium (BP; FBP1, FBP2) and porcine pericardium (PP; FPP)—and 2 unfixed tissues—bovine (UBP) and porcine pericardium (UPP). BP was modeled as a linear elastic (FBP1) and Ogden …

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 …

A Study On Culturally Sensitive Ai-Based Conversational Interfaces To Enhance Digital Service Accessibility For Senior Citizens In The Uae, Salma Eisa Alkhyeli

Thesis/ Dissertation Defenses

The UAE is steadfast in its digitalization process due to the Vision 2031, but the aging population encounters several barriers in its efforts to use essential services like subsidized food and fodder markets through an online platform. This thesis developed and validated an AI-based conversational system that focused on helping the elderly group to acquire digital services. The challenges here are associated with lack of knowledge on digital interfaces, language barrier because of dialects and lack of digital literacy in a majority of older adults. The Study addressed these challenges by considering the diverse dialect, cultural and technological needs of …

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 …

The Intersection Between Mindfulness And Cybersecurity: A Tool To Reduce Burnout And Improve Operational Effectiveness, Ivo Ricardo Dias Rosa

Journal of Cybersecurity Education, Research and Practice

Abstract: This paper offers a conceptual discussion of how mindfulness, understood as present moment awareness and deliberate attention regulation, can support cybersecurity professionals. Drawing on a narrative synthesis of workplace mindfulness, burnout, and high pressure decision making literature, we map plausible self regulation mechanisms to typical cyber defense tasks. Rather than presenting new empirical data, we develop an explanatory framework linking attention, reactivity, and recovery to decision quality, team communication, and adherence to incident playbooks. We focus on two connected outcomes: reducing burnout in roles with sustained cognitive and emotional demands, and improving operational effectiveness during critical situations such as …

Cyber-Ready Libraries, Building Digital Fortresses For Tomorrow, Adeyinka B. Tella, Oluchi Precious Ogbonna Dr, Adebola Aderemi Olatoye Mrs

Journal of Cybersecurity Education, Research and Practice

Background and Purpose: Libraries are evolving into highly networked information ecosystems in this age of fast digital transformation, which increases their susceptibility to cybersecurity risks. The study "Cyber-Ready Libraries: Building Digital Fortresses for Tomorrow" looks into how prepared libraries are to face cyber threats and considers methods for creating information systems that are safe, robust, and ready for the future. To safeguard digital assets and user data, the study aims to assess existing cybersecurity practices in library settings and offer a roadmap for combining technological, human, and governance solutions.

Design/Method: The existing literature, case studies, and policy frameworks pertaining …

Parent Cultural Wealth: How Beliefs, Curriculum Familiarity, And Community Awareness Facilitate Home-Based Cs Conversations, Nicol Howard, Leiny Y. Garcia, Jean Ryoo, Julie Flapan, Michelle Choi, Paula Nazario, Wei Wei

Mathematics, Physics, and Computer Science Faculty Articles and Research

Parents are critical influences on children's computer science (CS) pathways, yet families’ involvement remains understudied. Guided by parental involvement and community cultural wealth frameworks, this work-in-progress examined survey data from 53 parents representing families historically underrepresented in computing within California (71% women; 54% Hispanic/Latin/x/e). Findings revealed that CS school curriculum familiarity and awareness of CS community events significantly predicted parent-child CS conversations, while parent CS confidence and attitudes did not, suggesting that curriculum transparency and community connections can promote equitable involvement. Future work will integrate qualitative interviews to examine how families draw upon their cultural wealth.