Digital Commons Network^™

An Integrated Yolov8 Based System For Real Time Vehicle Tracking And Identification Using Cctv Footage, Riya Jain, Piyush Kumar, Ashwani Kumar Dubey, Angela Amphawan, Neo Tse Kian

Journal of Informatics and Web Engineering

Vehicle identification and tracking play a critical role in security and surveillance, supporting the efforts of law enforcing authorities to prevent crime, monitor traffic, and enforce the vehicle rules and regulations. This research presents a proof-of-concept system that employs YOLOv8 for real time vehicle detection and car type classification using live or recorded CCTV footage. YOLOv8 was chosen after rigorous comparison, it was chosen for its ease of use and efficient image training capabilities, making it well suited for rapid prototyping that the concept needed. The system is designed to extract key vehicle features, including colour, model of the vehicle, …

Enhancing Air Traffic Management Using Spatio-Temporal Deep Learning Predictions, Madan Singh, Azween Abdullah

Journal of Informatics and Web Engineering

Modern air traffic management faces growing challenges, particularly at busy airports that must handle high flight volumes in limited airspace corridors. The study is especially pertinent for such airports, where daily operations must navigate intricate air traffic networks. To overcome these challenges, innovative approaches are needed to predict and manage congestion effectively which remains a major challenge. The paper presents a Spatio-temporal analytical framework for air traffic flow forecasting, utilizing a dual deep-learning architecture that combines network topology evaluation with time-series analysis. The model contains two major components. The first component employs Graph Neural Networks (GNNs) to model spatial relationships …

Analysis And Predictive Modelling Of Ev Charging Patterns And User Behaviour, Yi Xuan Law, Kah Ong Michael Goh, Check Yee Law, Connie Tee, Yong Wee Sek, S M Hasan Mahmud

Journal of Informatics and Web Engineering

As more Electric Vehicles (EVs) are released, the ability to predict energy consumption and charging duration becomes crucial in building optimal infrastructure and a proper system of managing energy. This paper proposes a machine learning model that predicts these two important parameters using a real-world dataset. The dataset consists of 1320 EV charging sessions made between January and February 2024 on Kaggle. The data set includes vehicle specifications, time stamps of sessions, environmental conditions, and user behaviour. Feature engineering tasks followed a thorough preprocessing procedure where missing values were imputed, outliers were removed, and the type of data was converted, …

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 …

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 …

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 …

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 …

Enhanced Trust-Based Security Mechanism To Prevent Temporal Dos Vulnerabilities In Ipv6 Link-Local Networks, Iznan Husainy Hasbullah, Lokman Mohd Fadzil, Selvakumar Manickam, Supriyanto Praptodiyono, Mohamad Khairi Ishak

Journal of Informatics and Web Engineering

Many computer networks in operation today currently use both IPv4 and IPv6 stacks. On the other hand, there is a transition towards IPv6-only networks as a result of the limited availability of IPv4 addresses. The primary protocol for link-local IPv6 communication is the Neighbor Discovery Protocol (NDP). Regrettably, its insecure design and basic scope-based security mechanisms make the local network susceptible to insider threats. The Internet Engineering Task Force’s recommended security mechanism for NDP, which is Secure Neighbor Discovery (SEND), is well documented but complex and unsuitable for resource-constrained devices and networks. Trust-ND was positioned as an alternative to SEND …

Machine Learning-Based Prediction Of House Sale Prices In Hulu Langat, Kath Moon Yap Choo, Sew Lai Ng, Da An

Journal of Informatics and Web Engineering

House price prediction remains a complex task due to the interplay of structural, locational, and economic factors. This study proposes a machine learning-based predictive framework tailored to the residential real estate market in Hulu Langat, Malaysia, a district undergoing rapid urbanization. Using open-source datasets, three structured variations are developed: one containing only housing and locational attributes, another incorporating regional-level macroeconomic data, and a third combining both regional and national macroeconomic indicators. The proposed model (Ensemble Weighted Average) is trained and evaluated using evaluation metrics, then compared with models such as Random Forest, XGBoost, LightGBM, and Ensemble Stacking. The proposed model …

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 …

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. …

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 …

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 …

Users’ Searching Preferences In Accessing The E-Library Resources Of The Federal University Of Lafia, Nigeria, Alice Olije Odu, Samuel Otsonu Aboh, Olumide Johnson Akanmidu, Grace Sewuese Ape

Library Philosophy and Practice (e-journal)

Online information activities in the academic environment keeps getting better and easier thanks to the internet that forms part of regular source of connection between users and other information resources. Access to e- library resources are normally via computers and mobile technologies using local network or the internet connection. This makes searching from e-library resource more appealing to academic library users. However, libraries still experiencing low users’ patronage with the e-resource collection, hence this study is undertaken to ascertain the searching preference by the Students of the Federal University of Lafia to help devise better ways of meeting their information …

Why Student Research Matters: A Graduate Student’S Perspective, Jessica L. Hensley

The Journal of Integrative Physiology

Research fundamentally changes how students engage with knowledge. Rather than memorizing established facts, students learn to question, analyze, and contribute to the body of knowledge within their field (1). This shift supports deeper understanding and promotes critical thinking skills by encouraging active construction of knowledge (1). It also exposes students to the process of engaging with scientific literature and integrating information within the context of their research (2).

In my experience, research requires individuals to confront uncertainty. Experiments do not always produce exact results, and methods can require modification. Through this process, students develop adaptability, persistence, and the ability to …

A Universe Gone Quickly, Willie Carver

Critical Humanities

A Universe Gone Quickly

Submitted: May 25, 2026

Accepted: June 1, 2026

Idiom, Marianne Worthington

Critical Humanities

Idiom

Submitted: May 26, 2026

Accepted: June 1, 2026

Navigating Prose, Poetry And Criticism: An Interview With Nasir Abbas Nayyar On His Contribution To Urdu Literature And Literary Criticism, Nasir Abbas Nayyar, Inayat Ullah

Critical Humanities

Navigating Prose, Poetry and Criticism: An Interview with Nasir Abbas Nayyar on His Contribution to Urdu Literature and Literary Criticism 

Submitted: May 24, 2026

Accepted: June 1, 2026

Implementing 21st Century Skills In Quranic Education: A Case Study Of Sub-Saharan African Students Within Quranic Schools In The Souss Region Of Morocco, Ahmed Abaragh, Lahoussine Hamdoune

Critical Humanities

To help Sub-Saharan African students meet their needs in a globalized era, this article seeks to shed light on the essence of integrating or implementing 21st century skills into Quranic education within the Quranic schools in the Souss region of Morocco. The choice of southern Moroccan Quranic Schools, as the research area is due to its historically vibrant culture and the high-quality training provided by well-versed teachers and jurists. Based on the social ecology of human development theory with the use of a qualitative approach, the analysis highlights the prominent role of implementing critical thinking and problem-solving, communication and collaboration, …

The Possibility Of Post Binary Reflexivity: A Genealogy Of Binary Logic In Western Philosophy, Vaughan A. Bidois

Critical Humanities

This article traces a genealogy of binary logic within Western philosophy, from its origins in Platonic metaphysics through to its deconstruction in poststructuralist theory. Drawing on the genealogical methods of Friedrich Nietzsche and Michel Foucault, it critiques how oppositional structures, such as mind/body, reason/emotion, and truth/error, have persisted through shifting intellectual paradigms while shaping knowledge, power, and subjectivity. Structuralist and linguistic theories are examined for their role in reinforcing binary meaning-making, while poststructural critiques (notably Derrida and Foucault) are explored for their attempts to subvert such hierarchies. However, the article argues that these critiques remain paradoxically ensnared within the same …

Human Wisdom And Artificial Intelligence: Role Of Liberal Arts Education In A Neoliberal World, Purna Chandra Bhusal

Critical Humanities

Neoliberalism is an economic and political ideology that originated in the 1930s and 40s, gained momentum in the late 70s and 80s, and peaked in the 90s and 2000s. Since its inception, neoliberalism has promoted a free-market economy with minimal state intervention, granting individuals the freedom to compete and succeed. Since the last quarter of the twentieth century, the neoliberal system has invested in, incorporated, and promoted Artificial Intelligence (AI), valuing an AI-enhanced corporate ethos. As a result, the success of higher education is generally measured by the quantity of technologically sound, market-ready, AI-trained human capital it produces for industry. …

Why Literature Matters: Perpetrator Portrayal, Cultural Imaginaire And Poetry Of The Taliban, Inayat Ullah

Critical Humanities

The question of how perpetrators of violence are identified, represented and understood has become a central concern of Perpetrator Studies, an interdisciplinary field that emerged in the early twenty-first century. This article analyzes The Poetry of the Taliban in a bid to examine how the selected poems construct and negotiate multiple figurations of the perpetrator rather than presenting it as a singular or fixed category. While the poems frequently identify perpetrator through recognizable political, military and ideological structures, they also reveal the complexities involved in reducing the perpetrator to a clearly identifiable form or kind. In exploring these tensions, the …