A System For The Prediction Of Election Results Using Vader And Hybridized Machine Learning Model,
2026
Department of Computer Science, Nigerian Defence Academy, Kaduna State, Nigeria
A System For The Prediction Of Election Results Using Vader And Hybridized Machine Learning Model, Abraham E. Evwiekpaefe, Khadijah Kabir, Georgina N. Obunadike
Tanzania Journal of Science
Integrating different classifiers along with sentiment lexicons like Vader, can enhance the performance of sentiment analysis systems. However, such a hybrid model remains underexplored, particularly in the context of regional elections in developing countries like Nigeria. The aim of this research is to develop a hybrid model that combines three machine learning classifiers and Vader lexicon to possibly achieve a higher accuracy. A case study of the 2023 governorship election in Kogi, Bayelsa and Imo State, Nigeria was examined. Twitter API library was utilized to extracted public and personal tweets using hashtags and keywords related to the target data from …
Ai-Powered File Security System With Facial Biometrics, Qr Code, And Otp Verification,
2026
Multimedia University, Malaysia
Ai-Powered File Security System With Facial Biometrics, Qr Code, And Otp Verification, Xinyi Loi, Ming Man Chan, Yuk Yii Chan
Journal of Informatics and Web Engineering
Commonly used file-sharing systems suffer from impermissible security risks such as impersonation, credential theft, and QR code exploitation, which can expose sensitive information. This paper describes and evaluates an AI-driven secure file-sharing system designed to alleviate these risks through a multi-layered authentication strategy. The complete system involves AES-128 encryption, unique QR code referencing, live facial recognition, and One-Time Password (OTP) confirmation. In our proposed workflow, the sender encrypts a reference to the file and links it to facial data of the recipient, if applicable, and embeds this information into a QR code, that is dynamically generated during the send operation. …
An Integrated Yolov8 Based System For Real Time Vehicle Tracking And Identification Using Cctv Footage,
2026
Amity University Uttar Pradesh, India
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,
2026
Christ University, India
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 …
Enhanced Trust-Based Security Mechanism To Prevent Temporal Dos Vulnerabilities In Ipv6 Link-Local Networks,
2026
Universiti Sains Malaysia, Malaysia
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 …
Analysis And Predictive Modelling Of Ev Charging Patterns And User Behaviour,
2026
Multimedia University, Malaysia
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-Based Prediction Of House Sale Prices In Hulu Langat,
2026
Multimedia University, Malaysia
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 …
Ai-Powered Threat Hunting For Email Phishing Attack Detection Using Natural Language Processing (Nlp),
2026
Multimedia University, Malaysia
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,
2026
Multimedia University, Malaysia
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,
2026
Universiti Teknologi MARA Cawangan Terengganu, Malaysia
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 …
Ai-Driven Intrusion Detection System For Network Security,
2026
Multimedia University, Malaysia
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,
2026
SRM Valliammai Engineering College, India
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,
2026
Multimedia University, Malaysia
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,
2026
Tripoli College for Sciences and Technology, Libya
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,
2026
HELP University, Malaysia
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 …
Ai-Powered Continuous Health Monitoring For Chronic Disease Management In Malaysia: Real-Time Risk Prediction Using Wearable Sensors And Digital Biomarkers,
2026
HELP University, Malaysia
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,
2026
University of Transport and Communications, Vietnam
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 …
Synthetic Data Generation For Healthcare Machine Learning: A Case Study In Vital Signs And Diagnostic Predictions,
2026
HELP University, Malaysia
Synthetic Data Generation For Healthcare Machine Learning: A Case Study In Vital Signs And Diagnostic Predictions, Sellappan Palaniappan, Rajasvaran Logeswaran, Kasthuri Subaramaniam, Oras Baker, Bui Ngoc Dung
Journal of Informatics and Web Engineering
Healthcare Machine Learning (ML) applications face significant challenges in accessing high-quality training data due to stringent privacy regulations, institutional data silos, and concerns over patient confidentiality. This paper explores synthetic data generation as a viable and privacy-preserving alternative to real patient data for developing ML models in healthcare settings. We present techniques for generating realistic vital signs data including body temperature, blood pressure, heart rate, respiratory rate, and oxygen saturation according to appropriate statistical distributions. In addition, we demonstrate how synthetic datasets generated can be used to train diagnostic prediction models. The generated datasets were applied to multiple diagnostic prediction …
Design Of A Cnn–Nlp Based Visual Chatbot For E-Commerce Systems,
2026
Multimedia University, Malaysia
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 …
Synthetic Data Generation For Healthcare And Wellness: Methods, Applications, And Future Directions,
2026
HELP University, Malaysia
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 …
