Journal of Information and Communication Technology
Journal of Information and Communication Technology (JICT) is abstracted and indexed in Emerging Sources Citation Index (ESCI) in November 2017 and Scopus since 2011. It is a double-blind peer reviewed, international academic journal published quarterly (January, April, July and October) by Universiti Utara Malaysia.This journal covers all aspects of information and communication technology, its theories and applications. The aim of this journal is to provide coverage of the most significant research and development in the area of information and communication technology. To be accepted, a paper must be judged to be truly out standing in its field and to be of interest to a wide audience. We are particularly interested in work at the boundaries, both the boundaries of subdisciplines of information and communication technology and the boundaries between information and communication technology and other fields. This is an open access journal. The articles on this site are available in full-text and free of charge to our web visitors.
An Enhanced Iris Recognition using Deep Learning and 2D Chebyshev Wavelet Filter
Iris recognition is widely regarded as a reliable biometric authentication technique; however, its performance is often degraded by poor image quality, noise, eyelid and eyelash occlusion, illumination variation, off-angle acquisition, and partial iris visibility in unconstrained environments. This study proposes an enhanced iris recognition framework that integrates a Two-Dimensional Chebyshev Wavelet Filter (2DCWF) with a Convolutional Neural Network (CNN) to improve feature extraction, classification accuracy, and robustness under challenging imaging conditions. The proposed methodology consists of image acquisition from benchmark iris datasets, preprocessing through greyscale conversion, median filtering, and histogram equalisation, followed by iris segmentation using the Circular Hough Transform and normalisation using the Daugman rubber sheet model. The normalised iris images are then processed using the 2DCWF to extract discriminative multi-resolution spatial-frequency and texture features, which are supplied to a CNN classifier for identity recognition. The framework was evaluated using CASIA-IrisV4, UBIRIS.v2, and MMU datasets representing both controlled and unconstrained acquisition conditions. Experimental results show that the proposed 2DCWF-CNN model outperforms conventional feature extraction methods, including Gabor Wavelet Filter, Local Binary Pattern, and Legendre Wavelet Filter, achieving a highest recognition accuracy of 98.97%, with reduced False Acceptance Rate and False Rejection Rate and improved Genuine Acceptance Rate. These findings demonstrate that combining Chebyshev wavelet-based feature extraction with deep learning provides a robust and effective solution for reliable iris recognition in real-world biometric authentication systems.
Copyright (c) 2026 Journal of Information and Communication Technology https://creativecommons.org/licenses/by/4.0
Perceived Intelligence and Perceived Anthropomorphism as Additional Predictors of ChatGPT Usage Behaviour
The rapid advancement of generative artificial intelligence (AI) tools like ChatGPT is transforming various sectors, including education. Although many are adopting ChatGPT for learning, not all students are embracing it. This is because there are contrasting views regarding its usefulness, ease of use, reliability, and ethical implications. ChatGPT acceptance and usage behaviour have been frequently studied through the Unified Theory of Acceptance and Use of Technology (UTAUT) model. However, it may not completely describe the features of AI. Hence, this paper extends the UTAUT model by incorporating two constructs specifically on AI, namely, Perceived Intelligence and Perceived Anthropomorphism as predictors of the ChatGPT usage behaviour. The study followed a quantitative research design. Purposive sampling was used to collect data from 384 students at a public university in Northern Malaysia. Data was analyzed through Partial Least Squares Structural Equation Modeling. The results showed that all six predictors have significant effects on the Behavioural Intention with 48.3% of variance. Behavioural Intention predicts the actual Use Behaviour with 16.5% of variance. The results highlight the robustness of the extended UTAUT model, where the two constructs become predictors of behavioural intention to use. This indicated students’ dependency on the perceived reasoning of ChatGPT. Based on these predictors, the study has constructed a behavioural model to determine the ChatGPT usage among university students. The findings contribute by demonstrating the validity of the extended UTAUT model. Additionally, practical recommendations are provided to universities, educators, and AI developers for integrating ChatGPT in academic settings.
Designing Tangible User Interfaces for Neurodiversity: A Focus on Dyslexia-Friendly Inclusive Design Features
Tangible User Interfaces (TUIs) provide multisensory interaction and engagement, which are essential for learning for neurodivergent learners, such as children with dyslexia, dyscalculia, and autism spectrum disorder. Given their diverse learning needs, neurodivergent learners require specifically designed tools to support their learning. Existing TUIs provide limited evidence on specific design features, focusing on inclusive reading design for neurodivergent children and on Universal Design principles. Therefore, this paper presents five finalised inclusive design components for TUIs, specifically tailored to address the unique needs of children with reading difficulties. The components were established through thematic analysis of focus group data from five specialised educators using Atlas.ti. The findings revealed five inclusive design components, mapped directly to the five dimensions of interaction design and universal design principles. These components are crucial for triggering multisensory engagement and mitigating text-processing barriers for struggling readers, thereby providing a more inclusive and effective learning session. This effort strives to support UNESCO Sustainable Development Goal 4, which aims to promote inclusive and equitable quality education.
Cybercognition as Emerging Determinant in Protection Motivation Theory: Toward Explaining Secure Intention Behaviour in Online Gaming among Adolescents
The increasing prevalence of online gaming among adolescents has heightened the need to understand the psychological factors that influence secure online behaviour. Protection Motivation Theory (PMT) is limited in its ability to explain technology-related behaviours because it primarily focuses on conscious, rational decision-making driven by threat and coping appraisals. While PMT is widely used to explain individuals’ protective intentions, emerging cognitive constructs such as cybercognition, which refers to the skills, heuristics, and cognitive dispositions that shape online decision-making, remain underexplored within this framework. This study investigates cybercognition as a potential determinant of the secure intention behaviour model, which refers to self-instructions to perform actions to attain secure behaviour in an online gaming environment. Using a simple random survey of adolescent gamers, the research examines how cybercognitive abilities interact with PMT components, including perceived severity, vulnerability, self-efficacy and response efficacy, to predict intentions to engage in secure online practices. Structural equation modelling reveals that cybercognition significantly enhances the explanatory power of PMT by influencing both threat appraisal and coping appraisal pathways. Findings suggest that adolescents with higher cybercognitive awareness are more likely to adopt protective behaviours while gaming. The study contributes to theoretical advancements in PMT by integrating cybercognition and offers practical implications for designing targeted digital safety interventions and educational programs for the youth.
A Unified Machine Learning-Based IDS/IPS Framework with Bio-Inspired Feature Selection for Real-Time Detection of Malware-Laden URLs
Cybersecurity threats have become increasingly sophisticated and dynamic due to the rapid evolution of malicious software and attack techniques. However, the reliance of traditional IDS/IPS implementations on signature-based detection limits their ability to identify novel, rapidly evolving, and adaptive malware, despite their effectiveness against known cyber threats. This paper presents a unified machine-learning framework for defence against malware-laden URLs, which simultaneously targets intrusion detection (accuracy first, out of band) and intrusion prevention (latency first, inline) through module-aware, bio-inspired feature selection. The detection module employs random forest (RF) with the bat algorithm (BA), achieving 99.52% accuracy under stratified fivefold validation. This approach yields thousands of additional correct decisions per million URLs and reduces false negatives and false positives without adverse throughput effects. The prevention module utilizes a decision tree (DT) with the HHO ∩ BA consensus subset, achieving an inference time of 1 ms per URL with competitive accuracy (98.96%), enabling more than 1,000 URLs per second per core and meeting strict inline decision budgets. Standard metrics, such as accuracy, precision, recall, and F1-score, along with timing analyses, confirm that aligning feature selection with module objectives strikes a practical balance between detection quality and latency. The resulting guidance is direct: employ RF with BA for intrusion detection when optimal discriminative performance is essential, and adopt DT with the HHO ∩ BA subset for intrusion prevention when millisecond-scale latency dictates deployment.
An Enhanced Red Deer Algorithm with Adaptive Memory Strategy for the Capacitated Vehicle Routing Problem
The vehicle routing problem (VRP), especially its capacitated (CVRP), has been extensively studied. In the CVRP, each customer must be served exactly once without exceeding the vehicle’s capacity, with the aim of minimising the total distance of all routes. As a nondeterministic polynomial (NP)-hard problem, the CVRP is best solved using metaheuristics. The red deer algorithm (RDA), inspired by red deer mating behaviour, is population-based search metaheuristic with both exploration and exploitation phases. However, it may still get trapped in local optima and lacks a strong intensification mechanism for refining solutions. In RDA, not all solutions are explored, as it imposes no limit on solution reproduction, thereby restricting exploration. Thus, this study proposes an enhanced RDA that incorporates adaptive memory strategies to strengthen exploration and exploitation. The RDA classifies the population into males (i.e., high-quality solutions) and females or hinds (i.e., lower-quality solutions), with males further divided into commanders and stags. Exploitation occurs through roaring, selecting commanders, and fighting, while exploration involves forming harems and mating commanders with hinds both within and outside their harems. In enhanced RDA, previously mated hinds are adaptively excluded to promote mating with new ones, thereby enhancing exploration and solution quality. Enhanced RDA with an adaptive memory strategy (RDAM) was tested on 87 benchmark instances, outperforming a construction algorithm by 96.5% and the original RDA by 87.3%, and showing competitive results with established methods. The adaptive memory strategy in RDA enhanced the total distance compared with the original RDA, while improving its exploration–exploitation in solving the CVRP, offering theoretical and practical benefits for logistics efficiency.
Active Contour Model for Vector-Valued Intensity Inhomogeneity Images Corrupted by a Mixture of Additive Gaussian and Multiplicative Gamma Noise
Most of the image processing research has demonstrated the exceptional suitability of Active Contour Models (ACMs) concerning image segmentation models in image analysis as well as computer vision tasks. However, noise and intensity inhomogeneity in the vector-valued or colour images may compromise image quality and impact the results of segmentation. Most often, images are disrupted by mixed noise. To date, no existing research has addressed image segmentation under a combination of two noise types: Additive Gaussian and Multiplicative Gamma (AGMG). Thus, to fill this gap, this research proposes a variational ACM for segmenting vector-valued intensity inhomogeneity images distracted by mixed AGMG noise. Correspondingly, we developed our model by integrating dual denoising terms to reduce mixed noise and the term of optimized Laplacian of Gaussian (LoG) to recover homogeneous regions. As such, the solution to the suggested model involves minimization techniques, which are the Calculus of Variations as well as the finite difference approach. Concurrently, the models’ performance, including the proposed as well as comparative models, will be assessed under low, moderate, severe, and very severe noise. As a result, the average values of the suggested framework, namely the Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (JSC), as well as accuracy metric, were 3.29%, 1.796%, and 1.374% higher, respectively, than those of the compared models. Overall, this highlights the accuracy of our suggested model in addressing colour intensity inhomogeneity images with mixed AGMG noise for segmentation.
Structural Time Series Modelling of Climate Change Effects on Mortality in a Tropical Developing Country
Heat-related mortality has emerged as a critical public health issue, driven by the accelerating impacts of climate change. Most studies establish a direct causative relationship between high temperatures and mortality; however, there is scarce literature studying the climate change mortality ellipse. This study aims to fill this gap by examining the effects of climate change on mortality in the tropical region with consistently high year-round temperatures, specifically in Malaysia. Structural time-series models were applied to annual mortality data for the period 2005 to 2022, obtained from the Department of Statistics Malaysia. Monthly climate data, including temperature, rainfall amount, number of rainy days, and relative humidity, were sourced from the Malaysian Meteorological Department and subsequently aggregated into annual values to ensure consistency in the analysis. The model uses impulse indicator saturation, which makes it easier to spot structural breaks and extreme values, improving the reliability of the results. The analysis indicates that higher rainfall is strongly associated with increased mortality, reflecting the health risks linked to flooding and waterborne diseases. In contrast, periods of higher relative humidity tend to correspond with lower mortality rates. The fully saturated model identifies two key structural shifts in 2006 and 2018, likely caused by abrupt changes in temperature and rainfall. These findings provide a solid foundation for targeted interventions, such as heat-stress regulations and localised air-quality measures, and offer evidence to guide strategies to reduce climate-related health risks in the country, while also supporting broader public health planning.
Conditional Tabular Generative Adversarial Network-based Synthetic Data Generation for Model Generalisation Improvement
Accessing extensive and varied datasets is essential for developing strong predictive models in data analytics. However, many real-world applications suffer from small and imbalanced datasets, leading to overfitting, poor generalisation, and low model performance. Traditional data augmentation techniques are often unsuitable for tabular data, as they fail to preserve complex feature relationships. To address this challenge, this study adapts the Conditional Tabular Generative Adversarial Network (CTGAN) for synthetic data generation. The proposed approach involves five phases: (1) Data Acquisition, 2) Data Preparation, (3) Model Training, (4) Synthetic Data Generation, and (5) Evaluation. Experimental results on three benchmark datasets show that the proposed work produced data that closely adheres to the statistical distribution of the original dataset, with Wasserstein Distance < 0.05 for numerical features and Jensen-Shannon Divergence < 0.08 for categorical features. Additionally, models trained on datasets including synthetic and real data achieved up to 15% improvement in classification accuracy compared to those trained on real and small datasets alone. Training on a combination of real and synthetic data for the minority class in large datasets significantly improves the F1-score, with gains of approximately 9–10%. This approach also yields a modest increase in overall accuracy (around 1.5%), suggesting enhanced model generalisation. These results indicate that the adapted CTGAN is a viable option for data augmentation, addressing problems with limited and imbalanced data for machine learning data training.
Hybrid Partial Least Squares-Structural Equation Modelling and Multi-Layer Perceptron for Predicting E-Participation Success in E-Government Services: Socio-Cultural Insights and Extended Validation
Although the Delone and McLean IS Success Model (D&M) can explain the phenomenon of e-participation success (EPS), the model was initially created in an e-commerce setting and thus neglects external factors related to e-government services. To address the gap, this study revisits the D&M by extending it with four socio-cultural constructs of Trust (TR), Anonymity (AN), Nationalism (NT), and Culture (CR). Based on 428 survey data from Malaysian citizens, a hybrid methodology was employed, integrating Partial Least Squares-Structural Equation Modelling (PLS-SEM) and Multi-layer Perceptron (MLP) to capture non-linear relationships and enhance predictive accuracy. While hybrid modelling is common, past studies have often applied limited classification metrics, and the infrequent use of comprehensive metrics, such as Area Under the Receiver Operating Characteristic Curve (AUC-ROC), may potentially affect the reliability and generalizability of these models. A comparative R² analysis between the baseline and enhanced models in this study revealed significant improvements, with R² for e-participation intention (EPI) increased from 0.620 to 0.728, EPS from 0.330 to 0.345, while User Satisfaction (US) remained strong at 0.765. The analysis predicts a 94.80% success rate for e-participation, with the MLP model further demonstrating robust classification performance, achieving an accuracy of 90.1%, a precision of 0.909, a recall of 0.948, an F1 score of 0.928, and an AUC-ROC of 0.955, outperforming other benchmark classifiers. This study contributes theoretically by introducing underexplored socio-cultural variables into the D&M while methodologically extending the hybrid PLS-SEM and MLP through a robust model validation using AUC-ROC.
Parental Strategies for Mitigating Online Threats and Enhancing Children’s Cybersecurity Awareness
The widespread adoption of digital technology has also resulted in a concerning trend: the rise in online threats targeted at children. As online threats to children continue to increase, parents need to take proactive steps to protect their children’s safety on the Internet. This research explores how parents respond to online threats affecting their children by examining their cyber-parenting approaches and coping strategies through Protection Motivation Theory (PMT). This research involved semi-structured interviews with nine parents whose children had experienced online threats. The study revealed that authoritative parenting, which balances autonomy with guidance, was the most adopted approach by parents. Conversely, parents with a high level of digital literacy tended towards an authoritarian style, characterised by rigorous monitoring of their adolescents' online activities. Following online threat incidents, parents adopted both problem-focused coping and emotion-focused coping. These responses reflected varying degrees of threat appraisal and coping appraisal, consistent with PMT constructs. By identifying prevalent parenting styles and coping strategies, this research contributes to a deeper understanding of how parental behaviours influence children’s online behaviours and resilience to cyber incidents. The study also found that effective parental mediation not only reduces children’s exposure to harm but also plays a critical role in fostering their cybersecurity awareness, empowering them to recognise risks, apply protective behaviours, and navigate digital environments with greater confidence. Such insights are critical for designing effective interventions and educational initiatives aimed at fostering safer digital practices among families, particularly in raising children’s awareness.
Enhancing the Effectiveness of Machine Learning-Based Phishing Email Detection via an Improved Pre-Processing Technique for Data Security
The growing volume and sophistication of phishing emails have become a significant threat to data security, often serving as the initial vector for data breaches. Most past studies have focused on comparing machine learning models to determine the best-performing algorithm. They often neglect the role of pre-processing, which also contributes to the effectiveness of these models. To address this gap, this study investigates the impact of pre-processing techniques on phishing email detection, aiming to strengthen data protection. Three supervised machine learning algorithms, which are Support Vector Machine (SVM), Random Forest and Decision Tree, were selected to undergo two experimental iterations: one with basic pre-processing and the other with an enhanced pre-processing technique including Synthetic Minority Oversampling Technique (SMOTE), Term Frequency-Inverse Document Frequency (TF-IDF), Singular Value Decomposition (SVD) and cross-validation. Using a dataset comprising 28,747 labelled emails, the models were trained, tested, and evaluated based on accuracy, precision, recall, and F1-score, with further insight gained through confusion matrix analysis. Among the models, Random Forest demonstrated the strongest consistent performance across all metrics, while Decision Tree showed the most notable improvement. Although SVM maintains high recall and precision, it is less responsive to the applied pre-processing techniques. This result demonstrates that pre-processing techniques significantly contribute to the performance of the detection models. Overall, these findings highlight the critical role of pre-processing in enhancing phishing email detection, which contributes to stronger organisational resilience.
Motion-Pattern-Based Multi-Object Tracking for Real-Time Edge Computing Deployment
Real-time object tracking in embedded systems requires algorithms that balance processing performance with energy efficiency for practical deployment. This study evaluates the performance of the TG-Lite algorithm on Jetson Nano embedded hardware for surveillance applications requiring a minimum processing rate of 10 frames per second (FPS). The methodology involves comprehensive performance analysis, measuring frame-processing rates, energy consumption patterns, and system resource utilisation across video-file and live-stream scenarios. TG-Lite achieves 15.51 FPS processing capability with 11.24 watts (W) power consumption, meeting real-time processing requirements for embedded surveillance deployment. The algorithm demonstrates consistent performance across different input conditions, maintaining stable processing rates on both video files (10.86 FPS) and live streams (15.51 FPS). TG-Lite exhibits efficient resource utilisation, with moderate central processing unit (CPU) usage (17.4% average) and effective graphics processing unit (GPU) acceleration. The energy analysis reveals TG-Lite's 1.38 frames-per-watt efficiency, indicating effective allocation of computational resources for real-time processing demands. Performance evaluation shows that TG-Lite maintains stable tracking continuity, with an average track length of 35.8 frames and a consistent confidence distribution pattern. These results demonstrate the suitability of the proposed approach for embedded computer vision applications under the evaluated edge constraints.
Graph-Augmented Transformer Framework for Context-Aware Recommender Systems on Amazon E-Commerce Data
Recommender systems play a pivotal role in enhancing user experience and driving engagement across e-commerce platforms. However, traditional approaches such as collaborative filtering and matrix factorisation often struggle with sparse user-item interactions and fail to capture contextual nuances. To address these limitations, this paper proposes a novel hybrid framework that integrates Graph Neural Networks (GNNs) and Transformer-based architectures for context-aware recommendation. The framework first constructs a dynamic bipartite graph from user interactions and product metadata, enabling the GNN to learn relational embeddings that reflect user preferences and item similarities. These embeddings are then fused with a Transformer module that applies multi-head self-attention to model temporal and semantic patterns within user behaviour sequences. We propose a hybrid Graph-Augmented Transformer framework for context-aware recommendation, evaluated on the Amazon Reviews 2023 dataset (571M reviews, 54.5M users, 48.2M items). The model integrates graph-based relational embeddings with transformer-based sequential attention. Compared to LightGCN, SASRec, and BERT4Rec baselines, our approach achieves significant improvements in NDCG, recall, and robustness under cold-start conditions. The framework is scalable and suitable for real-time deployment, offering a practical blueprint for next-generation recommender systems. By combining graph-based relational learning with attention-driven context modelling, this research contributes a powerful and flexible solution for next-generation recommender systems.
Determinants of Farmers’ Willingness to Pay for IoT Adoption in Rice Farming: Evidence from Malaysia
Rice farming faces persistent challenges that constrain productivity and sustainability, underscoring the urgent need for digital innovations such as the Internet of Things (IoT). While IoT technologies offer the potential for efficiency and resilience, their adoption depends on farmers’ willingness to pay (WTP), which remains poorly understood in resource-constrained contexts. This study aimed to identify the determinants of WTP for IoT adoption by integrating farmer-related, technological, and institutional factors. A structured survey was conducted among 229 rice farmers in the Integrated Agricultural Development Area, Barat Laut Selangor, Malaysia. The findings revealed that most farmers demonstrated moderate IoT awareness, with the awareness index highlighting gaps in technical knowledge and training. The IoT readiness index recorded relatively high scores for shared infrastructure (71.8) and Internet connectivity (71.6) but lower levels for technical support (60.0) and energy supply (54.4), reflecting uneven preparedness. Logistic regression results indicated that higher-income farmers were significantly more likely to invest, with middle- and high-income groups showing 20.13% and 22.45% higher probabilities, respectively. Secure land tenure increased WTP by 3.18%, while each additional year of age reduced WTP by 0.61%. IoT infrastructural readiness raised WTP by 10.53%, and perceived ease of use increased WTP by 8.27%. However, perceived usefulness exhibited a negative marginal effect, suggesting that recognising IoT’s benefits does not necessarily translate into WTP when financial or institutional barriers persist. The findings indicate that financial support, reliable IoT infrastructure, and practical training are crucial to fostering inclusive IoT adoption and enhancing resilience in rice farming.
Evaluating the Effectiveness of Digital Product Advertisement Type using Machine Learning and Shapley Additive Explanations Analysis
Digital advertising continues to grow rapidly, yet advertisers face persistent uncertainty in selecting the most effective ad format for different campaign objectives and audience segments. Prior studies often rely on limited metrics or lack interpretability, making it difficult to explain why certain formats perform better. This study addresses this gap by evaluating ad format effectiveness using explainable machine learning. This study evaluates the effectiveness of image and video advertisements (ads) across five key performance indicators (KPIs): reach, impressions, link clicks, cost per click (CPC), and cost per thousand impressions (CPM). A dataset of 4,526 campaigns from Meta Ads Manager (July 2021–August 2024) was analysed using machine learning models integrated with Shapley Additive Explanations (SHAP). Model performance was assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). The results showed that video ads achieved higher reach and impressions with lower CPM. Meanwhile, image ads delivered lower CPCs and stronger click performance, particularly among users aged 13–17 and 55+. Aside from confirming established format patterns, this study made certain contributions by applying explainable machine learning in a large-scale, non-Western context and by clarifying the mechanisms behind ad performance. The findings offered actionable guidance; for example, video ads were optimal for awareness and visibility objectives. Additionally, image ads were preferable for engagement-driven campaigns.
Wasserstein Generative Adversarial Network with Gradient Penalty and Threshold-Enhanced for Imbalanced Panel Data for Financial Fraud Detection
Financial statement fraud detection is critical to maintaining trust among investors, regulators, and analysts. However, traditional audit procedures often fail to detect anomalies effectively because they occur infrequently but can result in significant economic losses. This study proposes an oversampling approach using a modified threshold in the Wasserstein Generative Adversarial Network with Gradient Penalty (WGANGP) to enhance synthetic data variance in financial fraud detection. The financial data were collected from the financial reports of companies listed on the Indonesia Stock Exchange and were labelled according to the Balanced Scorecard framework into four categories: normal, alarm, risky, and fraud. Given the severe class imbalance, this study introduces a WGANGP model with threshold optimisation in the generator and a gradient penalty to generate high-quality synthetic samples. This study conducted general and per-entity oversampling scenarios and evaluated them using Euclidean distance, Wasserstein distance, and classification metrics. In Scenario 1, the Generative Adversarial Network (GAN) outperformed the Synthetic Minority Oversampling Technique (SMOTE) and vice versa in Scenario 2. However, in financial fraud detection, the WGANGP with enhanced thresholding improved the F1-score by 13% to 17% compared to SMOTE and five GAN-based models across thirteen classification models, including traditional, machine learning, and deep learning models. This finding suggests that optimising the threshold in WGANGP reduces variance and improves model performance. Furthermore, generating synthetic data that is very similar to actual data may not necessarily improve classification; therefore, it is necessary to test how oversampling affects subsequent stages.
Evaluation of a Gamified Self-care Application for University Students using the Wheel of Sukr Framework
Effective self-care practices are important for university students’ well-being. However, many students struggle to sustain these practices due to low motivation with existing digital self-care tools. While gamification shows potential, its effectiveness is often limited by implementations that lack robust theoretical foundations or alignment with psychological and motivational theories. This study aims to utilise gamification to encourage university students to practice self-care. The study evaluates two versions of a self-care application prototype: Version A (without gamification) and Version B (with gamification), utilising the Wheel of Sukr framework. A pilot study was conducted to refine experimental procedures, followed by a main experiment with 20 undergraduate students. Data collection included pre-test forms, the User Engagement Scale-Short Form (UES-SF), the System Usability Scale (SUS), and qualitative feedback. Wilcoxon Signed-Rank tests showed significant differences favouring Version B (Z = -2.354, p = 0.019) for user engagement metrics, including focused attention, aesthetic appeal, and reward factors. Qualitative feedback indicates that students found gamification elements such as points, badges, and weather-based recommendations engaging, though some noted the added complexity as a usability challenge. The average SUS score for Version B was 80.63, indicating high usability. The findings suggest that adapting the Wheel of Sukr framework may provide a structured design approach for integrating gamification into student self-care applications, enhancing engagement while maintaining usability. This approach offers practical implications for the design of future theory-driven digital health interventions.
Interval Type-2 Gaussian Fuzzy Games: Energy Sharing in Distributed Systems with Electric Vehicle Integration under Uncertainty
Fuzzy cooperative games using Type-1 fuzzy numbers are commonly applied to model vagueness and imprecision in coalition values and player allocations. However, such models are less effective in capturing increased imprecision. This paper introduces interval Type-2 Gaussian fuzzy numbers (IT2 GFNs) to better represent increased imprecision in coalition values and player allocations in distributed energy systems (DES) caused by uncoordinated electric vehicle (EV) charging and discharging patterns. An analytical solution is proposed to ensure fair and efficient player allocations in IT2 Gaussian fuzzy cooperative games. The proposed method avoids the computational challenges of IT2 subtractions and the associated Type-1 fuzzy number subtractions, which tend to amplify the footprint of uncertainty (FOU) or restrict applicability. A comparative analysis with the interval-based Shapley value, computed using Moore’s subtraction operator, demonstrates that the proposed method achieves allocation efficiency by fully distributing the grand coalition value among players while overcoming the known limitations of the Hukuhara difference, which is not always well-defined.
Expert Elicitation of Fuzzy Membership Functions of Linguistic Terms: A Fire Evacuation Case Study
Decision-making in complex, uncertain environments, like emergency evacuations, often relies on the use of linguistic terms to express subjective judgments. However, the inherent ambiguity of these linguistic terms, coupled with the variability of expert opinions, poses a significant challenge for accurate decision-making. Membership functions (MFs) are essential tools for quantifying and representing the meaning of these linguistic terms, allowing for the computational processing of subjective judgments. Existing methods for eliciting MFs of these terms struggle to capture the inherent variability and probabilistic nature of expert opinions, hindering the accurate representation of uncertainty. Existing methods for eliciting membership functions (MFs) of these terms struggle to capture the inherent variability and probabilistic nature of expert opinions, hindering accurate representation of uncertainty. This study propose a framework for eliciting MFs of probabilistic linguistic terms using the Interval Estimation (IE) technique. The procedure integrates a graphic survey to construct MFs that fulfils the definition of Triangular Fuzzy Number (TFN). This approach allows experts to directly express their uncertainty ranges, ensuring the resulting MFs reflect both their individual perceptions and the overall probabilistic distribution of opinions. A case study eliciting psychological responses in fire evacuation scenarios is utilized to demonstrate the utility of our proposed framework. The developed MFs were integrated into a Bayesian Network (BN) decision model. The performance analysis indicated by sensitivity values confirms the stability and robustness of the BN model parameters, thereby validating the rationality and meaningfulness of the expert-elicited MFs. The resulting MFs demonstrated a significant improvement in capturing the variability of psychological responses compared to traditional methods. This robust methodology provides a practical tool for developing expert-driven fuzzy linguistic scales tailored to specific domains thus offering practical applications for decision-making in uncertain environments.
Copyright (c) 2025 Journal of Information and Communication Technology https://creativecommons.org/licenses/by/4.0
A Hybrid Machine Learning Model for Streaming Frequency-Based Anomaly Detection in Banking Transactions
The increasing sophistication of fraudulent activities in digital financial systems necessitates real-time anomaly detection models that can adapt to evolving transactional behaviours. While prior research has explored machine learning approaches for fraud detection, most rely on static datasets and overlook temporal dependencies inherent in financial transaction streams. This study addresses this critical gap by proposing a hybrid anomaly detection model that integrates dynamic streaming-frequency analysis via a 7-day sliding window, an unsupervised Long Short-Term Memory (LSTM) -autoencoder for anomaly scoring, and a supervised Artificial Neural Network (ANN) for classification. The sliding window mechanism enables the model to capture short-term temporal fluctuations and behavioural patterns, aligning with the streaming nature of financial data. The LSTM-autoencoder is trained exclusively on normal transaction sequences to learn temporal dependencies and compute reconstruction errors, which serve as deep anomaly features. These features are then fed into the ANN to classify transactions as normal or anomalous. Experimental results on the IBM Anti-Money Laundering (AML) dataset demonstrate the effectiveness of the proposed framework, achieving a classification accuracy of 99.92%, precision of 94.12%, recall of 88.43%, and F1 score of 91.19%. This layered architecture not only enhances early detection of anomalous behaviour but also provides a scalable, adaptive solution for real-time fraud detection in streaming financial environments.
Monitoring Land Surface Temperature on Urban Expansionusing Normalised Difference Vegetation Index and Google Earth Engine
Urban expansion and the corresponding increase in land surface temperature (LST) are significant environmental concerns worldwide. In Malaysia, the urbanisation rate has increased dramatically from 1970 to 2020, according to the Statistics Department. This rapid development, particularly in states such as Selangor, Johor, and Penang, has intensified surface heating and reduced ecological resilience, exacerbating the effects of climate change. Despite growing recognition of these impacts, many existing studies rely on static or predictive models that lack spatial precision and real-time monitoring. Malaysia has recorded an average LST increase since 2000, highlighting the urgent need for systematic, ICT-enabled frameworks to monitor and respond to thermal and environmental changes. This study aims to develop a scalable and replicable framework for monitoring LST changes linked to urbanisation by integrating vegetation-based indices and remote sensing technologies. The framework is demonstrated through a case study of Perak, Malaysia, which is an area experiencing accelerated urban growth and ecological stress. The study was conducted in four structured phases. First, satellite imagery from Landsat 8 and Moderate Resolution Imaging Spectroradiometer (MODIS) was collected using Google Earth Engine (GEE). Second, data preprocessing was performed to derive NDVI, Vegetation Cover Proportion (Pv), and surface Emissivity (Em). Third, spatial and temporal patterns were visualised. Finally, analysis was conducted to examine correlations between LST variation and land cover changes from 2019 to 2023. Findings reveal a consistent rise in LST and a decline in vegetation health in urbanising and deforested zones, confirming a reinforcing feedback loop of surface heat accumulation and ecological degradation. This ICT-driven framework provides a practical model for real-time environmental monitoring, supporting data-informed strategies in urban planning, reforestation, and climate adaptation, particularly in rapidly developing, climate-vulnerable contexts such as Malaysia.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
AI-driven Influencer and Market Analysis: A Social Network Approachto Measure E-Commerce Relationships
Social network analysis is a process of studying social structures and relationships using graph theory and data analysis techniques. It involves mapping and measuring connections and entities in a network. However, on online selling platforms, identifying influential entities such as individuals and high-value products remains a challenge due to the complexity of customer and seller interactions. This study aims to assess seller performance and product lifetime value using AI-driven network analysis involving a measure of centrality. AI-driven network analysis utilises artificial intelligence (AI) to identify influential individuals and predict emerging trends in consumer engagement. It uses weighted degree and betweenness centrality to assess their effectiveness in identifying influential entities, including sellers, products, or organisations in a commercial network. Weighted degree centrality measures the strength and frequency of direct connections, while betweenness centrality identifies entities that act as intermediaries across different network segments. The analysis reveals that weighted degree centrality, with a value of 3190 for annual seller performance, is more closely aligned with actual sales performance and stakeholder assessments, making it a more suitable metric for supporting business decisions in this context. The findings demonstrate that AI-driven analytics enable businesses to consistently identify high-performing sellers and products based on their structural positions within the network. It contributes to the development of more targeted marketing strategies, systematic recognition of top performers, and enhanced customer engagement through data-informed decision-making. Future research may explore the integration of dynamic network modelling with multi-layered e-commerce networks, thereby increasing the depth of analysis across various platforms and industries.
A Convex Variational Interactive Model with Projection Method for Digital Images Partitioning
Digital image partitioning separates the foreground of an image from the background for subsequent analysis. In literature, the variational global model is frequently employed for digital images partitioning; however, it has been shown to underperform when the targeted object is situated near a neighbouring object. To address this issue, researchers recently devised the variational interactive model (VIM). However, because of the formulation’s non-convexity, it is sensitive to the placement of the initial contour and provides inaccurate results if the initial contour is not positioned correctly. Consequently, a new convex formulation of VIM was recently developed based on the chessboard distance function, known as the Selective Segmentation based on Chessboard distance function (SSCD) model. Although this model achieved better accuracy and efficiency in image partitioning and is less sensitive to the initial contour’s location compared to the non-convex VIM, the partitioning process is significantly slower, especially for large-sized images. This stems from the utilisation of a complex penalty term and the approximation of the regularisation term during the energy minimisation phase, which also impacts the accuracy of the partitioning results. This work contributes by proposing a new convex VIM that omits the penalty term and avoids approximating the regularisation term. Moreover, the idea of utilising the projection method is proposed to speed up the partitioning process with improved accuracy. Numerical experiments demonstrated that the proposed model achieved higher accuracy and efficiency compared to existing models. The proposed model has the potential to be formulated into a three-dimensional formulation in the future.
Robust Single Sample Per Person Face Recognition with Probabilistic Illumination Enhancement
The challenges imposed by the Single Sample Per Person face recognition problem become especially critical when face images are captured in uncontrolled environments, which typically involve variations in illumination, facial expression, pose, occlusion, and more. In particular, illumination changes caused by poor lighting conditions can significantly degrade the performance of face recognition systems. In this paper, we investigate the SSPP face recognition problem in the presence of potential illumination variation and propose a two-fold approach. Firstly, we analyse and quantify the illumination variations in face images and then normalise these variations based on a probabilistic image enhancement approach. Subsequently, the feature embedding process was performed using the deep learning-based FaceNet system, and finally, faces were classified using the classical Support Vector Machine. The performance of the proposed approach is demonstrated through comprehensive experiments compared to state-of-the-art techniques. The proposed method achieved significant accuracy improvements, attaining 96.17% and 95.68% for 20 and 30 subjects, respectively, on the Extended Yale-B dataset. The performance of the proposed method is evaluated and exhibits superiority in handling illumination compared to state-of-the-art counterparts.
Evaluation of Garbage Detection Efficacy Utilising Deep Learning
Garbage management has become an urgent global challenge due to the expected 70% increase in volume between 2025 and 2050, driven by rapid urbanisation and population growth. Low public understanding of garbage sorting and source-based disposal is reflected in Indonesia’s inefficient garbage processing. In addition to being ineffective, traditional methods like landfill disposal and incineration present serious environmental hazards. These traditional methods are often ineffective due to resource constraints and time-consuming nature, and cannot be relied upon for extended periods because they damage the environment during the process. To increase the effectiveness and precision of garbage management, this research proposes a garbage detection and classification model using YOLOv11. This research includes data collection and pre-processing, model training, and performance evaluation using metrics such as mean average precision (mAP). This research uses the Trashnet Garbage Classification Dataset, which has 2,524 total images that are divided into six categories. The key technical contributions of this research are to apply additional techniques, such as data augmentation strategies, to the dataset, enabling a comparison between the original and more advanced datasets. The purpose of the data augmentation technique is to improve model generalisation. The results of the evaluation metrics show that the model using the augmented dataset has slightly better performance with an mAP50 value of 97.8% than the model using the original dataset. This model is capable of identifying and classifying accurately all of the categories in the dataset.
Designing a Hybrid Learning Resource Recommender Model using Learning Object Rating Algorithm for Adaptive Learning
E-learning has become a key component of modern education that provides access to digital learning resources. However, the overwhelming volume of content can make it difficult for learners to find materials suited to their needs. This has led to a growing demand for adaptive learning, which personalises content based on learner characteristics. To support this, e-learning platforms adopt recommender systems through machine learning techniques. While effective, these systems often depend heavily on historical data, such as user ratings and interactions, to generate meaningful recommendations. This dependency introduces a significant challenge known as data sparsity, where insufficient interaction data limits the model’s ability to provide accurate recommendations. This study addresses this challenge by proposing a hybrid learning resource recommender model that combines collaborative and content-based filtering and introduces the Learning Object Rating Algorithm (LORA). This hybrid approach reduces reliance on user-generated ratings by allowing LORA to generate initial ratings based on the learners’ profiles and resource characteristics, thus filling gaps in interaction history. The model was evaluated through experiments assessing its prediction accuracy and relevance of recommendations by using Mean Absolute Error (MAE), Precision, and Recall. Additionally, the performance of the proposed hybrid model was compared with existing hybrid models through a comparative analysis. Results revealed that the proposed model outperformed previous hybrid recommender models, generating better prediction accuracy and recommendation relevance. The integration of a hybrid approach and LORA enabled the model to generate ratings based on learning styles and resource characteristics, mitigating the data sparsity issue and reducing dependence on user-generated ratings.
The Mediating Role of Perceived Ease of Learning in Teacher Readiness to Adopt Blockchain for Educational Assessment
Traditional educational assessment systems in Malaysia face persistent issues such as data security vulnerabilities, lack of transparency, and centralised control, which collectively undermine trust, reliability, and operational efficiency. While blockchain (BC) technology has emerged as a promising solution offering decentralisation, immutability, and enhanced data security, a critical gap exists in understanding teacher readiness to adopt such innovations within the education sector. Most prior research emphasises the technical feasibility of blockchain or user acceptance in non-educational contexts, often overlooking educators’ cognitive and pedagogical challenges when learning and using unfamiliar technologies. This study investigates how key attributes of BC relative advantage (RA), compatibility (COM), and complexity (CPX) influence teacher readiness for adoption, mediated by perceived ease of learning (PEOL). A total of 750 questionnaires were distributed, resulting in 514 valid responses from post-secondary school teachers in the northern region of Malaysia, achieved through a simple and proportionate stratified random sampling technique. Partial Least Squares Structural Equation Modelling (PLS-SEM) reveals that COM and CPX significantly predict teacher readiness, while PEOL is a partial mediator. RA shows no significant direct or indirect effect, challenging assumptions about its centrality in technology adoption models. These findings contribute to the broader goals of Industrial Revolution (IR) 5.0 by highlighting the importance of human-centric, cognitively accessible technological integration strategies in education, particularly for fostering systemic reform in assessment practices.
Predictive Modeling of Software Team Performance based on Gender and Task Complexity using Logistic Regression and Decision Tree
This paper investigates the effect of gender and task complexity on software development performance, addressing a critical gap in empirical research and predictive modelling that warrants further investigation. Understanding how these factors interact is crucial for optimising task allocation and enhancing team efficiency in software engineering environments. To investigate this, data were collected from two universities involving 180 software development students with an equal representation of male and female participants. Participants were required to complete programming tasks at three levels of complexity—easy, medium, and hard—to assess their performance under varying cognitive loads. The results indicate a performance disparity based on task complexity, where male software developers consistently outperformed their female counterparts as the complexity of the tasks increased. Further analysis using predictive modelling techniques, which are logistic regression and decision tree models, revealed that logistic regression consistently achieved higher accuracy and F1 and AUC-ROC scores, demonstrating its superior predictive capability in modelling performance variations. Despite these findings, further research is necessary to explore additional interventions to help optimise task allocation based on individual strengths and expertise. Additionally, future studies should continue to challenge stereotypes and explore innovative strategies for promoting diversity and equality in software development.
Opinion Mining and Perceptions of Self-Determination in Videoconference Classrooms Using Machine Learning and Multidimensional Sentiment Frameworks
This study provides a comprehensive analysis of learner sentiments regarding learning in videoconference (VC) environments, highlighting critical areas for improving digital education. In the mixed method approach, Azure Machine Learning's (AML) three-category sentiment analysis as a quantitative approach is enhanced by a multidimensional sentiment model as a qualitative approach to analyse learner feedback. The sentiments are mapped onto the Self-Determination Theory (SDT) framework to highlight how autonomy, competence, and relatedness influence learner sentiments. Open-ended feedback to a prompt from 268 respondents across different education levels provided textual data captured through a Google form. AML provided sentiment scores that indicated the direction and type of sentiments. Combining with thematic analysis based on 5 sentiment categories enables the generation of richer and deeper insights into learner feedback. Findings indicate mixed user sentiments, with flexibility (autonomy) praised but technical issues and limited interaction (low competence and relatedness) criticised. Findings suggest enhancing interactivity, addressing technical barriers, and fostering engagement to improve learner experiences and meet diverse educational needs in digital classrooms. AML provides a broad sentiment classification with confidence scores that are more relatable and reliable. While recognising the overlapping sentiment categories in the two models, the approach offers more actionable insights for educators and platform developers. AML’s model could benefit from incorporating more granular sentiment categories to capture the complexity of user feedback better. The study enables a broader view of the user's need to provide more useful information to developers, policymakers, and instructors regarding using VC and other online platforms for teaching.