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.
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.
Copyright (c) 2026 Journal of Information and Communication Technology https://creativecommons.org/licenses/by/4.0
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.
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.
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.
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.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
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.
Prediction of Rice Yields in a Changing Climate Using the Mobile Rice Yield Prediction Application
In Malaysia, rice production is crucial for national food security but faces significant challenges due to unpredictable climate patterns and climate change. Traditional prediction methods often struggle to capture the complex interaction between climatic and agricultural factors that cause a critical gap in accurately forecasting rice yields. This paper examines the Mobile Rice Yield Prediction Application (MRYPA) through a comparative analysis of two regression models: A baseline multiple linear regression model and an enhanced version incorporating polynomial and interaction terms. A 10-year dataset from the Malaysian Meteorological Department (MetMalaysia) and the Department of Statistics Malaysia (DOA) were trained and tested on varying data splits and then evaluated using performance metrics such as R-squared, Mean Absolute Error, Mean Square Error, and Root Mean Square Error. The findings reveal that while the enhanced model demonstrates marginally better predictive accuracy in smaller datasets, both models exhibit comparable performance with larger training datasets. A 50% train-test split yielded the best results for both models, achieving an R-squared value of 0.9843 for the baseline model and marginal improvements for the enhanced model. These findings underscore the potential of integrating yield and climate data into user-friendly tools, such as MRYPA, to empower stakeholders to make informed, data-driven decisions for sustainable agriculture and national food security. Future work will refine the prediction accuracy and system utility by integrating different data sources and advanced machine-learning models.
Exploring Deep Learning Techniques for Sentiment Analysis inOnline Education Platforms: A Case Study of Coursera Reviews
The rise of online courses, accelerated by the COVID-19 pandemic, has underscored the need for effective educational models capable of addressing the challenges posed by remote learning. This study focuses on the development of sentiment classifiers using the Coursera reviews dataset to evaluate the polarity of student feedback. This research improved student engagement and support in online education by applying sophisticated sentiment analysis techniques. We explored a comprehensive methodology encompassing various pre-processing techniques, advanced tokenisation methods, and a range of deep learning architectures, including Feedforward Neural Networks (FNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), and Bidirectional Encoder Representations from Transformers (BERT)-based models. Each model’s performance is optimised through meticulous hyperparameter tuning using the Optuna framework. Results indicated that BERT is the best model, achieving a recall of 97.50% and an accuracy of 96.83%, while Bidirectional LSTM (BiLSTM) closely followed with a recall of 96.55% and an accuracy of 96.71%. In contrast, simpler models like FNN and RNN exhibited lower accuracy (92.83% and 87.83%, respectively). These findings underscore the importance of advanced models in capturing contextual meanings and highlight the effectiveness of leveraging embeddings, attention mechanisms, and tailored pre-processing strategies, which significantly improve sentiment classification performance.
Adoption Intentions Toward AI-based Clinical Decision Support Tools: A Tam Study on Hospital Pharmacists
The increasing prevalence of antibiotic resistance leads to an alarming challenge to global healthcare, necessitating the adoption of machine learning (ML)-driven clinical decision support systems (CDSS) to enhance antimicrobial stewardship. Despite its potential, hospital pharmacist’s adoption of ML-based antibiotic resistance predictors remains limited due to usability, trust, organisational support, and perceived risk concerns. This study uses an extended version of Technology Acceptance Model (TAM) to examine the primary factors influencing pharmacists’ behavioural intention (BI) to adopt the ML-driven CDSS. A cross-sectional survey was conducted among 235 hospital pharmacists across major provinces in Indonesia, using a PLS-SEM approach to test hypothesised relationships. The findings reveal that perceived usefulness (PU) (β = 0.355, p < 0.001) is the strongest predictor of BI, followed by trust in technology (TT) (β = 0.179, p = 0.017), social influence (SI) (β = 0.184, p = 0.009), and facilitating conditions (FC) (β = 0.150, p = 0.010). Perceived risk (PR) negatively affects BI (β = -0.103, p = 0.023), highlighting concerns over AI reliability. The study underlines the need for hospital administrators to enhance IT support and training, policymakers to establish AI regulatory frameworks, and professional organisations to promote AI acceptance through peer advocacy. Clinically, increased adoption of ML-driven CDSS can improve the accuracy of antibiotic prescribing, reduce resistance rates, and enhance patient safety. It provides insights into optimising AI interventions in antimicrobial stewardship programmes and guiding future implementation strategies in hospital pharmacy practice.
Transforming Alzheimer’s Disease Diagnosis: Implementing Vision Transformer (ViT) for MRI Images Classification
Alzheimer’s disease (AD) is a progressive neurological disorder and the leading cause of dementia, accounting for 60-80% of cases. Early detection of AD is crucial for timely intervention, as the disease significantly impacts cognitive functions and daily activities. Diagnosing AD in clinical settings remains challenging due to subtle early symptoms, diverse presentations, lengthy diagnostic processes, and inconsistent criteria that heavily rely on medical expertise. Accurate and timely diagnosis during the early stages is essential for effective treatment and intervention. Modern imaging techniques, such as Magnetic Resonance Imaging (MRI), have become essential in diagnosing Alzheimer’s by providing detailed insights into structural brain changes. This study explores the application of the Vision Transformer (ViT) model for classifying MRI images of Alzheimer’s patients, focusing on enhancing accuracy and reliability through data augmentation during pre-processing. A dataset of 8,000 MRI images, categorised into four groups—non-demented, very mild demented, mild demented, and moderate demented—was used to evaluate the ViT model. The experiment achieved promising results, with an accuracy of 98.19%, sensitivity of 96.34%, specificity of 98.80%, and an F1-score of 96.37%. These findings underscore the model’s effectiveness in distinguishing between affected and unaffected individuals, minimising misdiagnosis and enabling timely clinical interventions. However, some challenges remain, particularly in the classification between “Non-Demented” and “Very Mild Demented” cases. Future research should focus on enhancing data augmentation techniques and increasing data diversity in these categories to improve the model’s performance further. The ViT model holds great potential for advancing Alzheimer’s diagnosis, offering a valuable tool for early detection and intervention in clinical settings.
Integrating Information Gain and Chi-Square for Enhanced Malware Detection Performance
Malware represents a serious and continuously evolving threat in the modern digital environment. Detecting malware is essential to safeguard devices and systems from risks such as data corruption, data theft, account compromises, and unauthorized access that could result in total system takeover. As malware has progressed from its simpler, monomorphic variants to more sophisticated forms like oligomorphic, polymorphic, and metamorphic, a machine learning-based detection system is now required, surpassing the limitations of traditional signature-based methods. Recent studies have shown that this challenge can be addressed by employing machine learning algorithms for detection. Some studies have also implemented various feature selection methods to optimize detection efficiency. However, they continue to struggle with false positives and false negatives, striving to reach zero tolerance in malware detection. This study introduces the IGCS method, a combined feature selection approach that integrates Information Gain with Chi-Square (X²) to enhance both the effectiveness and efficiency of machine learning classifiers. Using IGCS, six classifiers—Random Forest, XGBoost, kNN, Decision Tree, Logistic Regression, and Naïve Bayes—achieved higher performance scores compared to other scenarios, such as when classifiers were combined with Information Gain, Chi-Square, PCA, or even without any feature selection. As a result, Random Forest with 30 features selected by IGCS proved superior to any combination of classifiers and feature selection methods in malware detection, achieving 99.0% accuracy, recall, precision, and F1-Score. This combination also demonstrated efficiency with a 52.5% decrease in training time and a 56.9% decrease in testing time.
Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification
Microarray-based cancer biomarker detection is one of the popular trends for cancer classification. Though existing approaches have given competing performance in terms of classification accuracy and reduced feature subsets, the classification of different cancer microarray datasets still requires improvements. Recently, the swarm-based hybrid algorithms have given significant performance in cancer classification. However, the efficiency of a swarm algorithm is dominated by certain factors such as fitness value, convergence, exploration, and exploitation capabilities. Thus, a swarm-based hybrid approach is proposed for cancer classification with a new variant of the Firefly Algorithm (FA) and Correlation-based Feature Selection (CFS) filter. The slow convergence issue in the FA is resolved by non-fixed size solutions termed as mutable size solutions and a composite position update function is designed for the mutable solutions. In addition, the local optima issue is overcome by the population reinitialisation method. The proposed algorithm, named the CFS-Mutable Composite Firefly Algorithm (CFS-MCFA), is evaluated based on two metrics, namely classification accuracy and genes subset size, using a Support Vector Machine (SVM) classifier. Results show that CFS-MCFA-SVM achieved 100% accuracy with only a few biomarkers for all four cancer microarray datasets, indicating the efficiency and the competing performance of the proposed algorithm in biomarker detection for microarray-based cancer classification. Apart from that, the proposed algorithm would also contribute to cancer-related issues upon verifying the relevancy of particular genes via technical analysis from a medical perspective and would be utilised in feature selection applications.
Multi-Objective Portfolio Optimization Strategy Using the SPEA-II Algorithm
In the finance world, the precise selection and optimization of stock portfolios are of paramount importance. This study explores the application of intelligent algorithms, particularly the multi-objective of Strength Pareto Evolutionary Algorithm II (SPEA-II), alongside traditional methods to determine optimal portfolios. Using the monthly stock prices, the Markowitz model is developed, focusing on the return and semi-variance criteria. Realistic constraints are applied to formulate a multi-objective optimization problem. SPEA-II and traditional multi-objective optimization methods are used to solve this problem, resulting in a set of optimal portfolios. The results show that the SPEA-II algorithm can generate portfolios with higher returns and lower risks compared to the Markowitz model and traditional methods, taking into account the complex and nonlinear conditions of the capital market. In addition, the SPEA- II algorithm showed significant efficiency and stability across different frequencies and time periods. The study highlights that the SPEA-II algorithm can serve as an effective and efficient method for stock portfolio selection and optimization, helping investors to identify portfolios with lower risk and higher return.
Deep Learning Approach in Seismology: Enhancing Earthquake Forecasting using K-Means Clustering and LSTM Networks
Located in the subduction zone of four tectonic plates, the high occurrence of seismic events is a severe threat in Indonesia. Mitigating the adverse effects of such disasters is essential to forecast the likelihood of future earthquakes. Consequently, developing a robust method of forecasting future earthquakes is critical to facilitate prevention and mitigation efforts. A reliable earthquake prediction method is necessary to reduce the after-effects to the greatest extent possible. This study utilises historical seismic and proposes innovative data pre-processing methods using K-means clustering to build a Long Short-Term Memory (LSTM) model for earthquake forecasting to overcome high-disparity locations. Four LSTM layers are embedded with adjusted fine-tuned network hyperparameters to enhance forecasting accuracy. The results attain 0.379816, 0.616292, and 0.414586 for Mean Square Error (MSE), Root MSE, and Mean Absolute Error, respectively, providing significant insights into earthquake prediction. In addition, predicted seismic occurrences are plotted on a map to display their geographic location within the specified research region. This research provides significant value in facilitating the efficient distribution of resources, such as evacuating residents impacted by earthquakes or reinforcing buildings and infrastructure, for emergency responders and policymakers.
Unlocking AI-Powered Tools Adoption among University Students: A Fuzzy-Set Approach
This study examines, from a post-pandemic theoretical perspective, university students' continuous intention (CI) to utilise AI-powered tools for educational purposes. AI-powered tools are new and underutilised in higher education. The fact that students and teachers need knowledge to use these apps in the classroom compounds the issue. Despite this technology's recent academic introduction, nothing is known about its impacts. In order to investigate the variables that influence the continual intention to employ artificial intelligence, this study discusses the possibility of integrating the self-determination theory (SDT) and technology acceptance model (TAM) with the post-acceptance model (PAM). Three hundred forty university students were solicited to complete a questionnaire to collect data for the proposed model. A dual-stage approach uses both symmetrical assumptions from structural equation modelling with partial least squares (PLS-SEM) and asymmetrical configurations from fuzzy-set qualitative comparative analysis (fsQCA). In order to better comprehend the intricate interplay between the model's inputs and its desired output, this approach is devised. Consideration is given to the fact that various configurations of external constructs exert distinct influences on internal constructs. In Thailand, perceived usefulness (PU) and autonomy predict continued AI-powered tool use. Perceived ease of use (PEOU) did not affect continuing intention. Conclusions drawn from the configurational analysis show that no single factor adequately explains a high CI level. Rather, three distinct configurations were identified as improving CI using AI-powered tools. Overall, theoretical and practical ramifications are addressed.
A Decade in Blockchain: A Bibliometric Reflection on the Growth and Interdisciplinary Reach of a Disruptive Technology
This study seeks to deliver a comprehensive bibliometric analysis of blockchain research undertaken from 2013 to 2023. It seeks to map the field’s evolution, highlight emerging trends, and identify key contributors and thematic areas. Understanding these aspects is crucial for guiding future research and policymaking in the rapidly growing domain of blockchain technology. The study utilises a systematic bibliometric analysis approach and the Scopus database was used for collecting data. The analysis includes keyword trends, citation patterns, author productivity, and network analysis. The results reveal significant expansion and diversification in blockchain research, with 13,369 publications and 52,009 contributing authors. Key metrics include a total citation count of 266,483 and an average of 19.93 citations per paper, underscoring the field’s scholarly impact. The study identifies core research themes such as smart contracts, security, IoT integration, and emerging topics like AI and sustainability. The findings highlight the interdisciplinary nature of blockchain research and its global distribution, with notable contributions from China and the United States. The study’s reliance on selected databases and predefined keywords may exclude relevant grey literature and industry reports. Future research should integrate these sources for a more complete view. This study provides a panoramic view of blockchain research, offering valuable insights into its development, current state, and future directions. Identifying key trends, influential contributors, and emerging themes contributes to the academic discourse and supports strategic planning for researchers, practitioners, and policymakers engaged in blockchain technology.
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Impact of Balanced Exploration and Exploitation on High-dimensional Feature Selection with Hierarchical Whale Optimisation Algorithm
High-dimensional datasets (HDDs) pose significant challenges in feature selection due to their complex nature. While metaheuristic algorithms like the Whale Optimisation Algorithm (WOA) have shown promise in addressing these challenges, stability issues are least addressed. Through an extensive literature review, it was identified that stability is manifested in the equilibrium of exploration and exploitation. Hence, this study introduced the Hierarchical Whale Optimisation Algorithm (HiWOA), an approach designed to enhance the WOA’s stability and performance in HDD feature selection tasks. The HiWOA incorporates a two-phase strategy comprising a nonlinear control parameter based on the arcsine function and a hierarchical position-update mechanism adapted from the Grey Wolf Optimiser. The proposed HiWOA was evaluated through 23 benchmark optimisation functions and feature selection experiments on 11 medical HDDs. The results indicate that the HiWOA outperformed the WOA and a modified variant (mWOA) in terms of a better fitness value, a more balanced exploration-exploitation ratio, and improved classification accuracy with fewer selected features. These findings demonstrate the HiWOA’s effectiveness in enhancing stability, making it a robust solution for high-dimensional optimisation and feature selection.
Restoration and Segmentation of Old Jawi Manuscripts using Variational Image Inpainting and Active Contour Models
Old Jawi Manuscripts (OJM) are crucial to historical studies, offering insights into past societies. However, degradation from mishandling and environmental factors can impair their legibility. To preserve OJM, image inpainting and segmentation are essential for restoring corrupted areas and identifying text. Recently, the Gaussian Regularization Segmentation (GRS) model has shown effectiveness in intensity inhomogeneity grayscale image segmentation, though it was not designed for corrupted OJM images. Therefore, this study aimed to reformulate the GRS model to restore and segment text from real corrupted OJM images. The methodology begins with the incorporation of the Mumford-Shah and Bertalmio inpainting models into the GRS model as new fitting terms, resulting in the Modified Gaussian Regularization Segmentation Mumford-Shah (MGRSM) model and the Modified Gaussian Regularization Segmentation Bertalmio (MGRSB) model, respectively. MATLAB was used to implement these models, and their performance was assessed on 30 corrupted OJM samples from Malay Ethnomathematics Research Group, with expert evaluations and efficiency measured by average elapsed time. The MGRSM model achieved 38.4 percent and 12.4 percent higher overall total scores from experts in terms of segmentation accuracy compared to the GRS and MGRSB models, respectively. While the GRS model is the fastest, the MGRSM model provides superior accuracy, with an average processing time of 9.35 seconds, making it the most optimal for restoring and segmenting OJM images. This approach not only enhances the preservation of historical manuscripts but also provides a practical tool for researchers and historians in safeguarding our cultural heritage.
Reinforcement Learning Algorithm for Optimising Durian Irrigation Systems: Maximising Growth and Water Efficiency
This study presents a Reinforcement Learning-based algorithm designed to optimise irrigation for Durio Zibethinus (i.e., durian) trees, aiming to maximise tree growth and reduce water usage. Traditional irrigation methods, as well as current machine learning models, often focus only on soil moisture and weather data, neglecting critical factors like actual tree growth. This study proposed a reinforcement learning irrigation (RL-Irr) algorithm incorporating tree growth stages, soil moisture, and weather conditions to determine precise irrigation needs. The algorithm was developed by calibrating the AQUACROP model using data from actual durian plantations where rain-fed irrigation (rain-fed) was practised. Daily irrigation volumes were calculated based on real-time soil moisture, weather forecasts, and weekly tree growth measurements. The reinforcement learning method was used to optimise irrigation schedules, with rewards based on soil moisture, tree growth, rainfall, and weather conditions. The algorithm was tested using AQUACROP simulations and compared against soil moisture balance irrigation (SMB-Irr) and rain-fed. The results showed that the RL-Irr reduced water use by up to 75 percent while maintaining tree growth. These findings suggest the algorithm could significantly improve water efficiency in durian farming, though real-world applications should consider potential model limitations.