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.

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.

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.

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.

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.

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.

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.

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.

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.