Heating, Ventilation, and Air Conditioning (HVAC) systems play a critical role in ensuring energy efficiency, occupant comfort, and sustainability in modern buildings. Their complex operational dynamics,however, make them prone to faults that, if undetected, can result in higher energy consumption, reduced comfort, and costly maintenance. Existing approaches to fault detection and diagnosis (FDD) often face challenges related to scalability and interpretability, with many relying heavily on Transformer-or GNN-based models. This study introduces an enhanced TabNet-based semi-supervised framework for multi-class HVAC fault detection, incorporating rigorous pseudo-label generation via Label Propagation, cost-sensitive learning for imbalanced classes, and hybrid feature engineering. Validated on the 2022 LBNL FDD FCU dataset, the model achieves an accuracy of 82.1%, along with high ROC-AUC and F1-scores values of 96.1% and 82.2% respectively, while its attention-based feature importance provided insights into the most influential operational variables. These results demonstrate that the TabNet framework effectively balances predictive power and interpretability, making it a practical solution for semi-supervised FDD in HVAC systems. The proposed approach contributes to building automation by offering a transparent and reliable pathway for fault detection.
I. Samuel, B. Stephen, S. Ozuomba et al.· E3S Web of Conferences· 0 citations
Financial fraud detection in transaction networks is challenging due to evolving attack strategies, complex relational structures, and extreme class imbalance. We propose a hybrid deep learning model that fuses Graph Convolutional Neural Networks (GCNNs) with bidirectional LSTMs enhanced by temporal attention, enabling joint modeling of structural dependencies and sequential transaction patterns. To improve class discrimination, the framework incorporates Triplet Loss, enhancing prior contrastive approaches, which enhances embedding separability under highly imbalanced conditions. Furthermore, we introduce graph augmentation strategies, including edge perturbation, node feature masking, and adaptive subgraph sampling, to increase robustness against noise and incomplete networks. Experiments on the IEEE-CIS and synthetic datasets with varied patterns demonstrate that the proposed model achieves up to 6.2% improvement in F1-score with a modest precision-recall trade-off favoring high-recall scenarios and 12% higher recall compared to strong baselines. Ablation studies confirm the complementary roles of the graph, temporal, and metric learning components. These consistent improvements, demonstrate that incorporating Triplet Loss within graph–temporal modeling provides a principled and effective approach to imbalanced fraud detection, establishing our model as a scalable and robust solution for fraud detection in financial transaction networks.
Ofonime Dominic Okon, Imo Enang, B. Stephen et al.· E3S Web of Conferences· 0 citations
Antibiotic resistance (AR) has emerged as a pressing global health challenge, undermining the effectiveness of conventional treatment options and threatening public health systems around the world. The rapid identification of resistance genes and their associated mechanisms is therefore critical for the development of diagnostic and therapeutic strategies. This study presents the development of an ensemble machine learning pipeline for predicting resistance gene functions using the Comprehensive Antibiotic Resistance Database (CARD). Protein sequences were extracted from CARD and processed into features using sequence-derived representations, including kmer embeddings and TF-IDF vectorization. An ensemble voting classifier was implemented, combining five base estimators: Extra Trees Classifier, Random Forest, XGBoost, Linear Discriminant Analysis, and K-Nearest Neighbors. The ensemble approach utilized both hard and soft voting strategies, with optimized weights determined through log-loss minimization. The system was evaluated on a curated subset of the CARD dataset, ensuring balanced class representation across 62 antibiotic drug classes. Results demonstrate that the ensemble approach achieves superior classification performance, with the optimized soft voting classifier achieving 89.76% accuracy, 92% precision, 90% recall, and 90% F1-score. The hard voting ensemble achieved 89.44% accuracy with comparable precision and recall metrics. These results represent significant improvements over individual base classifiers, highlighting the effectiveness of ensemble methods for antibiotic resistance prediction. The ensemble approach demonstrates superior performance while maintaining computational efficiency, making it suitable for deployment in resourceconstrained environments.
Princewill Ahumaraeze, Ofonime Dominic Okon, P. Asuquo et al.· E3S Web of Conferences· 0 citations
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