The Financial Technology (FinTech) sector has witnessed rapid growth, resulting in increasingly complex and high-volume digital transactions. Although this expansion improves efficiency and accessibility, it also introduces significant vulnerabilities, including fraud, money laundering, and market manipulation. Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data. Graph Neural Networks (GNNs), capable of modeling intricate interdependencies among entities, have emerged as a powerful framework for detecting subtle and sophisticated anomalies. However, the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability, performance, and interpretability. This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech, with an emphasis on the synergistic role of feature selection. We examine the theoretical foundations of GNNs, review state-of-the-art feature selection techniques, analyze their integration with GNNs, and categorize prevalent anomaly types in FinTech applications. In addition, we discuss practical implementation challenges, highlight representative case studies, and propose future research directions to advance the field of graph-based anomaly detection in financial systems.

Ensuring the reliability of power transmission networks depends heavily on the early detection of faults in key components such as insulators, which serve both mechanical and electrical functions. Even a single defective insulator can lead to equipment breakdown, costly service interruptions, and increased maintenance demands. While unmanned aerial vehicles (UAVs) enable rapid and cost-effective collection of high-resolution imagery, accurate defect identification remains challenging due to cluttered backgrounds, variable lighting, and the diverse appearance of faults. To address these issues, we introduce a real-time inspection framework that integrates an enhanced YOLOv10 detector with a Hybrid Quantum-Enhanced Graph Neural Network (HQGNN). The YOLOv10 module, fine-tuned on domain-specific UAV datasets, improves detection precision, while the HQGNN ensures multi-object tracking and temporal consistency across video frames. This synergy enables reliable and efficient identification of faulty insulators under complex environmental conditions. Experimental results show that the proposed YOLOv10-HQGNN model surpasses existing methods across all metrics, achieving Recall of 0.85 and Average Precision (AP) of 0.83, with clear gains in both accuracy and throughput. These advancements support automated, proactive maintenance strategies that minimize downtime and contribute to a safer, smarter energy infrastructure.

Visual speech recognition is a central problem in computer vision, encompassing both lip reading (visual speech recognition) and sign language recognition. Although substantial progress has been achieved independently on each task, their complementary characteristics have rarely been explored jointly. In this work we propose UniModal-LSR (Unified Multimodal Lip and Sign Recognition), a novel deep learning framework that jointly addresses lip reading and sign language recognition within a single multimodal architecture. By exploiting shared properties of visual communication channels, namely temporal dynamics, spatial articulation structure, and contextual dependencies, the proposed model enables bidirectional transfer of knowledge between modalities. The framework incorporates a Hierarchical Temporal-Spatial Encoder that captures multi-scale temporal patterns through the combination of local convolutions and global self-attention. It also includes a Cross-Modal Attention Fusion module that performs dynamic, context-aware information exchange via bidirectional cross-attention and adaptive gating. Additionally, a Contrastive Semantic Alignment loss enforces semantic consistency across modality-specific representations. Overall, the architecture integrates three-dimensional convolutional neural networks for spatiotemporal feature extraction with graph neural networks for explicit hand-pose modeling. Extensive experiments on several public benchmarks show that UniModal-LSR improves performance compared with recent methods. The model attains a Word Error Rate (WER) of 33.2% on LRS2-BBC, representing a 12.4% relative gain. On PHOENIX-2014, it achieves 18.3% WER, a 13.7% relative gain. Moreover, the unified model reduces parameter count by 25.9% relative to two separate task-specific systems. These results indicate that unified multimodal modeling can improve visual speech recognition performance and may support future communication technologies.

Keeping customers engaged remains a major challenge in appointment-based services, where user behavior continuously shifts due to seasonal, market, and social factors. These dynamic changes often cause concept drift, rendering traditional deep clustering models unreliable because they assume stable data distributions. Most existing approaches handle representation learning, parameter optimization, and model updating as separate components, limiting their adaptability in real-world streaming environments. This study proposes Hybrid-RL, a novel adaptive clustering framework that unifies incremental deep representation learning, multi-head reinforcement learning for joint hyperparameter optimization (number of clusters, latent dimension, and clustering method), incremental model updating, bandit-based decision making, surrogate-model explainable artificial intelligence (XAI), and continuous Gini-based fairness monitoring within a single closed-loop pipeline. The framework updates incrementally via autoencoder fine-tuning and MiniBatchKMeans partial_fit without requiring full retraining, enabling efficient adaptation to evolving customer behavior. Experiments conducted on real proprietary appointment data (10,212 records collected from 2021 to 2025) with natural concept drift demonstrate that Hybrid-RL achieves superior clustering quality, recording a Silhouette score of 0.7542, Davies–Bouldin Index (DBI) of 0.3150, and Calinski–Harabasz (CH) index of 1810.34, while maintaining an ultra-low inference time of 0.0001 s per sample. The model significantly outperforms 13 baseline methods. Under controlled synthetic drift, Hybrid-RL exhibits only a 6.1% drop in Silhouette score, confirming strong robustness. Additional validation on the public UCI Online Retail dataset further confirms the framework’s generalizability. Fairness analysis reports an average Gini coefficient of 0.49 across clusters, indicating balanced action distribution.

Visual speech recognition (VSR) aims to infer spoken content from visual observations of articulatory movements. Despite significant progress, it remains a challenging task in computer vision and speech processing. Its difficulty arises from pronounced speaker-to-speaker variability, the presence of homophenes (phonemes that are visually indistinguishable), changes in illumination, and the intrinsically high-dimensional nature of spatiotemporal lip dynamics. In this work, we propose NestLipGNN, a graph-based framework that integrates Graph Neural Networks (GNNs) with a nested multi-granularity learning strategy for visual speech recognition. We construct dynamic lip graphs from facial landmarks to model both spatial relationships between lip regions and their temporal motion during speech articulation. The proposed nested learning architecture supports hierarchical feature extraction across several levels of linguistic abstraction, spanning phoneme-level articulatory units, viseme-level visual speech categories, and word-level semantic representations. We further introduce a Temporal Graph Attention mechanism (T-GAT) that adaptively reweights the importance of distinct lip regions over time. We also introduce a graph-based contrastive learning objective to improve the discrimination of visually similar speech patterns, directly confronting the challenge of homophene resolution. Experiments on the LRW, LRS2, LRS3, and GRID datasets show that NestLipGNN improves recognition accuracy compared with existing methods, obtaining 92.3% word-level accuracy on LRW and delivering a 2.1% absolute performance gain over prior methods. Comprehensive ablation analyses confirm the contribution of each architectural component.

Improving consumer satisfaction with the appearance and surface quality of wood-based products requires inspection methods that are both accurate and efficient. The adoption of artificial intelligence (AI) for surface evaluation has emerged as a promising solution. Since the visual appeal of wooden products directly impacts their market value and overall business success, effective quality control is crucial. However, conventional inspection techniques often fail to meet performance requirements due to limited accuracy and slow processing times. To address these shortcomings, the authors propose a real-time deep learning-based system for evaluating surface appearance quality. The method integrates object detection and classification within an area attention framework and leverages R-ELAN for advanced fine-tuning. This architecture supports precise identification and classification of multiple objects, even under ambiguous or visually complex conditions. Furthermore, the model is computationally efficient and well-suited to moderate or domain-specific datasets commonly found in industrial inspection tasks. Experimental validation on the Zenodo dataset shows that the model achieves an average precision (AP) of 60.6%, outperforming the current state-of-the-art YOLOv12 model (55.3%), with a fast inference time of approximately 70 milliseconds. These results underscore the potential of AI-powered methods to enhance surface quality inspection in the wood manufacturing sector.