: anomaly detection based on multivariate normal distribution and deep reinforcement learning using a Deep Q-Network. While … anomaly detection effectively identifies deviations from normal transaction patterns, its static nature limits adaptability in … the DQN are modest compared to anomaly detection, its capacity for online learning and policy refinement enables long …

disclosure using sentence embeddings, multivariate anomaly detection, and explainable artificial intelligence. Prior research … historical baseline through unsupervised anomaly detection. Using Item 1A Risk Factors from Wells Fargo and JPMorgan Chase …

In the burgeoning field of anomaly detection within attributed networks, traditional methodologies often encounter the … residual-based attention mechanism, thereby creating a more nuanced and efficient method for anomaly detection in complex … surpasses existing state-of-the-art methods in anomaly detection. The results show the exceptional capability of our model to …

Anomaly detection is an important research area in a diverse range of real-world applications. Although many algorithms … have been proposed to address anomaly detection for numerical datasets, categorical and mixed datasets remain a significant … categorical anomaly detection approach, offering two key contributions to existing methods. First, a novel surprisal-based anomaly …

substantial practical contributions, providing detailed insights into news source contributions, effective anomaly detection, and …

) architectures tailored to blockchain data, enabling effective anomaly detection. To address the challenge of limited labeled data … anomaly detection accuracy and computational efficiency compared to conventional approaches, offering a practical and scalable …