Anomaly detection is a significant problem that has been studied in a broader spectrum of
research areas due to its diverse applications in different domains. Despite the usage of …

Time-series data have been extensively collected and analyzed in many disciplines, such as
stock market, medical diagnosis, meteorology, and oil and gas industry. Numerous data in …

Detecting anomalous subsequences in time series data is an important task in areas
ranging from manufacturing processes over finance applications to health care monitoring …

Fault detection is a critical step for machine condition monitoring and maintenance. With
advances in machine learning technologies, automated faulty condition identification can be …

Learning temporal patterns in time series remains a challenging task up until today.
Particularly for anomaly detection in time series, it is essential to learn the underlying …

Pipelines are one of the most common systems for storing and transporting petroleum
products, both liquid and gaseous. Despite the durable structures, leakages can occur for …

Accurate anomaly detection is critical to the early detection of potential failures of industrial
systems and proactive maintenance schedule management. There are some existing …

Recurrent Neural Networks are powerful machine learning frameworks that allow for data to
be saved and referenced in a temporal sequence. This opens many new possibilities in …

Connected and autonomous vehicles have recently emerged as promising technological
solutions to optimize traffic congestion, prevent accidents, and enhance driving safety and …

Aircraft maintenance routing (AMR) is crucial for both passenger airlines and cargo airlines
to maintain flight operations efficiency, meanwhile it ensures that aircraft fulfill the civil …