The knowledge discovery in database (KDD) is alarmed with development of methods and
techniques for making use of data. One of the most important step of the KDD is the data …

Outlier detection has been a topic in statistics for centuries. Over mainly the last two
decades, there has been also an increasing interest in the database and data mining …

Real-world timeseries have complex underlying temporal dynamics and the detection of
anomalies is challenging. In this paper, we propose the Temporal Hierarchical One-Class …

Traditional distance and density-based anomaly detection techniques are unable to detect
periodic and seasonality related point anomalies which occur commonly in streaming data …

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas
such as security, finance, health care, and law enforcement. While numerous techniques …

Women who have recovered from breast cancer (BC) always fear its recurrence. The fact
that they have endured the painstaking treatment makes recurrence their greatest fear …

Time series anomaly detection is challenging due to the complexity and variety of patterns
that can occur. One major difficulty arises from modeling time-dependent relationships to …

Advanced technologies are constantly emerging in automobile industry, which not only
provides drivers with a comfortable driving experience, but also enhances the safety of …

The aim of this study is to estimate the state of charge (SOC) of the lithium iron phosphate
(LiFePO4) battery pack by applying machine learning strategy. To reduce the noise sensitive …

The significant advancements in sensors and other resource-constrained devices, capable
of collecting data and communicating wirelessly, are poised to revolutionize numerous …