Recurrent neural networks (RNNs) have significantly advanced the field of machine learning
(ML) by enabling the effective processing of sequential data. This paper provides a …

Wireless Sensor Networks (WSNs) encounter a substantial challenge when it comes to
energy conservation. As sensor nodes rely on battery power to operate in unattended …

The increasing adoption of batteries in a variety of applications has highlighted the necessity
of accurate parameter identification and effective modeling, especially for lithium-ion …

Anomaly detection in time series data plays a critical role in various domains, including
cybersecurity, industrial monitoring, and financial fraud detection. In recent years, deep …

针对无线传感器网络(WSN) 异常检测中的自监督学习异常检测方法需要解决负例样本信息表示
单一缺乏多样性和提取WSN 节点采集到的多模态数据时空特征不够充分影响异常检测性能的 …

To tackle the defects of self-supervised learning anomaly detection methods for wireless
sensor network (WSN) need to address the problems of single negative sample types and …

In the realm of intrusion detection, the effective utilization of machine learning algorithms is
paramount to safeguarding network security. Using the NSL-KDD dataset, this study …

In the context of time series data, a contextual anomaly is considered an event or action that
causes a deviation in the data values from the norm. This deviation may appear normal if we …

The advancement of mobile broadband networks has introduced new challenges in data
privacy and the efficiency of anomaly detection. Traditional centralized data processing …

Аннотация В статье предлагается комбинированная методика сбора, предобработки и
анализа данных в беспроводных сенсорных сетях (БСС) для решения задач …