In recent years, with the rapid development of distributed photovoltaic systems (DPVS), the
shortage of data monitoring devices and the difficulty of comprehensive coverage of …

Networking is crucial for smart city projects nowadays, as it offers an environment where
people and things are connected. This paper presents a chronology of factors on the …

Various applications, such as electronic business, satellite remote sensing, intrusion
discovery, and network traffic monitoring, generate large unbounded data stream sequences …

As a representative evidential clustering algorithm, evidential c-means (ECM) provides a
deeper insight into the data by allowing an object to belong not only to a single class, but …

In Big data computing domains with a huge network of connected devices involved in
various internet and social network concerns mainly for security, integrity, authentication and …

With the proliferation of applications generating vast volumes of data streams, numerous
clustering methods have emerged to process and extract valuable insights from this data …

Statistical partitioning and publishing is commonly used in location-based big data services
to address queries such as the number of points of interest, available vehicles, traffic flows …

Practical machine learning applications for streaming data can involve concept drift (the
change in statistical properties of data over time), one-shot or few-shot learning (starting with …

In the network environment, data from various industries is dynamic and large-scale.
Traditional clustering algorithms struggle to effectively utilize existing clustering results when …

Data compression at the Internet of Things (IoT) edge node aims to minimize data traffic in
smart cities. The traditional Huffman Coding Algorithm (HCA) is shown as the most effective …