English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The prevailing developments in internet of things (IoT) and other sensor technologies such as cyber physical systems (CPS) and wireless sensor networks (WSNs), the huge amount of sensor data has been generating from various IoT devices and protocols. Making predictions and finding density patterns over such data is a challenging task. In order to find the density patterns and make analysis over real-time dynamic data, the machine learning (ML) based algorithms are widely used to deal with the IoT data. In this article, the authors proposed an efficient ML-based cluster analysis mechanism for finding density patterns in IoT dynamic data effectively. In this proposed mechanism, the k-means and GMM models are used for clustering data analysis. The proposed mechanism has been implemented on ThingSpeak Cloud platform for analysing the data efficiently on daily and weekly basis. Finally, the proposed mechanism acquired superior results than the existing benchmarked mechanisms over all the performance evaluation metrics used for analysis over IoT dynamic data.

An Efficient Machine Learning-Based Cluster Analysis Mechanism for IoT Data