Unsupervised Learning Model

Applications in 5G

Adoption in healthcare delivery



K-means Clustering, Gaussian Mixture Model (GMM), and Expectation Maximization (EM)

In automobile networks, cooperative spectrum sensing, and relay node selection are used.

Medical experts employ these models to develop more intelligent medical decision support systems, particularly for the treatment of liver diseases.

[35] [37]


Hierarchical Clustering

Detection of anomalies, faults, and intrusions in mobile wireless networks.

To discover the phylogenetic tree of animal evolution, this model can be used in conjunction with DNA sequencing. This is frequently crucial in determining the origin of a viral or disease outbreak.

[24] [35]


Unsupervised Soft-Clustering ML Framework

In heterogeneous cellular networks, latency is reduced by grouping fog nodes to automatically identify which low-power node (LPN) is converted to a high-power node (HPN). (LPN) is upgraded to a high-power node (HPN)

These models can be used to improve machine learning algorithms’ ability to diagnose chronic diseases.

[35] [38]


Affinity Propagation Clustering

Data-Driven Resource Management for Ultra-Dense Small Cells

This model can be adopted to investigate important genes associated with ovarian cancer.