The advancement of Industrial Internet of Things (IIoT) technology has resulted in the fourth
industrial revolution, or Industry 4.0, enabling industries to enhance productivity. However …

Clustering is a ubiquitous task in data science. Compared to the commonly used k-means
clustering, k-medoids clustering requires the cluster centers to be actual data points and …

The consumer health industry has witnessed a transformative revolution driven by the
Internet of Medical Things (IoMT) and the Metaverse. Additionally, Artificial Intelligence (AI) …

With the continuous elevation of demand for large-scale wind turbines and operation &
maintenance cost an increasing interest has been rapidly generated on CM (Condition …

Clustering is the task of assigning unlabeled data points into a finite number of clusters. The
assignment is usually based on similarity or distance, so data points located in the same …

The era of huge data necessitates highly efficient machine learning algorithms. Many
common machine learning algorithms, however, rely on computationally intensive …

Production of insurance policies in all types of Insurance requires a thorough examination of
the entity against which the Insurance is to be issued. In health insurance, it is the past …

k-means and k-medoids are arguably the two most popular clustering methods. This paper
reports an empirical study of the relative (de) merits of these two methods. We compare their …

Sorting does not only occupy a central place in computer science; it also constitutes a
building block in machine learning algorithms. This paper draws a useful connection …

This thesis used statistical methods to investigate and more accurately forecast Australian
emergency department presentations to four major public hospital EDs in South-East …