Advances in recent techniques for scientific data collection in the era of big data allow for the
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …

Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

We present CLUSTSEG, a general, transformer-based framework that tackles different
image segmentation tasks (ie, superpixel, semantic, instance, and panoptic) through a …

Clustering is the process of grouping the data based on their similar properties. Meanwhile,
it is the categorization of a set of data into similar groups (clusters), and the elements in each …

With the rapid development of electric vehicles, the safe and environmentally friendly
disposal of retired lithium batteries (LIBs) is becoming a serious issue. Echelon utilization of …

Clustering is a data mining technique used to analyse data that has variations and the
number of lots. Clustering was process of grouping data into a cluster, so they contained …

Conventional supervised and unsupervised machine learning models used for landslide
susceptibility prediction (LSP) have many drawbacks, such as an insufficient number of …

In this paper, we study what are the most important factors that deteriorate the performance
of the k-means algorithm, and how much this deterioration can be overcome either by using …

With the increasing production and marketing of global electric vehicles (EVs), a large
quantity of lithium ion battery (LIB) raw materials are demanded, and massive LIBs will be …

Increased interest in the opportunities provided by artificial intelligence and machine
learning has spawned a new field of health-care research. The new tools under …