The rapid evolution of technology has led to the generation of high dimensional data
streams in a wide range of fields, such as genomics, signal processing, and finance. The …

Outlier detection technologies play an important role in various application domains. Most
existing outlier detection algorithms have difficulty detecting outliers that are mixed within …

The building industry generates large amounts of solid waste due to construction,
maintenance, or demolition activities. Although the construction and demolition waste is well …

Outlier detection, also known as anomaly detection, has been a persistent and active
research area for decades due to its wide range of applications in various fields. Many well …

Microarrays have revolutionized genomics by enabling the simultaneous measurement of
thousands of gene expressions. However, the high dimensionality of microarray data poses …

Outliers accompany control engineers in their real life activity. Industrial reality is much richer
than elementary linear, quadratic, Gaussian assumptions. Outliers appear due to various …

Credit card fraud detection is a critical challenge in the financial sector, demanding
sophisticated approaches to accurately identify fraudulent transactions. This research …

Purpose This study aims to explain the state-of-the-art machine learning models that are
used in the intrusion detection problem for human-being understandable and study the …

In this article, we evaluate the efficiency and performance of two clustering algorithms: AHC
(Agglomerative Hierarchical Clustering) and K− M eans. We are aware that there are various …

Shale gas reservoirs have huge amounts of reserves. Economically evaluating these
reserves is challenging due to complex driving mechanisms, complex drilling and …