Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

DNA microarray data analysis is infamous due to a massive number of features, imbalanced
class distribution, and limited available samples. In this paper, we focus on high …

This paper presents a method for identifying fraudulent fund transfers using real bank data,
analyzing customer information, transactional activities, and customer relationships. The …

Resampling is the most commonly used method for dealing with imbalanced data, in
addition to modifying the algorithm mechanism, it can, for example, generate new minority …

Most existing deep ensemble credit scoring models have considered deep neural networks,
for which the structures are difficult to design and the modeling results are difficult to …

Fault detection is one of the most important research topics to guarantee safe operation and
product quality consistency especially in the batch process of semiconductor manufacturing …

Contrastive learning is a representation learning method performed by contrasting a sample
to other similar samples so that they are brought closely together, forming clusters in the …

Production environments bring inherent system challenges that are reflected in the high-
dimensional production data. The data is often nonstationary, is not available in sufficient …

The location fingerprinting method, which typically utilizes supervised learning, has been
widely adopted as a viable solution for the indoor positioning problem. Many indoor …

Outdoor positioning systems based on the Global Navigation Satellite System have several
shortcomings that have deemed their use for indoor positioning impractical. Location …