Logistic regression is a commonly used classification algorithm in machine learning. It
allows categorizing data into discrete classes by learning the relationship from a given set of …

For class-imbalance problems, traditional supervised learning algorithms tend to favor
majority instances (also called negative instances). Therefore, it is difficult for them to …

Monitoring valve operation status is very significant in saving natural gas resources and
realizing sustainability of the fossil energy. At present, many machine learning algorithms …

Support vector machines (SVMs) are some of the most successful machine learning models
for binary classification problems. Their key idea is maximizing the margin from the data to …

Sparse representation of kernel based regression (KBR) has received considerable
attention in recent years. Studies on sparse KBR can be divided into two distinct groups …

This project aims to investigate the best machine learning (ML) algorithm for classifying
sounds originating from the environment that were considered noise pollution in smart cities …

Traffic flow usually contains complex nonlinear patterns. Deep learning can model nonlinear
fluctuations through iterative updates of trainable parameters. It generally requires a large …

Support vector classification (SVC) is a well-known statistical technique for classification
problems in machine learning and other fields. An important question for SVC is the …

Dissolved oxygen (DO) concentration in aquatic systems plays a vital role in water
aquaculture. An innovative approach that combines feature selection and ensemble …

This article considers the parameter estimation problems for the controlled autoregressive
systems interfered by moving average noises. A recursive extended gradient algorithm with …