Filter rule along with other trading algorithms is used to identify potentially profitable trading
points in stock markets. In this study, the scope of the filter rule has been expanded to …

It will be beneficial to devise an effective approach for short-term macroeconomic
forecasting. Existing traditional statistics-based macroeconomic forecasting mainly focuses …

Social robots coexist with humans in situations where they have to exhibit proper
communication skills. Since users may have different features and communicative …

In practical applications, imbalanced data has brought great challenges to classification
problems. In this paper, we propose two new methods:(1) a new oversampling method …

Time series forecasting has wide applications in our daily lives, such as meteorological
warnings and decision-making. However, traditional supervised models do not perform well …

Noisy label learning is an important process that facilitates the collection of noisy label data
for training accurate deep neural networks. The latest label correction methods are effective …

Predicting electricity demand is crucial for ensuring energy security. However, the low-
carbon energy transition has brought a new bidirectional feedback between power demand …

This article probes into the global polynomial stabilization (GPS) of proportional delayed
inertial memristive neural networks (PDIMNNs). Here, ruling out the reduced-order way …

In network intrusion detection, since the available attack traffic is much less than normal
traffic, detecting attacks and intrusions from these unbalanced traffic can be a problem of …

One-class classification (OCC) is a classification task where the training data have only one
class. The goal is to classify input data into one seen class or other unseen classes. This …