Numerous recent studies have attempted to create efficient mechanical trading systems
through the use of machine learning approaches for stock price estimation and portfolio …

Electricity is an essential resource for human production and survival. Accurately predicting
electrical load consumption can help power supply companies make informed decisions …

With the advancement of cloud computing technologies, there is an ever-increasing demand
for the maximum utilization of cloud resources. It increases the computing power …

The Industrial Internet of Things has grown significantly in recent years. While implementing
industrial digitalization, automation, and intelligence introduced a slew of cyber risks, the …

Stock price prediction is crucial but also challenging in any trading system in stock markets.
Currently, family of recurrent neural networks (RNNs) have been widely used for stock …

Diabetes is an acute disease that happens when the pancreas cannot produce enough
insulin. It can be fatal if undiagnosed and untreated. If diabetes is revealed early enough, it …

This study proposes an ensemble deep learning approach that integrates Bagging Ridge
(BR) regression with Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks …

With the advent of the Internet of Things (IoT), it has become possible to have a variety of
data sets generated through numerous types of sensors deployed across large urban areas …

Due to the comprehensive impact of external factors (politics, economy, market, etc.) and
internal factors (organizational structure, management ability, innovation capability, etc.) …

The financial market, as a complex nonlinear dynamic system frequently influenced by
various factors, such as international investment capital, is very challenging to build trading …