Predicting long-term stock index prices is a challenging and debatable task. Most of the
studies focus on predicting next-day stock prices. However, those are not useful to long-term …

Stock price forecasting is a challenging task due to the complexity of financial markets and
the high volatility of stocks. Because of the strong nonlinear representation ability of neural …

Combined cogeneration systems are employed to recover waste heat from the sCO 2 cycle
in nuclear power generation systems. However, dealing with their multidimensional and …

Vigorously developing new energy vehicles (NEVs) is a new strategic goal of the automotive
industry nowadays. Stocks are important channels for NEV enterprises to raise funds, and …

The implementation of statistical techniques in on-line surveillance of financial markets has
been frequently studied more recently. As a novel approach, statistical control charts which …

Many urban road networks today are experiencing increasing congestion that threatens not
only transport efficiency but also living environment. To solve these problems, providing …

In this study, we propose a predictive model TabLSTM that combines machine learning
methods such as TabNet and Long Short-Term Memory Neural Network (LSTM) with a …

Multi-view learning (MVL) is a promising field that seeks to make the most of information
shared across different views, ultimately improving generalization performance. Numerous …

Universum data sets, a collection of data sets that do not belong to any specific class in a
classification problem, give previous information about data in the mathematical problem …

Pollution caused by environmental problems has aggravated the problem of resource
scarcity, and the destruction of the ecological environment by mankind has shown serious …