Studies on developing strategies to predict the stability and performance of the composting
process have increased in recent years. Machine learning (ML) has focused on process …

An increasing number of studies have shown the effectiveness of using deep reinforcement
learning to learn profitable trading strategies from financial market data. However, a single …

In this study, the use of Deep Cascade Forward Neural Network (DCFNN) was investigated
to model both linear and non-linear chaotic relationships in co-composting of dewatered …

Financial time series prediction problems, for decision-makers, are always crucial as they
have a wide range of applications in the public and private sectors. This study presents a …

An Autoencoder (AE) is an independent feature extractor from data samples and a deep
network can be obtained by stacking several AEs. This paper presents a novel hybrid …

Forecasting stock market movements is a challenging task from the practitioners' point of
view. We explore how model selection via the least absolute shrinkage and selection …

Advancements in machine learning have led to an increased interest in applying deep
reinforcement learning techniques to investment decision-making problems. Despite this …

Building an investment portfolio is a problem that numerous researchers have addressed for
many years. The key goal has always been to balance risk and reward by optimally …

Time-series forecasting is essential for system analysis. Many traditional studies have paid
attention to individual stock-oriented solutions and disregarded general approaches on …

Air pollution directly affects people's life and work and is an important factor affecting public
health. An accurate prediction of air pollution can provide a credible foundation for …