Artificial intelligence and machine learning approaches in composting process: a review
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 …
process have increased in recent years. Machine learning (ML) has focused on process …
A multi-agent reinforcement learning framework for optimizing financial trading strategies based on timesnet
Y Huang, C Zhou, K Cui, X Lu - Expert Systems with Applications, 2024 - Elsevier
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 …
learning to learn profitable trading strategies from financial market data. However, a single …
Modelling and optimization of sewage sludge composting using biomass ash via deep neural network and genetic algorithm
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 …
to model both linear and non-linear chaotic relationships in co-composting of dewatered …
A novel intuitionistic fuzzy time series prediction model with cascaded structure for financial time series
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 …
have a wide range of applications in the public and private sectors. This study presents a …
[HTML][HTML] A hybrid neural network and optimization algorithm for forecasting and trend detection of Forex market indices
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 …
network can be obtained by stacking several AEs. This paper presents a novel hybrid …
[HTML][HTML] Forecasting stock closing prices with an application to airline company data
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 …
view. We explore how model selection via the least absolute shrinkage and selection …
A multi-scaling reinforcement learning trading system based on multi-scaling convolutional neural networks
Y Huang, K Cui, Y Song, Z Chen - Mathematics, 2023 - mdpi.com
Advancements in machine learning have led to an increased interest in applying deep
reinforcement learning techniques to investment decision-making problems. Despite this …
reinforcement learning techniques to investment decision-making problems. Despite this …
Enhancing portfolio management using artificial intelligence: literature review
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 …
many years. The key goal has always been to balance risk and reward by optimally …
[HTML][HTML] A novel distance-based moving average model for improvement in the predictive accuracy of financial time series
U Ejder, SA Özel - Borsa Istanbul Review, 2024 - Elsevier
Time-series forecasting is essential for system analysis. Many traditional studies have paid
attention to individual stock-oriented solutions and disregarded general approaches on …
attention to individual stock-oriented solutions and disregarded general approaches on …
Air pollution prediction based on discrete wavelets and deep learning
Y Shu, C Ding, L Tao, C Hu, Z Tie - Sustainability, 2023 - mdpi.com
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 …
health. An accurate prediction of air pollution can provide a credible foundation for …