Mean–variance portfolio optimization using machine learning-based stock price prediction
W Chen, H Zhang, MK Mehlawat, L Jia - Applied Soft Computing, 2021 - Elsevier
The success of portfolio construction depends primarily on the future performance of stock
markets. Recent developments in machine learning have brought significant opportunities to …
markets. Recent developments in machine learning have brought significant opportunities to …
[HTML][HTML] Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area
Forest fire disaster is currently the subject of intense research worldwide. The development
of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous …
of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous …
A spatially explicit deep learning neural network model for the prediction of landslide susceptibility
With the increasing threat of recurring landslides, susceptibility maps are expected to play a
bigger role in promoting our understanding of future landslides and their magnitude. This …
bigger role in promoting our understanding of future landslides and their magnitude. This …
Shallow landslide susceptibility mapping: A comparison between logistic model tree, logistic regression, naïve bayes tree, artificial neural network, and support vector …
Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices,
and can cause social upheaval and loss of life. As a result, many scientists study the …
and can cause social upheaval and loss of life. As a result, many scientists study the …
Prediction success of machine learning methods for flash flood susceptibility mapping in the Tafresh watershed, Iran
Floods are some of the most destructive and catastrophic disasters worldwide. Development
of management plans needs a deep understanding of the likelihood and magnitude of future …
of management plans needs a deep understanding of the likelihood and magnitude of future …
Soft computing ensemble models based on logistic regression for groundwater potential mapping
Groundwater potential maps are one of the most important tools for the management of
groundwater storage resources. In this study, we proposed four ensemble soft computing …
groundwater storage resources. In this study, we proposed four ensemble soft computing …
Performance evaluation of machine learning methods for forest fire modeling and prediction
Predicting and mapping fire susceptibility is a top research priority in fire-prone forests
worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB) …
worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB) …
Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping
Using multiple ensemble learning techniques for improving the predictive accuracy of
landslide models is an active research area. In this study, we combined a radial basis …
landslide models is an active research area. In this study, we combined a radial basis …
Landslide susceptibility mapping using machine learning algorithms and remote sensing data in a tropical environment
We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an
ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands …
ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands …
Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility
Forests are important dynamic systems which are widely affected by fire worldwide. Due to
the complexity and non-linearity of the forest fire problem, employing hybrid evolutionary …
the complexity and non-linearity of the forest fire problem, employing hybrid evolutionary …