Depth prediction of urban flood under different rainfall return periods based on deep learning and data warehouse
Z Wu, Y Zhou, H Wang, Z Jiang - Science of The Total Environment, 2020 - Elsevier
With the global climate change and the rapid urbanization process, there is an increase in
the risk of urban floods. Therefore, undertaking risk studies of urban floods, especially the …
the risk of urban floods. Therefore, undertaking risk studies of urban floods, especially the …
A comparative study of demand forecasting models for a multi-channel retail company: a novel hybrid machine learning approach
A Mitra, A Jain, A Kishore, P Kumar - Operations research forum, 2022 - Springer
Demand forecasting has been a major concern of operational strategy to manage the
inventory and optimize the customer satisfaction level. The researchers have proposed …
inventory and optimize the customer satisfaction level. The researchers have proposed …
A novel dynamic ensemble selection classifier for an imbalanced data set: An application for credit risk assessment
Credit risk assessment is usually regarded as an imbalanced classification task solved by
static ensemble classifiers. However, the dynamic ensemble selection (DES) strategy that …
static ensemble classifiers. However, the dynamic ensemble selection (DES) strategy that …
Identification of geographical origins of Radix Paeoniae Alba using hyperspectral imaging with deep learning-based fusion approaches
Abstract The Radix Paeoniae Alba (Baishao) is a traditional Chinese medicine (TCM) with
numerous clinical and nutritional benefits. Rapid and accurate identification of the …
numerous clinical and nutritional benefits. Rapid and accurate identification of the …
Short-term rockburst risk prediction using ensemble learning methods
Short-term rockburst risk prediction plays a crucial role in ensuring the safety of workers.
However, it is a challenging task in deep rock engineering as it depends on many factors …
However, it is a challenging task in deep rock engineering as it depends on many factors …
CUS-heterogeneous ensemble-based financial distress prediction for imbalanced dataset with ensemble feature selection
X Du, W Li, S Ruan, L Li - Applied Soft Computing, 2020 - Elsevier
Due to the global financial crisis occurred in 2008, with a large amount of companies
troubling in financial distress, the machine learning-based prediction of this dilemma has …
troubling in financial distress, the machine learning-based prediction of this dilemma has …
Artificial intelligence and policing of financial crime: a legal analysis of the state of the field
H Harris - Financial Technology and the Law: Combating …, 2022 - Springer
This article maps the existing use of Artificial Intelligence (AI) in law enforcement, with a
focus on crimes perpetrated on financial markets—including market manipulation and …
focus on crimes perpetrated on financial markets—including market manipulation and …
A new method of diesel fuel brands identification: SMOTE oversampling combined with XGBoost ensemble learning
S Wang, S Liu, J Zhang, X Che, Y Yuan, Z Wang… - Fuel, 2020 - Elsevier
Using proper diesel brand is the key to ensure the normal operation of diesel engine. It is
even more important to identify the brands of diesel oil effectively. This paper presented a …
even more important to identify the brands of diesel oil effectively. This paper presented a …
Machine learning-based wind pressure prediction of low-rise non-isolated buildings
This paper proposes a novel machine learning-based wind pressure prediction model (ML-
WPP) for low-rise non-isolated buildings. ML-WPP combines a gradient boosting decision …
WPP) for low-rise non-isolated buildings. ML-WPP combines a gradient boosting decision …
A new approach for evaluating node importance in complex networks via deep learning methods
The evaluation of node importance is a critical research topic in network science, widely
applied in social networks, transport systems, and computer networks. Prior works …
applied in social networks, transport systems, and computer networks. Prior works …