[HTML][HTML] The application of machine learning techniques to improve El Niño prediction skill
We review prediction efforts of El Niño events in the tropical Pacific with particular focus on
using modern machine learning (ML) methods based on artificial neural networks. With …
using modern machine learning (ML) methods based on artificial neural networks. With …
[HTML][HTML] Ensemble deep learning models for forecasting cryptocurrency time-series
Nowadays, cryptocurrency has infiltrated almost all financial transactions; thus, it is generally
recognized as an alternative method for paying and exchanging currency. Cryptocurrency …
recognized as an alternative method for paying and exchanging currency. Cryptocurrency …
A boosted SVM classifier trained by incremental learning and decremental unlearning approach
R Kashef - Expert Systems with Applications, 2021 - Elsevier
Abstract The Support Vector Machines (SVM) classifier is a margin-based supervised
machine learning method used for categorization and classification tasks. A Linear SVM …
machine learning method used for categorization and classification tasks. A Linear SVM …
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 …
Deep Neural Network Based Ensemble learning Algorithms for the healthcare system (diagnosis of chronic diseases)
J Abdollahi, B Nouri-Moghaddam… - arXiv preprint arXiv …, 2021 - arxiv.org
learning algorithms. In this paper, we review the classification algorithms used in the health
care system (chronic diseases) and present the neural network-based Ensemble learning …
care system (chronic diseases) and present the neural network-based Ensemble learning …
A survey of evolutionary algorithms for supervised ensemble learning
HEL Cagnini, SCND Dôres, AA Freitas… - The Knowledge …, 2023 - cambridge.org
This paper presents a comprehensive review of evolutionary algorithms that learn an
ensemble of predictive models for supervised machine learning (classification and …
ensemble of predictive models for supervised machine learning (classification and …
RMSE calculation of LSTM models for predicting prices of different cryptocurrencies
Cryptocurrencies are becoming popular day by day and their use in financial applications
has also increased; hence they are recognized as a method for payments. For decades …
has also increased; hence they are recognized as a method for payments. For decades …
AdaBoost-based transfer learning method for positive and unlabelled learning problem
B Liu, C Liu, Y Xiao, L Liu, W Li, X Chen - Knowledge-Based Systems, 2022 - Elsevier
Positive and unlabelled learning (PU learning) is a problem that the training of a classifier
only utilizes labelled positive examples and unlabelled examples. Recently, PU learning …
only utilizes labelled positive examples and unlabelled examples. Recently, PU learning …
[HTML][HTML] Predictive modeling for occupational safety outcomes and days away from work analysis in mining operations
A Yedla, FD Kakhki, A Jannesari - International journal of environmental …, 2020 - mdpi.com
Mining is known to be one of the most hazardous occupations in the world. Many serious
accidents have occurred worldwide over the years in mining. Although there have been …
accidents have occurred worldwide over the years in mining. Although there have been …
Using machine-learning to predict sudden gains in treatment for major depressive disorder
Objective Sudden gains during psychotherapy have been found to consistently predict
treatment outcome but evidence on predictors of sudden gains has been equivocal. To …
treatment outcome but evidence on predictors of sudden gains has been equivocal. To …