Machine learning for predicting epileptic seizures using EEG signals: A review
With the advancement in artificial intelligence (AI) and machine learning (ML) techniques,
researchers are striving towards employing these techniques for advancing clinical practice …
researchers are striving towards employing these techniques for advancing clinical practice …
Metaheuristic design of feedforward neural networks: A review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a
key interest among the researchers and practitioners of multiple disciplines. The FNN …
key interest among the researchers and practitioners of multiple disciplines. The FNN …
Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation
Mobile banking (m-banking) has emerged dynamically over the years due to consumers'
increased use of mobile technologies, their ever-growing lifestyle choices and also the …
increased use of mobile technologies, their ever-growing lifestyle choices and also the …
A systematic study of the class imbalance problem in convolutional neural networks
In this study, we systematically investigate the impact of class imbalance on classification
performance of convolutional neural networks (CNNs) and compare frequently used …
performance of convolutional neural networks (CNNs) and compare frequently used …
Deep learning applications in medical image analysis
The tremendous success of machine learning algorithms at image recognition tasks in
recent years intersects with a time of dramatically increased use of electronic medical …
recent years intersects with a time of dramatically increased use of electronic medical …
Dynamically weighted balanced loss: class imbalanced learning and confidence calibration of deep neural networks
KRM Fernando, CP Tsokos - IEEE Transactions on Neural …, 2021 - ieeexplore.ieee.org
Imbalanced class distribution is an inherent problem in many real-world classification tasks
where the minority class is the class of interest. Many conventional statistical and machine …
where the minority class is the class of interest. Many conventional statistical and machine …
A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework
Class imbalance poses new challenges when it comes to classifying data streams. Many
algorithms recently proposed in the literature tackle this problem using a variety of data …
algorithms recently proposed in the literature tackle this problem using a variety of data …
Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
Machine learning models for medical image analysis often suffer from poor performance on
important subsets of a population that are not identified during training or testing. For …
important subsets of a population that are not identified during training or testing. For …
Clustering-based undersampling in class-imbalanced data
WC Lin, CF Tsai, YH Hu, JS Jhang - Information Sciences, 2017 - Elsevier
Class imbalance is often a problem in various real-world data sets, where one class (ie the
minority class) contains a small number of data points and the other (ie the majority class) …
minority class) contains a small number of data points and the other (ie the majority class) …
Imbalanced deep learning by minority class incremental rectification
Model learning from class imbalanced training data is a long-standing and significant
challenge for machine learning. In particular, existing deep learning methods consider …
challenge for machine learning. In particular, existing deep learning methods consider …