A systematic review on imbalanced learning methods in intelligent fault diagnosis
The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …
achievements and significantly benefited industry practices. However, most methods are …
[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …
belonging to one class is lower than the other. Ensemble learning combines multiple models …
Attack classification of imbalanced intrusion data for IoT network using ensemble-learning-based deep neural network
With the increase in popularity of Internet of Things (IoT) and the rise in interconnected
devices, the need to foster effective security mechanism to handle vulnerabilities and risks in …
devices, the need to foster effective security mechanism to handle vulnerabilities and risks in …
Iterative training sample augmentation for enhancing land cover change detection performance with deep learning neural network
Labeled samples are important in achieving land cover change detection (LCCD) tasks via
deep learning techniques with remote sensing images. However, labeling samples for …
deep learning techniques with remote sensing images. However, labeling samples for …
Rolling bearing fault diagnosis based on 2D time-frequency images and data augmentation technique
W Fu, X Jiang, B Li, C Tan, B Chen… - … Science and Technology, 2023 - iopscience.iop.org
It confronts great difficulty to apply the traditional rolling bearing fault diagnosis methods to
adaptively extract features conducive to fault diagnosis under complex operating conditions …
adaptively extract features conducive to fault diagnosis under complex operating conditions …
Augmented data driven self-attention deep learning method for imbalanced fault diagnosis of the HVAC chiller
C Shen, H Zhang, S Meng, C Li - Engineering Applications of Artificial …, 2023 - Elsevier
The chiller fault diagnosis is of great significance to maintain the normal operation of the
HVAC system and indoor comfort. Due to the difficulty in collecting the chiller's fault data, we …
HVAC system and indoor comfort. Due to the difficulty in collecting the chiller's fault data, we …
Effective class-imbalance learning based on SMOTE and convolutional neural networks
Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from
achieving satisfactory results. ID is the occurrence of a situation where the quantity of the …
achieving satisfactory results. ID is the occurrence of a situation where the quantity of the …
A deep learning methodology for predicting cybersecurity attacks on the internet of things
OA Alkhudaydi, M Krichen, AD Alghamdi - Information, 2023 - mdpi.com
With the increasing severity and frequency of cyberattacks, the rapid expansion of smart
objects intensifies cybersecurity threats. The vast communication traffic data between …
objects intensifies cybersecurity threats. The vast communication traffic data between …
[HTML][HTML] Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer …
Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past
decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of …
decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of …
A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for research
The combination of class imbalance and overlap is currently one of the most challenging
issues in machine learning. While seminal work focused on establishing class overlap as a …
issues in machine learning. While seminal work focused on establishing class overlap as a …