Results 31 to 40 of about 505,761 (263)

Fault Diagnosis of Semi-Supervised Electromechanical Transmission Systems Under Imbalanced Unlabeled Sample Class Information Screening

Entropy
In the health monitoring of electromechanical transmission systems, the collected state data typically consist of only a minimal amount of labeled data, with a vast majority remaining unlabeled.
Chaoge Wang, Pengpeng Jia, Xinyu Tian, Xiaojing Tang, Xiong Hu, Hongkun Li   +5 more
doaj   +1 more source

A Novel Intelligent Fault Diagnosis Method for Rolling Bearings Based on Wasserstein Generative Adversarial Network and Convolutional Neural Network under Unbalanced Dataset

Sensors, 2021
Rolling bearings are widely used in industrial manufacturing, and ensuring their stable and effective fault detection is a core requirement in the manufacturing process.
Hongtao Tang, Shengbo Gao, Lei Wang, Xixing Li, Bing Li, Shibao Pang   +5 more
doaj   +1 more source

Accurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks

Sensors
Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for arrhythmia diagnosis. The subtle differences in characteristics among various types of arrhythmias, coupled with class imbalance issues in datasets ...
Suzhao Bi, Rongjian Lu, Qiang Xu, Peiwen Zhang   +3 more
doaj   +1 more source

Distinguishing Bladder Cancer from Cystitis Patients Using Deep Learning

Mathematics, 2023
Urinary tract cancers are considered life-threatening conditions worldwide, and Bladder Cancer is one of the most malignant urinary tract tumors, with an estimated number of more than 1.3 million cases worldwide each year.
Dong-Her Shih, Pai-Ling Shih, Ting-Wei Wu, Chen-Xuan Lee, Ming-Hung Shih   +4 more
doaj   +1 more source

Transfer learning for class imbalance problems with inadequate data [PDF]

Knowledge and Information Systems, 2015
A fundamental problem in data mining is to effectively build robust classifiers in the presence of skewed data distributions. Class imbalance classifiers are trained specifically for skewed distribution datasets. Existing methods assume an ample supply of training examples as a fundamental prerequisite for constructing an effective classifier. However,
Samir Al-Stouhi, Chandan K. Reddy
openaire   +2 more sources

A New Body Weight Lifelog Outliers Generation Method: Reflecting Characteristics of Body Weight Data

Applied Sciences, 2022
Lifelogs are generated in our daily lives and contain useful information for health monitoring. Nowadays, one can easily obtain various lifelogs from a wearable device such as a smartwatch. These lifelogs could include noise and outliers. In general, the
Jiyong Kim, Minseo Park
doaj   +1 more source

SYNAuG: Exploiting synthetic data for data imbalance problems

Pattern Recognition Letters
Data imbalance in training data often leads to biased predictions from trained models, which in turn causes ethical and social issues. A straightforward solution is to carefully curate training data, but given the enormous scale of modern neural networks, this is prohibitively labor-intensive and thus impractical.
Moon Ye-Bin, Nam Hyeon-Woo, Won-Seok Choi 0001, Nayeong Kim, Suha Kwak, Tae-Hyun Oh   +5 more
openaire   +2 more sources

Organ‐specific redox imbalances in spinal muscular atrophy mice are partially rescued by SMN antisense oligonucleotides

FEBS Letters, EarlyView.
We identified a systemic, progressive loss of protein S‐glutathionylation—detected by nonreducing western blotting—alongside dysregulation of glutathione‐cycle enzymes in both neuronal and peripheral tissues of Taiwanese SMA mice. These alterations were partially rescued by SMN antisense oligonucleotide therapy, revealing persistent redox imbalance as ...
Sofia Vrettou, Brunhilde Wirth
wiley   +1 more source

Quantification of Data Imbalance

Expert Systems
ABSTRACTIn this article, we propose a novel approach to quantify the imbalance in data, addressing a significant gap in the field of regression analysis. Real‐world datasets often exhibit an inherent imbalance in their data distribution, which adversely affects learning algorithms such as those used in neural networks.
Jelke Wibbeke, Sebastian Rohjans, Andreas Rauh   +2 more
openaire   +1 more source

Machine Learning-Based Alzheimer’s Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation

Diagnostics
Background/Objectives: This study presents a comparative analysis of the multistage diagnosis of Alzheimer’s disease (AD), including mild cognitive impairment (MCI), utilizing two distinct types of biomarkers: blood gene expression and clinical biomarker
Manash Sarma, Subarna Chatterjee
doaj   +1 more source