EBBA-detector: An effective detector for defect detection in solar panel EL images with unbalanced data [0.03%]
基于不平衡数据的光伏组件EL图像缺陷检测方法EBBA-Detector
Yixing Zhang,Ziyan Mo,Zhuan Xin et al.
Yixing Zhang et al.
Solar panel defect detection, a crucial quality control task in the manufacturing process, often faces challenges such as varying defect sizes, severe image background interference, and imbalanced data sample distribution. To address these ...
Distribution Learning Based on Evolutionary Algorithm-Assisted Deep Neural Networks for Imbalanced Image Classification [0.03%]
基于进化算法辅助的深度神经网络的分布学习在不平衡图像分类中的应用
Yudi Zhao,Kuangrong Hao,Chaochen Gu et al.
Yudi Zhao et al.
Imbalanced image classification faces critical challenges in balancing the quality and diversity of synthetic minority samples. This article proposes the improved estimation distribution algorithm-based latent feature distribution evolution...
PhysioDimClassifier-imbalance data classifier model for IoMT-based remote patient monitoring systems [0.03%]
基于IoMT的远程患者监测系统的不平衡数据分类模型 PhysioDimClassifier
Sayyed Johar,G R Manjula
Sayyed Johar
Remote patient monitoring systems (RPMS) using the Internet of Medical Things (IoMT) continuously collect and exchange periodic sensor-observations through communication modules. However, these data streams often contain relevant and irrele...
Odor classification: Exploring feature performance and imbalanced data learning techniques [0.03%]
气味分类：探索特征性能和不平衡数据学习方法
Durgesh Ameta,Surendra Kumar,Rishav Mishra et al.
Durgesh Ameta et al.
This research delves into olfaction, a sensory modality that remains complex and inadequately understood. We aim to fill in two gaps in recent studies that attempted to use machine learning and deep learning approaches to predict human smel...
Comparison of Deep Transfer Learning Against Contrastive Learning in Industrial Quality Applications for Heavily Unbalanced Data Scenarios When Data Augmentation Is Limited [0.03%]
在数据增强受限的情况下，深度迁移学习与对比学习在工业质量应用中的不平衡数据场景下的比较研究
Amir Farmanesh,Raúl G Sanchis,Joaquín Ordieres-Meré
Amir Farmanesh
AI-oriented quality inspection in manufacturing often faces highly imbalanced data, as defective products are rare, and there are limited possibilities for data augmentation. This paper presents a systematic comparison between Deep Transfer...
Early Detection of Fetal Health Conditions Using Machine Learning for Classifying Imbalanced Cardiotocographic Data [0.03%]
基于机器学习的分类算法在胎心监护不平衡数据中的应用及胎儿健康状况的早期检测研究
Irem Nazli,Ertugrul Korbeko,Seyma Dogru et al.
Irem Nazli et al.
Background: Cardiotocography (CTG) is widely used in obstetrics to monitor fetal heart rate and uterine contractions. It helps detect early signs of fetal distress. However, manual interpretation of CTG can be time-consuming and may vary be...
Research on equipment fault diagnosis model based on gan and inverse PINN: Solutions for data imbalance and rare faults [0.03%]
基于生成对抗网络和逆向PINN的装备故障诊断模型研究：不平衡数据及稀有故障的解决方案
Jian Deng,Zheng Cheng,Aiming Gu et al.
Jian Deng et al.
In the field of medical imaging equipment, fault diagnosis plays a vital role in guaranteeing stable operation and prolonging service life. Traditional diagnostic approaches, though, are confronted with issues like intricate fault modes, as...
Intelligent approach to detecting online fraudulent trading with solution for imbalanced data in fintech forensics [0.03%]
金融科技取证中用于处理不平衡数据的智能欺诈交易检测方法
Saad M Darwish,Amr Ibrahim Salama,Adel A Elzoghabi
Saad M Darwish
Detecting online fraudulent trading in the realm of Fintech presents several challenges, primarily due to the dynamic nature of financial markets and the evolving tactics of fraudsters. Traditional machine learning algorithms trained on unb...
Correction: An explainable ensemble approach for advanced brain tumor classification applying Dual-GAN mechanism and feature extraction techniques over highly imbalanced data [0.03%]
纠正：使用双GAN机制和特征提取技术的可解释集成方法在高度不平衡数据下进行高级脑肿瘤分类
PLOS One Staff
PLOS One Staff
[This corrects the article DOI: 10.1371/journal.pone.0310748.]. Copyright: © 2025 The PLOS One Staff. This is an open access article distributed under t...
Published Erratum
PloS one. 2025 May 20;20(5):e0324970. DOI:10.1371/journal.pone.0324970 2025
Learning Self-Growth Maps for Fast and Accurate Imbalanced Streaming Data Clustering [0.03%]
自增长图快速准确地聚类不平衡数据流
Yiqun Zhang,Sen Feng,Pengkai Wang et al.
Yiqun Zhang et al.
Streaming data clustering is a popular research topic in data mining and machine learning. Since streaming data is usually analyzed in data chunks, it is more susceptible to encountering the dynamic cluster imbalance issue. That is, the imb...
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