Machine learning of automatic hierarchical multi-label classification method for identifying metal failure mechanisms [0.03%]
基于机器学习的金属失效机制识别的自动层次化多标签分类方法
Ruitong Han,Chang-Bo Liu,Wanting Sun et al.
Ruitong Han et al.
In this study, a hierarchical multi-label classification method called HFFNet-2d is proposed for the automatic classification of scanning electron microscope (SEM) images of metal failure....The dataset of high-quality SEM images in this work is sourced from reputable materials science publications for its comprehensive coverage of various failure modes and its suitability for training and validating the hierarchical multi-label classification model.
Tackling over-smoothing in multi-label image classification using graphical convolution neural network [0.03%]
基于图卷积神经网络的多标签图像分类方法研究过平滑问题
Vikas Chauhan,Aruna Tiwari,Boppudi Venkata et al.
Vikas Chauhan et al.
The importance of the graphical convolution network in multi-label classification has grown in recent years due to its label embedding representation capabilities. The graphical convolution network is able to capture the label dependencies using the correlation between labels.
Qi He,Sophia Bano,Danail Stoyanov et al.
Qi He et al.
The balanced sampling strategy is implemented via resampling and mix-up techniques, fine-grained classification is enabled through multi-granularity feature learning, and multi-label classification is achieved using hierarchical label joint learning.
Prompt-guided consistency learning for multi-label classification with incomplete labels [0.03%]
基于提示的多标签分类不完整标签一致性学习方法
Shouwen Wang,Qian Wan,Zihan Zhang et al.
Shouwen Wang et al.
Addressing insufficient supervision and improving model generalization are essential for multi-label classification with incomplete annotations, i.e., partial and single positive labels. Recent studies incorporate pseudo-labels to provide additional supervision and enhance model generalization.
Interpretable multi-label classification model for predicting post-anesthesia care unit complications: a prospective cohort study [0.03%]
一种用于预测术后麻醉并发症的可解释多标签分类模型：前瞻性队列研究
Guoting Ma,Wenjun Yan,Zunqiang Zhao et al.
Guoting Ma et al.
This study aimed to develop and validate an interpretable multi-label classification model to predict PACU complications concurrently....A multi-label classification model was developed on the basis of 16 key features, and a Markov network was embedded to quantify and analyze the association network among these complications....Conclusion: The integration of a multi-label classification model with interpretable methods has significant advantages in simultaneously predicting PACU complications, identifying the risk factors for specific complications, optimizing postoperative resource allocation, and improving
Hybrid attention-based deep learning for multi-label ophthalmic disease detection on fundus images [0.03%]
基于混合注意力深度学习的视网膜图像多标签眼疾检测方法
Rabiya Hanfi,Harsh Mathur,Ritu Shrivastava
Rabiya Hanfi
Traditional deep learning models often lack accuracy, interpretability, and efficiency for multi-label classification tasks in ophthalmology.
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.
We evaluated their performance using four different multi-label classification models.
Publication Type Tagging using Transformer Models and Multi-Label Classification [0.03%]
基于Transformer模型和多标签分类的出版物类型标注方法研究
Joe D Menke,Halil Kilicoglu,Neil R Smalheiser
Joe D Menke
Specifically, we trained PubMedBERT-based models using a multi-label classification approach, and explored undersampling, feature verbalization, and contrastive learning to improve model performance.
SSC-Net: A multi-task joint learning network for tongue image segmentation and multi-label classification [0.03%]
基于多任务联合学习的舌象图像分割和多重分类网络(SSC-Net)
Xiaopeng Sha,Zheng Guan,Ying Wang et al.
Xiaopeng Sha et al.
Finally, a fine-grained classification module is employed for multi-label classification on multiple tongue characteristics.
Prediction of Chemically Modified Antimicrobial Peptides and Their Sub-functional Activities Using Hybrid Features [0.03%]
基于混合特征的化学修饰抗菌肽及其亚功能活性预测
Yujie Yao,Daijun Zhang,Henghui Fan et al.
Yujie Yao et al.
The second layer of our model, designated as iCMAMP-2L, employed a multi-label classification approach to predict the sub-functional activities of cmAMPs, with a specific focus on the dipeptide composition-based features.
耗时 0.07946 秒，为您在
48206918
条记录里面共找到 413 篇文章 [XML]