Results 101 to 110 of about 141,868 (281)

Imbalanced Data Parameter Optimization of Convolutional Neural Networks Based on Analysis of Variance

Applied Sciences
Classifying imbalanced data is important due to the significant practical value of accurately categorizing minority class samples, garnering considerable interest in many scientific domains.
Ruiao Zou, Nan Wang
doaj   +1 more source

From Lab to Landscape: Environmental Biohybrid Robotics for Ecological Futures

Advanced Robotics Research, EarlyView.
This Perspective explores environmental biohybrid robotics, integrating living tissues, microorganisms, and insects for operation in real‐world ecosystems. It traces the leap from laboratory experiments to forests, wetlands, and urban environments and discusses key challenges, development pathways, and opportunities for ecological monitoring and ...
Miriam Filippi
wiley   +1 more source

Dual generative adversarial networks based on regression and neighbor characteristics.

PLoS ONE
Imbalanced data is a problem in that the number of samples in different categories or target value ranges varies greatly. Data imbalance imposes excellent challenges to machine learning and pattern recognition.
Weinan Jia, Ming Lu, Qing Shen, Chunzhi Tian, Xuyang Zheng   +4 more
doaj   +1 more source

Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset. [PDF]

Sci Rep, 2023
Schaudt D, von Schwerin R, Hafner A, Riedel P, Reichert M, von Schwerin M, Beer M, Kloth C.   +7 more
europepmc   +1 more source

Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification

, 2020
Generative Adversarial Networks (GANs) have been used in many different applications to generate realistic synthetic data. We introduce a novel GAN with Autoencoder (GAN-AE) architecture to generate synthetic samples for variable length, multi-feature ...
Ger, Stephanie, Klabjan, Diego
core  

Class-Imbalanced Learning on Graphs: A Survey

ACM Computing Surveys
Rapid advancement in machine learning is increasing the demand for effective graph data analysis. However, real-world graph data often exhibits class imbalance, leading to poor performance of standard machine learning models on underrepresented classes. To address this, C lass-
Yihong Ma, Yijun Tian 0001, Nuno Moniz, Nitesh V. Chawla   +3 more
openaire   +2 more sources

Temporal and Cell‐Specific Regulation of Synaptic Homeostasis by the Chromatin Remodeler Chd1

Advanced Science, EarlyView.
Chd1, the Drosophila homologue of mammalian CHD2 ‐ a gene linked to autism, epilepsy, and intellectual disability, is required for synaptic homeostatic plasticity. Chd1 in glia is necessary for the rapid induction of synaptic homeostasis, whereas Chd1 in motoneurons, muscle, and glia is critical for long‐term maintenance.
Danielle T. Morency, Tao Cui, Yimei Cai, Chloe Lok, Rachel E. Nokku, Ruoxian Huang, Grace L. Chu, Yumeng Xie, Saleem W. Abu‐Tayeh, Kaikai He, Chengjie Qiu, Junyi Wang, Paxton M. Paganelli, Ting Wang, Gabrielle Williams, Sreejith Nair, Huadong Pei, Dion K. Dickman, Stefano Vicini, Tingting Wang   +19 more
wiley   +1 more source

CL-PMI: A Precursor MicroRNA Identification Method Based on Convolutional and Long Short-Term Memory Networks

Frontiers in Genetics, 2019
MicroRNAs (miRNAs) are the major class of gene-regulating molecules that bind mRNAs. They function mainly as translational repressors in mammals. Therefore, how to identify miRNAs is one of the most important problems in medical treatment. Many known pre-
Huiqing Wang, Yue Ma, Chunlin Dong, Chun Li, Jingjing Wang, Dan Liu   +5 more
doaj   +1 more source

Causal Prediction of TP53 Variant Pathogenicity Using a Perturbation‐Informed Protein Language Model

Advanced Science, EarlyView.
A TP53‐specific predictor, CaVepP53, is developed by fine‐tuning ESMC on experimentally validated variants, quantifying pathogenicity via Euclidean distances. It outperforms general‐purpose models and extends to five cancer genes, enabling interpretable variant classification for precision medicine.
Huiying Chen, Yang Zhao, Boqiang Hu, Wuke Wang, Minfang Song, Annabeth Xinyu Zhao, Xiangyang Li, Gefei Wang, Yanfen Wang, Weiyan Zheng, Xinpeng Zhang, Xia Lin, Yanbin Yin, Xingxu Huang, Jinfang Zheng, Tingbo Liang   +15 more
wiley   +1 more source

A New Multi-Sensor Stream Data Augmentation Method for Imbalanced Learning in Complex Manufacturing Process. [PDF]

Sensors (Basel), 2022
Xu D, Zhang Z, Shi J.
europepmc   +1 more source