Yuyang Liu,Haoyu Liu,Runze Wu et al.
Yuyang Liu et al.
Crowdsourcing delivers responses that are asynchronous and incomplete, making offline aggregators that assume complete response sets impractical. Prior online methods often either require per-step completeness or repeatedly reload historica...
Attention mechanism and mixup data augmentation for classification of COVID-19 Computed Tomography images [0.03%]
基于注意力机制和混合数据增强的COVID-19 CT图像分类方法
Özgür Özdemir,Elena Battini Sönmez
Özgür Özdemir
The Coronavirus disease is quickly spreading all over the world and the emergency situation is still out of control. Latest achievements of deep learning algorithms suggest the use of deep Convolutional Neural Network to implement a compute...
DeSa COVID-19: Deep salient COVID-19 image-based quality assessment [0.03%]
基于显著的COVID-19图像的质量评估 Desmond-COVID-19
Risnandar
Risnandar
This study offers an advanced method to evaluate the coronavirus disease 2019 (COVID-19) image quality. The salient COVID-19 image map is incorporated with the deep convolutional neural network (DCNN), namely DeSa COVID-19, which exerts the...
iVaccine-Deep: Prediction of COVID-19 mRNA vaccine degradation using deep learning [0.03%]
基于深度学习预测COVID-19 mRNA疫苗降解的iVaccine-Deep模型
Amgad Muneer,Suliman Mohamed Fati,Nur Arifin Akbar et al.
Amgad Muneer et al.
Messenger RNA (mRNA) has emerged as a critical global technology that requires global joint efforts from different entities to develop a COVID-19 vaccine. However, the chemical properties of RNA pose a challenge in utilizing mRNA as a vacci...
Lexical sorting centrality to distinguish spreading abilities of nodes in complex networks under the Susceptible-Infectious-Recovered (SIR) model [0.03%]
基于SIR模型复杂网络中节点传播能力区分的词法排序中心性研究
Aybike Şimşek
Aybike Şimşek
Epidemic modeling in complex networks is a hot research topic in recent years. The spreading of a virus (such as SARS-CoV-2) in a community, spreading computer viruses in communication networks, or spreading gossip on a social network is th...
Pruning-based oversampling technique with smoothed bootstrap resampling for imbalanced clinical dataset of Covid-19 [0.03%]
基于修剪的过采样技术结合平滑自助重采样法处理不平衡的Covid-19临床数据集
Prasetyo Wibowo,Chastine Fatichah
Prasetyo Wibowo
The Coronavirus Disease (COVID-19) was declared a pandemic disease by the World Health Organization (WHO), and it has not ended so far. Since the infection rate of the COVID-19 increases, the computational approach is needed to predict pati...
Analysis of COVID-19 severity from the perspective of coagulation index using evolutionary machine learning with enhanced brain storm optimization [0.03%]
基于改进脑暴优化的进化机器学习COVID-19严重程度分析研究
Beibei Shi,Hua Ye,Ali Asghar Heidari et al.
Beibei Shi et al.
Coronavirus 2019 (COVID-19) is an extreme acute respiratory syndrome. Early diagnosis and accurate assessment of COVID-19 are not available, resulting in ineffective therapeutic therapy. This study designs an effective intelligence framewor...
Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features [0.03%]
基于深度CNN和DWT优化特征混合的随机森林分类器在胸部X光片中检测Covid-19
Rafid Mostafiz,Mohammad Shorif Uddin,Nur-A- Alam et al.
Rafid Mostafiz et al.
Chest X-ray image contains sufficient information that finds wide-spread applications in diverse disease diagnosis and decision making to assist the medical experts. This paper has proposed an intelligent approach to detect Covid-19 from th...
State-of-art review of information diffusion models and their impact on social network vulnerabilities [0.03%]
信息扩散模型及其对社交网络脆弱性影响的研究进展综述
Abdul Razaque,Syed Rizvi,Meer Jaro Khan et al.
Abdul Razaque et al.
With the development of information society and network technology, people increasingly depend on information found on the Internet. At the same time, the models of information diffusion on the Internet are changing as well. However, these ...
CGUFS: A clustering-guided unsupervised feature selection algorithm for gene expression data [0.03%]
一种用于基因表达数据的聚类引导无监督特征选择算法
Zhaozhao Xu,Fangyuan Yang,Hong Wang et al.
Zhaozhao Xu et al.
Aim: Gene expression data is typically high dimensional with a limited number of samples and contain many features that are unrelated to the disease of interest. Existing unsupervised feature selection algorithms primaril...