A new approach to COVID-19 data mining: A deep spatial-temporal prediction model based on tree structure for traffic revitalization index

一种基于树结构的深度时空预测模型的COVID-19数据挖掘新方法以交通复兴指数为例

Zhiqiang Lv,Xiaotong Wang,Zesheng Cheng et al.

Data & knowledge engineering. 2023 Jul:146:102193. DOI:10.1016/j.datak.2023.102193

An automated multi-web platform voting framework to predict misleading information proliferated during COVID-19 outbreak using ensemble method

用于预测COVID-19爆发期间传播的误导信息的基于集成方法的自动多网络平台投票框架

Deepika Varshney,Dinesh Kumar Vishwakarma

Data & knowledge engineering. 2023 Jan:143:102103. DOI:10.1016/j.datak.2022.102103

Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalization index

新冠疫情中的深度学习：城市交通振兴指数的深度模型

Zhiqiang Lv,Jianbo Li,Chuanhao Dong et al.

Data & knowledge engineering. 2021 Sep:135:101912. DOI:10.1016/j.datak.2021.101912

Leveraging output term co-occurrence frequencies and latent associations in predicting medical subject headings

Ramakanth Kavuluru,Yuan Lu

Data & knowledge engineering. 2014 Nov;94(B):189-201. DOI:10.1016/j.datak.2014.09.002

Interaction mining and skill-dependent recommendations for multi-objective team composition

Christoph Dorn,Florian Skopik,Daniel Schall et al.

Data & knowledge engineering. 2011 Oct;70(10):866-891. DOI:10.1016/j.datak.2011.06.004