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1

FuseNet：lightweight image recognition network for mobile applications

FuseNet：应用于移动端的轻量型图像识别网络

Tian Xinchi

Wang Yagang

Yin Zhong

convolutional neural network(CNN)

image recognition

mobile device

lightweight

Transformer

FuseNet

No. 1, 2023 : 288-293,298 doi:10.19734/j.issn.1001-3695.2022.05.0230

2

Interpretable method of just-in-time defect prediction model for mobile App

移动App即时缺陷预测模型的可解释性方法

Hu Xinyu

Chen Xiang

Xia Hongling

Gu Yafeng

just-in-time defect prediction

mobile App

interpretability

hyper parameter optimization

No. 7, 2022 : 2104-2108 doi:10.19734/j.issn.1001-3695.2021.12.0679

3

Comprehensive research on multiple feature sets and multiple label sets for mobile network traffic classification

移动互联网流量分类的多特征集合和多类别标签研究

Huang Yi

Liu Zhen

Wang Ruoyu

Chen Jietong

mobile App traffic

machine learning algorithms

classification method

clustering method

data stream

No. 11, 2020 : 3353-3358 doi:10.19734/j.issn.1001-3695.2019.05.0255

4

Research and application of multi-feature sets based ensemble clustering method for mobile App traffic

面向移动App流量的多特征集合集成聚类方法研究与应用

Wu Zhimin

Liu Zhen

Wang Ruoyu

Chen Jietong

mobile App traffic

traffic statistics features

ensemble clustering

traffic identification

No. 10, 2019 : 3101-3106 doi:10.19734/j.issn.1001-3695.2018.04.0250

5

Mobile App traffic identification based on visual perception features

基于视觉感知特征的手机应用流量识别方法

Li Ding

Zhu Yuefei

Lin Wei

mobile App

traffic identification

convolutional autoencoder

latent feature

No. 4, 2019 : 1153-1157 doi:10.19734/j.issn.1001-3695.2017.11.1001

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Searchmobile Appreturned 5 results

FuseNet：lightweight image recognition network for mobile applications

Interpretable method of just-in-time defect prediction model for mobile App

Comprehensive research on multiple feature sets and multiple label sets for mobile network traffic classification

Research and application of multi-feature sets based ensemble clustering method for mobile App traffic

Mobile App traffic identification based on visual perception features