2021 11th International Conference on Information Science and Technology (ICIST)最新文献_第9页

Analysis and forecast of taxi transport capacity in railway station: A case of Beijing West Railway Station 火车站出租车运力分析与预测——以北京西站为例

2021 11th International Conference on Information Science and Technology (ICIST) Pub Date : 2021-05-21 DOI: 10.1109/ICIST52614.2021.9440603

Haitao Yu, Jiao Gui, Zilong Zhao, Randong Xiao, Yong Du

引用次数: 0

Designing Event-Triggered Prescribed-Time Consensus and Containment Control of Multi-Agent Systems Under Directed Graphs 有向图下事件触发的多智能体系统约定时间一致性设计与约束控制

2021 11th International Conference on Information Science and Technology (ICIST) Pub Date : 2021-05-21 DOI: 10.1109/ICIST52614.2021.9440585

Xiaotong Zhou, Liguo Tan, Zybin Eugene Yuryevich, Shen-min Song

引用次数: 4

Adaptive Output Feedback Boundary Stabilization for First-order Hyperbolic Distributed Parameter Systems 一阶双曲型分布参数系统的自适应输出反馈边界镇定

2021 11th International Conference on Information Science and Technology (ICIST) Pub Date : 2021-05-21 DOI: 10.1109/ICIST52614.2021.9440565

R. Guo, Kangkang Sun, Jianbin Qiu

引用次数: 0

Robust H∞ Sliding Mode Load Frequency Control of Multi-area Power System 多区域电力系统鲁棒H∞滑模负荷频率控制

2021 11th International Conference on Information Science and Technology (ICIST) Pub Date : 2021-05-21 DOI: 10.1109/icist52614.2021.9440570

Xinghua Liu, Siwei Qiao

引用次数: 0

Differentially Private Decision Tree Based on Pearson’s Correlation Coefficient 基于Pearson相关系数的差分私有决策树

2021 11th International Conference on Information Science and Technology (ICIST) Pub Date : 2021-05-21 DOI: 10.1109/ICIST52614.2021.9440604

Qi Yan, Jinyan Wang, Songfeng Liu, De Li

{"title":"Differentially Private Decision Tree Based on Pearson’s Correlation Coefficient","authors":"Qi Yan, Jinyan Wang, Songfeng Liu, De Li","doi":"10.1109/ICIST52614.2021.9440604","DOIUrl":"https://doi.org/10.1109/ICIST52614.2021.9440604","url":null,"abstract":"As one of popular machine learning algorithms, decision trees can be applied to both classification and regression tasks. Many decision tree models are trained and used by some companies and researchers on data obtained from users for business decision. However, the use of decision trees may pose a potential risk to privacy when training data contains sensitive personal information. In this paper, we proposed an effective differentially private decision tree algorithm based on Pearson’s correlation coefficient, called Diff-PCCDT. The exponential mechanism is applied to select the best splitting attribute with the absolute value of Pearson’s correlation coefficient as quality function at every intermediate node. The laplace noise is added into the true class count of every leaf node to ensure that the decision tree does not leak privacy during classification. Furthermore, a parallel algorithm of Diff-PCCDT over Map-Reduce, called DiffMR-PCCDT, is proposed for big data scenario. The experimental results demonstrate that the Diff-PCCDT algorithm can effectively train differentially private decision tree and its parallel implementation DiffMR-PCCDT can deal with the big data classification problems.","PeriodicalId":371599,"journal":{"name":"2021 11th International Conference on Information Science and Technology (ICIST)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130366426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Passenger Flow Prediction Method for Bus Lines Based on Multiple Stepwise Regression Analysis 基于多元逐步回归分析的公交线路客流预测方法

2021 11th International Conference on Information Science and Technology (ICIST) Pub Date : 2021-05-21 DOI: 10.1109/ICIST52614.2021.9440559

Randong Xiao, Jiajia Zhu, Zilong Zhao, Haitao Yu, Yong Du

引用次数: 1

Formation Maintenance of Large-Scale Unmanned Aerial Vehicles Under Wireless Channels 无线信道下大型无人机编队维护

2021 11th International Conference on Information Science and Technology (ICIST) Pub Date : 2021-05-21 DOI: 10.1109/ICIST52614.2021.9440572

Lan Ma, Yirui Cong, Xiangke Wang, Huiming Li

引用次数: 0

Improved Mushroom Reproduciton Optimization for Robot Path Planning 机器人路径规划的改进蘑菇繁殖优化

2021 11th International Conference on Information Science and Technology (ICIST) Pub Date : 2021-05-21 DOI: 10.1109/ICIST52614.2021.9440568

Dongli Wang, Dashan Li, Zhou Yan, Jinzhen Mu

引用次数: 0

Adaptive Memory Event-triggered Control for Multi-agent Systems under External Disturbance 外部干扰下多智能体系统的自适应记忆事件触发控制

2021 11th International Conference on Information Science and Technology (ICIST) Pub Date : 2021-05-21 DOI: 10.1109/ICIST52614.2021.9440652

Dongyu Zhang, D. Ding, Zeming Zhao, Xianggui Guo

引用次数: 0

LTS-NET: Lung Tissue Segmentation from CT Images using Fully Convolutional Neural Network LTS-NET:基于全卷积神经网络的CT图像肺组织分割

2021 11th International Conference on Information Science and Technology (ICIST) Pub Date : 2021-05-21 DOI: 10.1109/ICIST52614.2021.9440594

Lingyu Zhou, Xiuyuan Xu, Kai Zhou, Jixiang Guo

{"title":"LTS-NET: Lung Tissue Segmentation from CT Images using Fully Convolutional Neural Network","authors":"Lingyu Zhou, Xiuyuan Xu, Kai Zhou, Jixiang Guo","doi":"10.1109/ICIST52614.2021.9440594","DOIUrl":"https://doi.org/10.1109/ICIST52614.2021.9440594","url":null,"abstract":"Lung tissue segmentation is vital in computer-assisted diagnosis, and many studies have been devoted to this field. However, there are still challenges because of redundant information in CT images and the deformation of organ tissue. In this study, we constructed a fully convolution neural network-based model called LTS-Net to segment lung tissue from CT images. First, to improve efficiency and guarantee segmentation accuracy, the network model uses three downward max-pooling layers and three up-sampling layers. The number of convolution channels increases exponentially with each down-sampling step, thereby resulting in fast feature extraction. Furthermore, each convolutional layer follows an ReLU and a batch normalization layer to maintain robustness. Finally, we analyzed the influence of the receptive field size for lung tissue segmentation to provide a trade-off between accuracy and efficiency. To evaluate the performance of LTS-Net, we constructed a CT image dataset at West China Hospital of Sichuan University. The results demonstrated the constructed model's performance: LTS-Net obtained a new state-of-the-art result of 0.992 Dice coefficient on the dataset.","PeriodicalId":371599,"journal":{"name":"2021 11th International Conference on Information Science and Technology (ICIST)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132906362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2