2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)最新文献_第5页

A hybrid predicting model for displacement of multifactor-triggered landslides 多因素诱发滑坡位移的混合预测模型

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778500

Honggao Deng, Shanwen Guan, Yuanfa Ji, Li Zhou, Xiaonan Luo

{"title":"A hybrid predicting model for displacement of multifactor-triggered landslides","authors":"Honggao Deng, Shanwen Guan, Yuanfa Ji, Li Zhou, Xiaonan Luo","doi":"10.1109/ICACI.2019.8778500","DOIUrl":"https://doi.org/10.1109/ICACI.2019.8778500","url":null,"abstract":"This paper presents a new hybrid model for land-slide distance prediction. In the model, the cumulative displacement are divided into three parts: the trend term, the period term, and the random noise obtained by the wavelet domain de-nosing method and Hodrick-Prescott (HP) filter. The trend term controlled by the geological conditions is generated using the double exponential smoothing (DES). The period term is predicted by the extreme learning machine (ELM) model, and the dynamic multi-swarm particle swarm optimizer (DMS-PSO) algorithm is applied to obtain optimal parameters of ELM. Case study involving real data collected from the Baishuihe landslide in China is used to verify that the hybrid approach enhances the ability to calculate the period term. Inputs of the proposed model include the period factors extracted from the seasonal triggers and displacement values which enhance excellently the robustness of the prediction model of the period displacement. Extensive experiments are carried out on the Baishuihe landslide dates. Comparing with the predictions obtained by the real original displacement, our model is efficient for predicting the landslide distance of multiple factors induced landslide.","PeriodicalId":213368,"journal":{"name":"2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122713029","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

Acoustic Signal Positioning and Calibration with IMU in NLOS Environment NLOS环境下IMU声信号定位与标定

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778614

Hucheng Wang, Xiao-peng Luo, Y. Zhong, Rushi Lan, Zhi Wang

引用次数: 5

Identifying Tumor in Whole-Slide Images of Breast Cancer Using Transfer Learning and Adaptive Sampling 利用迁移学习和自适应采样在乳腺癌全片图像中识别肿瘤

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778616

Chenchen Wu, Jun Ruan, Guanglu Ye, Jingfan Zhou, Simin He, Jianlian Wang, Zhikui Zhu, Junqiu Yue, Yanggeling Zhang

{"title":"Identifying Tumor in Whole-Slide Images of Breast Cancer Using Transfer Learning and Adaptive Sampling","authors":"Chenchen Wu, Jun Ruan, Guanglu Ye, Jingfan Zhou, Simin He, Jianlian Wang, Zhikui Zhu, Junqiu Yue, Yanggeling Zhang","doi":"10.1109/ICACI.2019.8778616","DOIUrl":"https://doi.org/10.1109/ICACI.2019.8778616","url":null,"abstract":"Deep learning is widely used in medical applications in view of the excellent performance it achieved in image processing. Early methods of diagnosis on whole slide images (WSIs) is usually based on dense sampling which is time-consuming and requires a lot of memory to handle it. In this paper, we propose an adaptive sampling method that classify WSI of breast biopsies into two categories (cancer area and normal area) complied by transfer learning. This method involves: i) an adaptive sampling method based on probability gradient map. ii) a classifier which contain feature extraction part and classifier part to divide WSI into two categories. We tried nine different transfer learning models based on TensorFlow and Keras platform and apply the model to execute classification in WSI under three different magnifications (x5, x20, x40). The results showed that (1) the transfer learning combined with SVM or NN is enough to detect the cancer area which achieved an average test accuracy of 97.07% under x20 magnification, and (2) the adaptive sampling method is an effective strategy to deal with WSI with good performance (achieve the Dice coefficient of 80%) and far fewer samples (less than 5% of samples when use uniform sampling method).","PeriodicalId":213368,"journal":{"name":"2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133386738","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: 4

A Multi-objective Cuckoo search Algorithm Based on Decomposition 基于分解的多目标布谷鸟搜索算法

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778450

Liang Chen, Wenyan Gan, Hongwei Li, Xin Xu, Lin Cao, Yufang Feng

引用次数: 4

An Improved U-Net Method for Sequence Images Segmentation 一种改进的U-Net序列图像分割方法

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778625

P. Wen, Menglong Sun, Yongqing Lei

{"title":"An Improved U-Net Method for Sequence Images Segmentation","authors":"P. Wen, Menglong Sun, Yongqing Lei","doi":"10.1109/ICACI.2019.8778625","DOIUrl":"https://doi.org/10.1109/ICACI.2019.8778625","url":null,"abstract":"In multi-view three-dimensional reconstruction of objects, the accuracy of the image segmentation plays a key role in the accuracy of the model. The traditional Convolutional Neural Network segmentation method often leads to significant feature losses in the target’s edges. It also requires a lot of data for training. Therefore, this paper proposes an improved U-Net method for sequence image segmentation. To begin with, the U-Net structure is used as the basis to solve the problem of feature position information loss and to improve the precision of the edges of segmented objects. Next, multi-scale convolution modules are added on the basis of U-Net structure to increase the network depth and improve feature extraction capability. Then the batch normalization layer is added to solve the problem of vanishing gradient and to accelerate the speed of converged network. Finally, a heat-map channel is added in the input data to prevent errors of classification in similar areas. The experimental results showed that this method ranks higher than the classical U-Net on key indicators, Fl-score and IOU. It can effectively improve the segmentation accuracy, yielding results similar to those of manual segmentation.","PeriodicalId":213368,"journal":{"name":"2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134143281","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: 3

A Risk Prediction Model for Type 2 Diabetes Based on Weighted Feature Selection of Random Forest and XGBoost Ensemble Classifier 基于随机森林加权特征选择和XGBoost集成分类器的2型糖尿病风险预测模型

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778622

Zhongxian Xu, Zhiliang Wang

{"title":"A Risk Prediction Model for Type 2 Diabetes Based on Weighted Feature Selection of Random Forest and XGBoost Ensemble Classifier","authors":"Zhongxian Xu, Zhiliang Wang","doi":"10.1109/ICACI.2019.8778622","DOIUrl":"https://doi.org/10.1109/ICACI.2019.8778622","url":null,"abstract":"Type 2 diabetes mellitus is a severe chronic disease threatening human health and has a high incidence worldwide. People need to use effective prediction model to diagnose and prevent diabetes in time. At present, data mining technology has become an increasingly important technology with classification capability in the field of medical diagnosis. This paper proposes a risk prediction model for type 2 diabetes based on ensemble learning method. In the proposed model, the weighted feature selection algorithm based on random forest (RF-WFS) is used for optimal feature selection, and extreme gradient boosting (XGBoost) classifier. The effectiveness of the method was validated by comparing the various performance metrics and the results of different contrast experiments. Additionally, we get a better prediction accuracy using the method than using the other classification algorithms (C4.5, Naive Bayes, AdaBoost, Random Forest). The validation results at UCI Pima Indian diabetes dataset shows that the model has better accuracy and classification performance than other research results mentioned in the literature. As a result, it has been proven that the model would be effective for the diagnosis of diabetes at the initial stage.","PeriodicalId":213368,"journal":{"name":"2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123215221","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: 37

Network Reliability Analysis Based on ADD under Capacity and Delay Constraints 容量和时延约束下基于ADD的网络可靠性分析

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778617

Fengying Li, Huihui Liu, Weiqiang Jiang, Rongsheng Dong

引用次数: 0

Research on Generation Algorithm of Complex Event Processing Rules Based on Time Series 基于时间序列的复杂事件处理规则生成算法研究

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778586

Yue Li, Tong Zhang, Chenfei Song

引用次数: 1

Multi-residuals Network and Region Constraints Based Face-image Denoising 基于多残差网络和区域约束的人脸图像去噪

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778608

Haiqing Chen, Fei Chen

引用次数: 0

Inferring Missing Attributes of Users in Large-Scale Social networks 大规模社交网络中用户缺失属性的推断

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778611

Huadeng Wang, Songhua Xu, Lihui Liu, Xiaonan Luo

引用次数: 1