2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)最新文献_第8页

Filter-SSLE method based on line search technology 基于行搜索技术的Filter-SSLE方法

2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965637

Xiaorong Zhu, Sai Qiao, Qian Zhang, Yi-Lin Bei, Hong-guo Zhao

引用次数: 0

Depth based Hand Gesture Recognition for Smart Teaching 基于深度的智能教学手势识别

2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965567

Tao Xu, Zhiquan Feng, Wenyin Zhang, Xiaohui Yang, Ping Yu

引用次数: 1

Research and implementation of multiscale dynamic terrain 多尺度动态地形的研究与实现

2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965645

Ping Yu, Tao Xu, Bo Zheng, Yanfeng Zhang

引用次数: 0

Chinese Herbal Medicine Classification Using Convolutional Neural Network with Multiscale Images and Data Augmentation 基于多尺度图像和数据增强的卷积神经网络中草药分类

2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965566

Tianhao Li, Fengyang Sun, R. Sun, Lin Wang, Meihui Li, Huawei Yang

引用次数: 3

Comparison of Different Classification Methods for Breast Cancer Subtypes Prediction 不同分类方法预测乳腺癌亚型的比较

2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965553

Jingru Xu, Peng Wu, Yuehui Chen, Li Zhang

{"title":"Comparison of Different Classification Methods for Breast Cancer Subtypes Prediction","authors":"Jingru Xu, Peng Wu, Yuehui Chen, Li Zhang","doi":"10.1109/SPAC46244.2018.8965553","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965553","url":null,"abstract":"Breast cancer is one of the most common cancers among women. Due to heterogeneity of cancers, breast cancer is divided into different subtypes. Different subtypes have different molecular genesis, so the corresponding target cells and treatment plans are different. Identifying the correct cancer subtypes is important for cancer diagnosis and prognosis. Breast cancer subtypes can be divided into four types: Basal, Her2, Luminal A, and Luminal B. Many machine learning approaches have been applied to cancer subtypes classification in the past few years, we present a comparison of different classifiers K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perception (MLP), and Multi-Grained Cascade Forest (gcForest) on The Cancer Genome Atlas (TCGA) databases of breast cancer. As we all know, biological data are high-dimensional and have small sample size, so before classification, we use subtype dependent feature selection method to reduce dimensionality of RNA-Seq gene expression data. Experimental results show that gcForest has a higher accuracy rate for breast cancer subtypes prediction compared with other classifiers.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122042612","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

Design and implementation of a network based intrusion detection systems 设计并实现了一个基于网络的入侵检测系统

2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965538

Bing-Yan Zhang, C. Wei, Xing-Hai Yang, Bei-Bei Song

引用次数: 2

Calibration-Free Gaze Zone Estimation Using Convolutional Neural Network 基于卷积神经网络的无标定凝视区域估计

2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965441

Xiaolei Cha, Xiaohui Yang, Zhiquan Feng, Tao Xu, Xue Fan, Jinglan Tian

引用次数: 5

Point Symbol Recognition Algorithm based on Improved Generalized Hough Transform and Nonlinear Mapping 基于改进广义霍夫变换和非线性映射的点符号识别算法

2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965548

Jianfeng Song, Zhen Zhang, Yutao Qi, Q. Miao

{"title":"Point Symbol Recognition Algorithm based on Improved Generalized Hough Transform and Nonlinear Mapping","authors":"Jianfeng Song, Zhen Zhang, Yutao Qi, Q. Miao","doi":"10.1109/SPAC46244.2018.8965548","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965548","url":null,"abstract":"The point symbol recognition in the color-scan topographic map is a difficult task in the pattern recognition of the map, which has the problems of low quality of the scanned topographic map, small symbol area, diversified shapes, superimposition with other topographic elements in the map and difficulty of separation. Therefore, many traditional recognition methods, which usually based on pre-segmentation and recognition, do not perform well in the face of such a complex condition. In this paper, we present a new method of point symbol recognition based on improved generalized Hough transform and nonlinear mapping. The proposed method contains two steps. At the first step, an improved generalized Hough transform algorithm is used to extract the target symbol from the original topographic map directly. At the second step, through the method of non-linear mapping and template matching, the preliminary identification of the region for further screening and correction. Experimental results prove that our proposed method can directly recognize and locate point symbols in color topographic maps and outperforms many traditional point symbols recognition algorithms both in both the accuracy of the location of the symbols and the accuracy of the symbol recognition.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114935919","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: 1

Breast cancer risk prediction model based on C5.0 algorithm for postmenopausal women 基于C5.0算法的绝经后妇女乳腺癌风险预测模型

2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965528

Xia Zhang, Yingming Sun

{"title":"Breast cancer risk prediction model based on C5.0 algorithm for postmenopausal women","authors":"Xia Zhang, Yingming Sun","doi":"10.1109/SPAC46244.2018.8965528","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965528","url":null,"abstract":"Breast cancer is one of the most common malignant tumors of women in the world, and it most happen in the elderly women, but in recent years the age of onset has become younger. As we know that, postmenopausal women are the groups with less research on breast cancer, and the characteristics of breast cancer are still to be explored. In this paper, based on the characteristic of 1031 postmenopausal women (⩾43 years old) breast cancer data, a breast cancer risk prediction model based on C5.0 algorithm was constructed and the model was optimized. The experimental results show that: a) Compared with machine learning methods such as neural network and support vector machine, C5.0 algorithm has better performance in constructing breast cancer risk prediction model; b) Costmatrix_C5.0 Model with cost matrix is better than adaptiveboosting_c5.0 model with Adaptive Enhancement algorithm; c) The risk of breast cancer is strongly correlated with post-menopausal hormones, age, age of menopause, history of benign breast disease and age of the first childbearing. This research is a practical application of data mining in the medical field and has certain reference value for the clinical diagnosis of breast cancer.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127045509","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: 1

Finite Time Adaptive Fuzzy Control for MIMO Nonlinear Systems Under Actuator Failures 执行器失效下MIMO非线性系统的有限时间自适应模糊控制

2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965434

Wenshun Lv, F. Wang

引用次数: 0