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2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)最新文献

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Reliable and practical fall prediction using artificial neural network 基于人工神经网络的坠落预测可靠实用
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393052
William Engel, W. Ding
{"title":"Reliable and practical fall prediction using artificial neural network","authors":"William Engel, W. Ding","doi":"10.1109/FSKD.2017.8393052","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393052","url":null,"abstract":"The growing elder population has inspired remarkable research in the prevention of fall injuries. A reliable technique to predict fall incidence, along with a corresponding mobile phone app, is proposed in this paper. The technique combines the benefits of traditional medical history based paradigm and non-historical paradigm. The app analyzes single leg motion to predict if the carrying individual is about to fall with a desirably practical alert time, not too long like in the medical history based paradigm, not too short like in the non-historical paradigm. Furthermore, this approach utilizes leg motion instead of torso motion to gain considerable longer alert time. This fall prediction technique will be a perfect fit into a real time automated system for fall prevention.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116253153","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: 5
Flower classification based on single petal image and machine learning methods 基于单瓣图像和机器学习方法的花卉分类
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393382
Siyuan Lu, Zhihai Lu, Xianqing Chen, Shuihua Wang, Yudong Zhang
{"title":"Flower classification based on single petal image and machine learning methods","authors":"Siyuan Lu, Zhihai Lu, Xianqing Chen, Shuihua Wang, Yudong Zhang","doi":"10.1109/FSKD.2017.8393382","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393382","url":null,"abstract":"This research presented a novel automatic flower classification system based on computer vision and machine learning techniques. First, we obtained in total 157 petal images of three alike categories using a digital camera. After pre-processing, we extracted color features and wavelet entropies from the petal images. Then, principle component analysis was utilized for feature reduction. Finally, four different classifiers, Support Vector Machine, Weighted k Nearest Neighbors, Kernel based Extreme Learning Machine, and Decision Tree, were trained to recognize the categories of the petals. 5-fold cross validation was employed to evaluate the out-of-sample performance of the classifiers. The experimental results showed that Weighted k-Nearest Neighbors performed the best among all four classifiers with an overall accuracy of 99.4%. The proposed approach is efficient in identifying flower categories in comparison with state-of-the-art methods.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115403739","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: 5
Attribute weighted fuzzy clustering algorithm based on mutual information 基于互信息的属性加权模糊聚类算法
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393018
Y. Cao, He Lin, Biao Liu
{"title":"Attribute weighted fuzzy clustering algorithm based on mutual information","authors":"Y. Cao, He Lin, Biao Liu","doi":"10.1109/FSKD.2017.8393018","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393018","url":null,"abstract":"It is studied by applying the mutual information which is used to assess the contribution of each attribute that has the different important degrees to the classification in the fuzzy clustering algorithm, then the attribute weighted fuzzy clustering algorithm based on mutual information is proposed. By using the mutual information to quantify the contribution of each attribute to the classification, the attributes are weighted and introduced into the fuzzy C mean algorithm. For incomplete data sets, the missing attribute is also introduced as a target object to be optimized and as a part of the iterative to be optimization. Finally, an example verifies the applicability of the algorithm in dealing with incomplete data sets and incomplete data sets, and analyzes the effect of each attribute value loss on clustering results in incomplete data sets.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"506 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123427314","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
Prediction of distribution network malfunction based on meteorological factors 基于气象因素的配电网故障预测
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8392904
Mingzhu Zhang, Yan Chen, Ruoxi Liu, Xuejie Cheng, Yi Jiao, Jiakui Zhao, Ouyang Hong
{"title":"Prediction of distribution network malfunction based on meteorological factors","authors":"Mingzhu Zhang, Yan Chen, Ruoxi Liu, Xuejie Cheng, Yi Jiao, Jiakui Zhao, Ouyang Hong","doi":"10.1109/FSKD.2017.8392904","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8392904","url":null,"abstract":"Distribution network malfunction often causes serious economic losses and social negative impact. If we can effectively predict the numbers of distribution network malfunction, it would provide reliable data basis for the promptly maintenance and power repair. In this paper, three kinds of analysis algorithms, stepwise regression analysis, zero-inflated Poisson regression and support vector regression (SVR), are used to fit the numbers of malfunction. We utilized the lightning data and meteorological factors as independent variables, and utilized the external malfunctions and natural malfunctions as the dependent variables to establish the prediction models. At the end of this paper, the accuracy of these three methods is discussed. The relative root mean square error(R-RMSE) of each prediction method is calculated. We found that the external malfunctions using support SVR to obtain the best results, and natural malfunctions are better with the zero-inflated Poisson regression model.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123256106","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
Variational hidden conditional random fields with beta processes 具有β过程的变分隐藏条件随机场
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393055
Chen Luo, Shiliang Sun, Jing Zhao
{"title":"Variational hidden conditional random fields with beta processes","authors":"Chen Luo, Shiliang Sun, Jing Zhao","doi":"10.1109/FSKD.2017.8393055","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393055","url":null,"abstract":"Hidden conditional random fields (HCRFs) are an effective method for sequential classification. It extends the conditional random fields (CRFs) by introducing latent variables to represent the hidden states, which helps to learn the hidden structures in the sequential data. In order to enhance the flexibility of the HCRF, Dirichlet processes (DPs) are employed as priors of the state transition probabilities, which allows the model to have countable infinite hidden states. Besides DPs, Beta processes (BPs) are another kinds of prior models for Bayesian nonparametric modeling, which are more suitable for latent feature models. In this paper, we propose a novel Bayesian nonparametric version of the HCRF referred as BP-HCRF, which takes the advantages of the BPs on modeling hidden states. In the BP-HCRF, BPs are employed as priors for the state indicator variables for each sequence, and the modeled sequences can have different state spaces with infinite hidden states. We develop a variational inference approach for the BP-HCRF using the stick-breaking construction of BPs. We conduct experiments on synthetic dataset to demonstrate the effectiveness of our proposed model.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121482461","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 method for discovering data patterns through constructing feature networks 一种通过构造特征网络发现数据模式的方法
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393143
Xiaomeng Li, Chengli Zhao, Qiangjuan Huang, Xiaojie Wang, Dong-yun Yi
{"title":"A method for discovering data patterns through constructing feature networks","authors":"Xiaomeng Li, Chengli Zhao, Qiangjuan Huang, Xiaojie Wang, Dong-yun Yi","doi":"10.1109/FSKD.2017.8393143","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393143","url":null,"abstract":"With the arrival of the big data era, data is playing a prominent role increasingly. At present, data is stored in a variety of ways, which relational data is one of the most important. This paper aims to combine the method of data mining and complex network to analyze and utilize relational data, and a method of constructing feature network is proposed to discover some interesting patterns hidden in the massive relational data. First, a method is introduced to transform relational data to complex networks, in which the features of data is defined as nodes of networks and the correlation of two features is taken as edge weight of networks. Second, some measures of feature networks is calculated to find some data patterns. Finally, the above method is applied in two medical data sets, and the analyzed result shows that different kind of data (healthy people and patients) is significantly different in their topology of feature networks. The method is expected to provide an efficient way to discover data patterns and classifying data.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122461551","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
Research on the key technology of big data service in university library 高校图书馆大数据服务关键技术研究
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393181
C. Ye
{"title":"Research on the key technology of big data service in university library","authors":"C. Ye","doi":"10.1109/FSKD.2017.8393181","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393181","url":null,"abstract":"With the development of information technology of university library, the mass data of the university library has the basic characteristics of Big Data. However, the current situation of the university library is the lack of distributed storage and computing model for massive data, the lack of capacity to handl the diverse data sources, including the structured, semi-structured and unstructured data, the lack of a simple, flexible application model of big data service. In order to solve the problems in the service innovation of University Libraries in China, such as the problem of distributed storage and computation of massive data, the distributed management of diverse data sources, the simple and flexible application of big data services, this paper analyzes the research contents of big data processing, Hadoop ecosystem and the demand for big data services in University Libraries, and presents a technology framework for big data service in University Libraries based Hadoop. The framework includes the distributed storage and parallel computing model of mass data, the distributed management model of diverse data sources and the model of diversified service application for university libraries. This framework takes full account of the service innovation change of University Library under the environment of big data, such as data storage and calculation, data management and service applications et al. It can solve the key technical problems of big data service of University Library in a certain extent.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125323697","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: 6
A novel approach for paper recommendation based on rough-fuzzy set theory 基于粗糙模糊集理论的论文推荐新方法
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8392976
Chu Wang, Daling Wang, Shi Feng, Yifei Zhang, Hongchen Liu
{"title":"A novel approach for paper recommendation based on rough-fuzzy set theory","authors":"Chu Wang, Daling Wang, Shi Feng, Yifei Zhang, Hongchen Liu","doi":"10.1109/FSKD.2017.8392976","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8392976","url":null,"abstract":"Nowadays, recommending relevant and valuable academic papers has drawn great attentions from academic researchers. However, most of time paper recommendation still relies on the keywords given by users as input. Previous studies have shown that this kind of method are not effective because they did not take semantic similarity into consideration. To address the problem, in this paper we conduct similarity analysis based on synoptic content including the titles and abstracts between the papers referred by users and the ones in paper dataset, and then recommend scientific papers to the users. We use the TF-IDF to pick out the important words of the titles and abstracts in papers, and apply rough-fuzzy set method as well as WordNet to calculate the similarity values of candidate papers. After ranking the papers based on the values, we can get the paper recommendation result. The experiments on the public available dataset have demonstrated the superiority of our proposed method over other baselines.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125333593","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
A novel strategy of initializing the population size for ant colony optimization algorithms in TSP 一种新的TSP蚁群优化算法种群规模初始化策略
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393166
Fanzhen Liu, Jiaqi Zhong, Chen Liu, Chao Gao, Xianghua Li
{"title":"A novel strategy of initializing the population size for ant colony optimization algorithms in TSP","authors":"Fanzhen Liu, Jiaqi Zhong, Chen Liu, Chao Gao, Xianghua Li","doi":"10.1109/FSKD.2017.8393166","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393166","url":null,"abstract":"The ant colony optimization (ACO) algorithm belonging to swarm intelligence methods has been used to solve quantities of optimization problems. Among those problem, the travelling salesman problem (TSP) is a very essential application of ACO algorithm, which displays the great ability of ACO algorithm to find short paths through graphs. However, the existing ant colony optimization algorithms still perform a low efficiency in solving TSP within a limited time. In order to overcome these shortcomings, a hypothesis about initializing the population size for ACO algorithms is put forward, based on the analysis of the relationship among the initial number of ant, the average optimal solution and the computational cost. Furthermore, some experiments are implemented in six datasets, and the results prove that the hypothesis is reasonable and reveal that the initial population size is relevant to the number of cities in a dataset. Based on the hypothesis, this paper proposes a novel strategy of initializing the number of ants for ACO algorithms in TSP, so that the relative high-quality optimal solutions can be obtained within a short time.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125382518","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: 6
Prediction model of the unemployment rate for nanyang in henan province based on BP neural network 基于BP神经网络的河南省南阳市失业率预测模型
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8392903
Yiyuan Cheng, Tao Hai, Yangbing Zheng, Baolei Li
{"title":"Prediction model of the unemployment rate for nanyang in henan province based on BP neural network","authors":"Yiyuan Cheng, Tao Hai, Yangbing Zheng, Baolei Li","doi":"10.1109/FSKD.2017.8392903","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8392903","url":null,"abstract":"A prediction model for the unemployment rate of Nanyang in Henan province based on BP (Back Propagation) neural network has been built in this paper. The training samples are from the data in Nanyang statistical yearbook, and the prediction model has been simulated by the MATLAB software. It's concluded that it is fully feasible to adopt the BP neural network to predict unemployment rate from the results, which provides some employment guidance for early warning.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125006096","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
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