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2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)最新文献

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Moving object grouping rule mining based on accumulated spatio-temporal data 基于累积时空数据的运动对象分组规则挖掘
2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167060
Guodong Yang, Xiang Wang, Zhitao Huang
{"title":"Moving object grouping rule mining based on accumulated spatio-temporal data","authors":"Guodong Yang, Xiang Wang, Zhitao Huang","doi":"10.1109/CIAPP.2017.8167060","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167060","url":null,"abstract":"With the advance of mobile electronic devices and the development of positioning technology, a large volume of spatio-temopral data are collected in the form of desultorily data streams, which contain a lot of potential information. In this study, we focus on discovering the composition relationships between observation moving objects in a long period. Such research can be widely used in military and civilian areas, including recommendation systems, wildlife research, military monitoring and battlefield situation awareness. The composition relationships of moving objects can be called as moving object grouping rule. In this paper, we proposed an improved traveling companion discovery method based on Nearest neighbor of time to obtained the object transactions in short time and used the incremental association rule mining (ARM) method to discovering the grouping rules of moving objects in long-term.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122321625","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
Path planning of tourist scenic area based on variable dimension particle swarm optimization 基于变维粒子群优化的旅游景区路径规划
2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167203
Yunyang Liu, Haoyu Wang, Quanyu Wang, Junjie Wang
{"title":"Path planning of tourist scenic area based on variable dimension particle swarm optimization","authors":"Yunyang Liu, Haoyu Wang, Quanyu Wang, Junjie Wang","doi":"10.1109/CIAPP.2017.8167203","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167203","url":null,"abstract":"Tourists sometimes can only experience part of the scenic spots due to time limitation. Thus, these spots should be chosen and a path needs to be found for the tourists. However, the Particle Swarm Optimization (PSO) is on a fixed dimension and it cannot meet the above requirements. Therefore, in this paper we propose the Variable Dimension PSO (VDPSO) to plan the path. The traditional updating methods of the velocity vector and the position vector are not suitable for the discrete domain in PSO, so they are replaced by the crossover and the mutation operations. Our algorithm will redistribute the particles from low dimensions to high dimensions, which can enhance the ability to search in the high dimensional particle space. Experiments on the Summer Palace data demonstrate effectiveness of the proposed algorithm.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126146806","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
Interest point localization based on edge detection according to gestalt laws 基于格式塔法边缘检测的兴趣点定位
2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167237
Patryk Najgebauer, L. Rutkowski, R. Scherer
{"title":"Interest point localization based on edge detection according to gestalt laws","authors":"Patryk Najgebauer, L. Rutkowski, R. Scherer","doi":"10.1109/CIAPP.2017.8167237","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167237","url":null,"abstract":"The paper proposes a method for grouping fragments of contours of objects in the images of microscopic parasitological examinations, characterized by high transparency of analyzed objects. The method is based on a graphical representation of the edges in vector form, allowing to substantially reduce the required calculations. The method uses simple vector operations to determine stroke parameters describing the degree of curvature and closure of the examined contour and the direction where the remaining part of the contour should be located. Compared with other methods of detecting elliptic objects, the proposed method allows for the association of objects with rather irregular and distorted shapes occurring in parasitic images.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123953236","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
Application of improved immune algorithm in logistics distribution center location 改进免疫算法在物流配送中心选址中的应用
2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167193
Huan Ping, Chuyi Song, Nan Wang, Jing-qing Jiang
{"title":"Application of improved immune algorithm in logistics distribution center location","authors":"Huan Ping, Chuyi Song, Nan Wang, Jing-qing Jiang","doi":"10.1109/CIAPP.2017.8167193","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167193","url":null,"abstract":"Logistics distribution center location problem is a hot topic nowadays. We choose a medium-sized city group to study in this paper. The improved artificial immune algorithm based on similarity vector distance selection is used for the logistics distribution center location problem. The algorithm uses the threshold value to restrict concentration calculation of two antibodies from two aspects which are the structure and the fitness. It can effectively improve the convergence speed and solve the problem.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130092208","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 safety risk early warning method of the apron based on RS-IPSO-SVM 基于RS-IPSO-SVM的停机坪安全风险预警方法研究
2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167278
Junyong Liu, Shu Gao, Fan Luo, Liangchen Chen
{"title":"Research on the safety risk early warning method of the apron based on RS-IPSO-SVM","authors":"Junyong Liu, Shu Gao, Fan Luo, Liangchen Chen","doi":"10.1109/CIAPP.2017.8167278","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167278","url":null,"abstract":"For better resolving the safety risk early warning of the apron effectively, the attribute reduction algorithm based on Rough Set is used to simplify the set as the warning index set of the apron is too large. The improved Particle Swarm Optimization (PSO) algorithm is used to optimize the parameters of Support Vector Machine. Combined with the Rough Set and SVM which is optimized by the improved Particle Swarm Optimization algorithm, a safety risk early warning method based on RS-IPSO-SVM is designed. Finally, with the data of the apron, the experimental results show that the method has higher warning accuracy, and has practical value to the safety risk early warning of the apron.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130194334","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 new method to construct the software vulnerability model 一种构建软件漏洞模型的新方法
2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167212
Xiang Li, Jinfu Chen, Zhechao Lin, Lin Zhang, Zibin Wang, Minmin Zhou, Wanggen Xie
{"title":"A new method to construct the software vulnerability model","authors":"Xiang Li, Jinfu Chen, Zhechao Lin, Lin Zhang, Zibin Wang, Minmin Zhou, Wanggen Xie","doi":"10.1109/CIAPP.2017.8167212","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167212","url":null,"abstract":"With the development of information technology, software plays an increasingly important role in the process of social development. However, at the same time, the number of software vulnerabilities is growing, posing a threat to national security and social stability. Therefore, some scholars and research institutions are paying their attention to the study of software vulnerability. In this paper, we propose a new vulnerability model construction method by considering the vulnerability causes and characteristics. Firstly, the causes and characteristics of software vulnerability are analyzed, and a formal vulnerability model is also established. Based on the causes and characteristics of software vulnerability, we establish the vulnerability model using the extended chemical abstract machine and deduce the software vulnerability through a formal method. We verified the effectiveness and efficiency of the proposed model using software vulnerability datasets. In addition, a prototype system is also designed and implemented. Experimental results show that the proposed model is more effective than other methods in the detection of software vulnerabilities.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130381015","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
BetterChoice: A migraine drug recommendation system based on Neo4J BetterChoice:一个基于Neo4J的偏头痛药物推荐系统
2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167244
Benjamin Stark, Constanze Knahl, Mert Aydin, Mohammad Samarah, Karim O. Elish
{"title":"BetterChoice: A migraine drug recommendation system based on Neo4J","authors":"Benjamin Stark, Constanze Knahl, Mert Aydin, Mohammad Samarah, Karim O. Elish","doi":"10.1109/CIAPP.2017.8167244","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167244","url":null,"abstract":"Migraine is a common disease throughout the world. Not only does it affect the life of people tremendously, but it also leads to high costs, e.g. due to inability to work or various required drug-taking cycles for finding the best drug for a patient. Solving the latter aspect could help to improve the life of patients and decrease the impact of the other consequences. Therefore, in this paper, we present an approach for a drug recommendation system based on the highly scalable native graph database Neo4J. The presented system uses simulated patient data to help physicians gain more transparency about which drug fits a migraine patient best considering her individual features. Our evaluation shows that the proposed system works as intended. This means that only drugs with highest relevance scores and no interactions with the patient's diseases, drugs or pregnancy are recommended.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121494092","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: 19
Regression model for appraisal of real estate using recurrent neural network and boosting tree 基于递归神经网络和提升树的房地产评估回归模型
2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167209
Junchi Bin, Shi Yuan Tang, Yihao Liu, G. Wang, Bryan Gardiner, Zheng Liu, Eric Li
{"title":"Regression model for appraisal of real estate using recurrent neural network and boosting tree","authors":"Junchi Bin, Shi Yuan Tang, Yihao Liu, G. Wang, Bryan Gardiner, Zheng Liu, Eric Li","doi":"10.1109/CIAPP.2017.8167209","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167209","url":null,"abstract":"Automated valuation model (AVM) is a mathematical program to estimate the market value of real estates based on the analysis of locations, neighborhood characteristics, and relevant property characteristics. The most common AVMs em-ployed by the appraisal industry are based on multiple regression analysis. Other analytic tools such as statistical learning and fuzzy algorithms have become more popular because of the increasing capability of collecting a high volume of data and the advancement of machine learning. The new analytic model thus becomes possible to build a more sophisticated model to exploit the information embedded in the collected data. In this work, we proposed a boosting tree model facilitated with a Recurrent Neural Network (RNN) to forecast the average price of an area. The experimental results indicate that our model outperforms the existing models adopted in the appraisal industry.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122798430","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: 21
Rapid detecting SSC and TAC of peaches based on NIR spectroscopy 基于近红外光谱快速检测桃子中SSC和TAC
2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167229
Lingling Li, Yuan Wu, Lian Li, Bingqing Huang
{"title":"Rapid detecting SSC and TAC of peaches based on NIR spectroscopy","authors":"Lingling Li, Yuan Wu, Lian Li, Bingqing Huang","doi":"10.1109/CIAPP.2017.8167229","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167229","url":null,"abstract":"Peach is one of the most important commodities in the global fresh product market. With the development of people's living standard, consumers pay more attention to the internal quality of fruits than the appearance quality of fruits. The requirement of nondestructive analysis could be satisfied by near infrared (NIR) spectroscopy with appropriate data analysis methods. In this paper, we measured the spectra, soluble solid content (SSC) and total acid content (TAC) of peach samples, applied Principal Component Analysis (PCA) algorithm, Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM) to conduct SSC and TAC prediction of peach samples. Through the experiments, we could know that it is feasible to forecast the SSC and TAC values of peaches and near infrared spectroscopy technique, coupled with intelligent algorithm, could be used as a quality control method for peaches.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132503186","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 hybrid model of VSM and LDA for text clusteing 基于VSM和LDA的文本聚类混合模型
2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167213
Xiaomeng Liu, Haitao Xiong, Nan Shen
{"title":"A hybrid model of VSM and LDA for text clusteing","authors":"Xiaomeng Liu, Haitao Xiong, Nan Shen","doi":"10.1109/CIAPP.2017.8167213","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167213","url":null,"abstract":"In today's era, the number of today's web text is exploding. The analysis of the text is still a hot topic. The traditional VSM model in the weight statistics and similarity calculation, due to the data latitude is too high, lack of understanding and other issues, will lead to the final clustering inaccurate. In view of this, this paper presents a hybrid model of VSM and LDA for text clustering. Through the collection of text, filtering, application of statistical methods we calculated VSM model and LDA model similarity respectively. The two similarity models are combined by linear addition method, and the mixed similarity is obtained. Then through the K-means algorithm for text clustering and the three models of clustering results we can get the visual effect of clustering. Finally we can judge the merits of the model. The experimental results show that this hybrid model is effective.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128442609","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: 11
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