2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)最新文献_第10页

Mini-XML: An efficient mapping approach between XML and relational database Mini-XML: XML和关系数据库之间的有效映射方法

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-01 DOI: 10.1109/ICIS.2017.7960109

Huchao Zhu, Huiqun Yu, Guisheng Fan, Huaiying Sun

引用次数: 13

Investment time series prediction using a hybrid model based on RBMs and pattern clustering 基于rbm和模式聚类的投资时间序列混合模型预测

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-01 DOI: 10.1109/ICIS.2017.7960017

Fan Shen, N. Luo

引用次数: 3

Application of improved wavelet neural network in MBR flux prediction 改进小波神经网络在MBR磁通预测中的应用

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-01 DOI: 10.1109/ICIS.2017.7960019

Guoshuai Cai, Chunqing Li

{"title":"Application of improved wavelet neural network in MBR flux prediction","authors":"Guoshuai Cai, Chunqing Li","doi":"10.1109/ICIS.2017.7960019","DOIUrl":"https://doi.org/10.1109/ICIS.2017.7960019","url":null,"abstract":"Membrane Bio-Reactor(MBR) technology plays an important role in modern sewage treatment » but the performance of the MBR technology is seriously affected by the membrane fouling. In general, the result of membrane fouling is decline of MBR membrane flux, and the effect of MBR sewage treatment is directly affected by the decrease of membrane flux. In order to predict MBR membrane flux accurately and rapidly, the forecasting model of MBR membrane flux based on particle swarm improving wavelet neural network algorithm (PSO_WNN) was established. In view of the complexity of the MBR membrane fouling factor, in the beginning, the main components of the factors affecting the flux of MBR membrane were analyzed. The important factor is extracted as the input of the PSO_WNN prediction model, and the membrane flux is used as the output. Then, the PSO_WNN simulation model is established, and the prediction results are obtained by using the model. By comparing the predicted data and experimental data, the predictive accuracy of this algorithm is high on the membrane flux, and compared with the BP neural network model, the comparative results show that the PSO_WNN forecasting model has higher predicted accuracy.","PeriodicalId":301467,"journal":{"name":"2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114774132","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

An analysis of consumers' cognition and reading time prediction in digital reading 数字阅读中消费者认知与阅读时间预测分析

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-01 DOI: 10.1109/ICIS.2017.7960054

Li Haiyan, Zhang Hui, Zhang Fanghong, Guo Meijing

引用次数: 1

A query suggestion method based on random walk and topic concepts 一种基于随机漫步和主题概念的查询建议方法

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-01 DOI: 10.1109/ICIS.2017.7960002

Jiawei Liu, Qingshan Li, Yishuai Lin, Yingjian Li

{"title":"A query suggestion method based on random walk and topic concepts","authors":"Jiawei Liu, Qingshan Li, Yishuai Lin, Yingjian Li","doi":"10.1109/ICIS.2017.7960002","DOIUrl":"https://doi.org/10.1109/ICIS.2017.7960002","url":null,"abstract":"Related query suggestion is very important for search engines. Users could find required information more quickly and accurately with the help of query suggestions, which could greatly improve users' search experience. Thus, query suggestion technology has become a research hotspot in the field of the search engine. Most of existing methods focused on the query log data to mine related queries. However, some of the query log data exist relatively sparse characteristics and have some interferential noise data. Besides, the method that only focus on query log trend to fail to consider the user's initial query intention. These shortages would reduce the accuracy of the recommendation. Thus, this paper proposes a query suggestion method based on random walk and topic concepts (QuS-RWTC). The method is based on the query log data and suggestions from other mature search engines, which could make the suggestions more comprehensive and obtain a higher coverage. In addition, the paper further executes procedures of topic concepts to re-order the candidate queries, which make the suggestions more accurate, since they are more satisfied to the user's initial intention. The results prove the excellent performance of QuS-RWTC method compared with traditional methods and validate the importance of topic concepts.","PeriodicalId":301467,"journal":{"name":"2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117186690","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

Stochastic radio interferometric positioning with unsynchronized modulated signals in wireless sensor networks 无线传感器网络中不同步调制信号的随机无线电干涉定位

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-01 DOI: 10.1109/ICIS.2017.7959969

Ruiling Gao, Wei-Tse Sun, A. Couch, C. Chang

{"title":"Stochastic radio interferometric positioning with unsynchronized modulated signals in wireless sensor networks","authors":"Ruiling Gao, Wei-Tse Sun, A. Couch, C. Chang","doi":"10.1109/ICIS.2017.7959969","DOIUrl":"https://doi.org/10.1109/ICIS.2017.7959969","url":null,"abstract":"Sensor localization problem is one of the fundamental issues in Wireless Sensor Networks (WSN). In this paper, we investigate this problem and present an innovative Radio Interferometric Positioning System (RIPS) for WSN — Stochastic Radio Interferometric Positioning System with Unsynchronized Modulated Signals (SRIPS_UMS). To the best of our knowledge, we are the first to utilize unsynchronized modulated signals for radio interferometric positioning and demonstrate its viability in theory. Although previous radio interferometric positioning methods provide centimeter accuracy, they are still not widely adopted due to (1) strict hardware requirements for Received Signal Strength Indicator (RSSI) circuitry (2) time synchronization problem between distributed hardware. SRIPS_UMS overcomes these limitations by using unsynchronized modulated signals. Its viability and advantages are demonstrated through mathematical models and simulations. Extensive simulations based on radio chip CC2420 demonstrate that SRIPS_UMS is an efficient positioning system for outdoor WSN.","PeriodicalId":301467,"journal":{"name":"2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129593576","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

Express supervision system based on NodeJS and MongoDB 基于NodeJS和MongoDB的快递监管系统

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-01 DOI: 10.1109/ICIS.2017.7960064

Li Liang, Ligu Zhu, Wenqian Shang, Dongyu Feng, Zida Xiao

引用次数: 13

Management information systems for advertisement based on online-to-offline strategy 基于线上到线下策略的广告管理信息系统

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-01 DOI: 10.1109/ICIS.2017.7959993

Yang Xu, Fei Teng, Qian-hua Liu

引用次数: 1

Target oriented tweets monitoring system during natural disasters 面向目标的自然灾害微博监测系统

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-01 DOI: 10.1109/ICIS.2017.7959984

S. Win, Than Nwe Aung

{"title":"Target oriented tweets monitoring system during natural disasters","authors":"S. Win, Than Nwe Aung","doi":"10.1109/ICIS.2017.7959984","DOIUrl":"https://doi.org/10.1109/ICIS.2017.7959984","url":null,"abstract":"Twitter, Social Networking Site, becomes most popular microblogging service and people have started publishing data on the use of it in natural disasters. Twitter has also created the opportunities for first responders to know the critical information and work effective reactions for impacted communities. This paper introduces the tweet monitoring system to identify the messages that people updated during natural disasters into a set of information categories and provide user desired target information type automatically. In this system, classification is done at tweet level with three labels by using LibLinear classifier. This system intended to extract the small number of informational and actionable tweets from large amounts of raw tweets on Twitter using machine learning and natural language processing (NLP). Feature extraction of this work exploited only linguistic features, sentiment lexicon based features and especially disaster lexicon based features. The annotation system also creates disaster related corpus with new tweets collected from Twitter API and annotation is done on real time manner. The performance of this system is evaluated based on four publicly available annotated datasets. The experiments showed the classification accuracy on the proposed features set is higher than the classifier based on neural word embeddings and standard bag-of-words models. This system automatically annotated the Myanmar_Earthquake_2016 dataset at 75% accuracy on average.","PeriodicalId":301467,"journal":{"name":"2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124171130","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: 28

A forecast for bicycle rental demand based on random forests and multiple linear regression 基于随机森林和多元线性回归的自行车租赁需求预测

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-01 DOI: 10.1109/ICIS.2017.7959977

YouLi Feng, Shanshan Wang

{"title":"A forecast for bicycle rental demand based on random forests and multiple linear regression","authors":"YouLi Feng, Shanshan Wang","doi":"10.1109/ICIS.2017.7959977","DOIUrl":"https://doi.org/10.1109/ICIS.2017.7959977","url":null,"abstract":"Bike sharing system is a ways of renting bicycles; bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able to rent a bike from a one pick up location and combine with their as-need, customer returns bike to the place, which they would prefer to return. This paper is asked to combine historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bike share program in Washington, D.C. Firstly, the multiple linear regression model was established by the conventional method, Multiple linear regression equation was obtained by using SPSS software, After comparing the data with the real value, it is indicated that the multiple linear regression model is less accurate. After analysis, we find that the data includes the dummy variables such as the time and the season. Hence this paper proposes a random forest model and a GBM packet to improve the decision tree. The results and the accuracy of multiple regression analysis are greatly improved when use of random forest model to predict the demand for bicycle rental.","PeriodicalId":301467,"journal":{"name":"2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126103163","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: 44