Proceedings of the 2nd International Conference on Intelligent Information Processing最新文献_第3页

A Data Mining Method for Potential Fire Hazard Analysis of Urban Buildings based on Bayesian Network 基于贝叶斯网络的城市建筑火灾隐患分析数据挖掘方法

Proceedings of the 2nd International Conference on Intelligent Information Processing Pub Date : 2017-07-17 DOI: 10.1145/3144789.3144811

Xin Liu, Yutong Lu, Zijun Xia, Feifei Li, Tianqi Zhang

{"title":"A Data Mining Method for Potential Fire Hazard Analysis of Urban Buildings based on Bayesian Network","authors":"Xin Liu, Yutong Lu, Zijun Xia, Feifei Li, Tianqi Zhang","doi":"10.1145/3144789.3144811","DOIUrl":"https://doi.org/10.1145/3144789.3144811","url":null,"abstract":"At present, with rapid development of China's urbanization, the population density increases, the structure of buildings become more complexity, and building materials and techniques emerge endlessly. Frequent unsafe personal behavior and complex external unsafe factors bring more uncontrollable influences on preventing and controlling fire hazard of buildings in urban area. Traditional methods of fire hazard analysis have limitations on fire hazards forecasting in complex urban areas. This paper presents a data mining method based on Bayesian Network for fire hazard analysis of urban buildings. Based on the historical records of fire incidents in a city of China in past three years, from 2014 to 2016, we analyze the potential fire risk according to building properties and outside influences of buildings. We process and analyze the data, and construct a Bayesian Network based on the analytic results and the actual fire extinguishing situation. After that, we train the model with positive samples and negative samples. At last, we use the Bayesian Network model to assess the risks of building fire hazards. By using ROC curve to analyze the accuracy of the model, we get accurate and stable results. Based on Bayesian Network model with building property and external influence, the building fire risk probability is about 1.0×10-9 to 1.0×10-12. We also introduce another machine learning method, Logistic Regression algorithm to evaluate the performance of Bayesian Network model. The results show that our Bayesian Network model can achieve better performance.","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124435258","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

The Dynamic Hyper-ellipsoidal Micro-Clustering for Evolving Data Stream Using Only Incoming Datum 仅使用传入基准的演化数据流动态超椭球微聚类

Proceedings of the 2nd International Conference on Intelligent Information Processing Pub Date : 2017-07-17 DOI: 10.1145/3144789.3144818

Narongrid Tangpathompong, U. Suksawatchon, J. Suksawatchon

{"title":"The Dynamic Hyper-ellipsoidal Micro-Clustering for Evolving Data Stream Using Only Incoming Datum","authors":"Narongrid Tangpathompong, U. Suksawatchon, J. Suksawatchon","doi":"10.1145/3144789.3144818","DOIUrl":"https://doi.org/10.1145/3144789.3144818","url":null,"abstract":"Data stream clustering is becoming the efficient method to cluster an online massive data. The clustering task requires a process capable of partitioning data continuously with incremental learning method. In this paper, we present a new clustering method, called DyHEMstream, which is online and offline algorithm. In online phase, dynamic hyper-ellipsoidal micro-cluster is proposed used to keep summary information about evolving data stream based on new incoming data sample. The shape of proposed micro-cluster can represent the incoming data better than traditional micro-cluster. The algorithm processes each data point in one-pass fashion without storing the entire data set. In offline phase, each cluster is generated by expanding hyper-ellipsoidal micro-clusters to form the final clusters. The DyHEMstream algorithm is evaluated on various synthetic data sets using different quality metrics compared with a famous data stream clustering -- DenStream. Based on purity, Rand index, and Jaccard index, DyHEMstrem is very efficient than DenStream in term of clustering quality in different shapes, sizes, and densities in noisy data.","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129068738","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 New Method for Compressed Sensing Color Images Reconstruction Based on Total Variation Model 基于全变分模型的压缩感知彩色图像重构新方法

Proceedings of the 2nd International Conference on Intelligent Information Processing Pub Date : 2017-07-17 DOI: 10.1145/3144789.3144810

Fan Liao, Shuai Shao

引用次数: 0

An Overview of Label Space Dimension Reduction for Multi-Label Classification 面向多标签分类的标签空间降维研究综述

Proceedings of the 2nd International Conference on Intelligent Information Processing Pub Date : 2017-07-17 DOI: 10.1145/3144789.3144807

L. Tang, Lin Liu, Jianhou Gan

引用次数: 0

Proceedings of the 2nd International Conference on Intelligent Information Processing

Proceedings of the 2nd International Conference on Intelligent Information Processing Pub Date : 1900-01-01 DOI: 10.1145/3144789

引用次数: 0