2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC)最新文献_第6页

A tool for morphologically ambiguous text processing 一个用于形态学歧义文本处理的工具

2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) Pub Date : 2016-07-06 DOI: 10.1109/DIPDMWC.2016.7529377

E. Klyshinsky, N. Kochetkova

引用次数: 0

Effective strategy for competences forming

2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) Pub Date : 2016-07-06 DOI: 10.1109/DIPDMWC.2016.7529396

S. Kulik, K. Tkachenko

引用次数: 3

Multi-objective selection approach for association mining based on interesting measures 基于兴趣度量的关联挖掘多目标选择方法

2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) Pub Date : 2016-07-06 DOI: 10.1109/DIPDMWC.2016.7529359

P. Leesutthipornchai

引用次数: 0

Two layers of beam alignment for millimeter-wave communications 用于毫米波通信的两层波束对准

2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) Pub Date : 2016-07-06 DOI: 10.1109/DIPDMWC.2016.7529400

Yi Wang, Zhenyu Shi, Kun Zeng, Peiying Zhu

引用次数: 2

Predicting IT employability using data mining techniques 使用数据挖掘技术预测IT就业能力

2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) Pub Date : 2016-07-06 DOI: 10.1109/DIPDMWC.2016.7529358

K. Piad, Menchita F. Dumlao, Melvin A. Ballera, Shaneth C. Ambat

引用次数: 39

A computational model for refining Data domains in the property reconciliation 用于在属性调节中细化数据域的计算模型

2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) Pub Date : 2016-07-06 DOI: 10.1109/DIPDMWC.2016.7529364

Viacheslav Wolfengagen, L. Ismailova, S. Kosikov

{"title":"A computational model for refining Data domains in the property reconciliation","authors":"Viacheslav Wolfengagen, L. Ismailova, S. Kosikov","doi":"10.1109/DIPDMWC.2016.7529364","DOIUrl":"https://doi.org/10.1109/DIPDMWC.2016.7529364","url":null,"abstract":"A computational model for refining of data domains which are selected out in the property recognition over the Big Data sources is developed and considered. Data sources can originate both from natural and/or human activities. Thus discovered in a problem domain data objects - the individuals, - are considered as processes in a mathematical sense depending on parameters. The proposed parametrization is based on two-dimensional model using cross-referencing over assignments/crowdsoucers and recognizable properties/domains and is aimed to support the iteration procedure. This gives rise to the computational model based on the variable domains assumption. Such a vision is able to take into account the interaction of crowdsourcers and properties when they are varying with the evolving the events. The property recognition stage-by-stage model enables the fine tuning of the target data domains and has the representable functor. This model as may be shown is faithfully embedded into a category of indexed sets. The proposed (f, g)-tuning of the data domains leads to a neighborhood structure for cognition activity and gives a flexible computing model.","PeriodicalId":298218,"journal":{"name":"2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116571834","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: 10

Identification of JPEG files fragments on digital media using binary patterns based on Huffman code table 基于霍夫曼码表的二进制模式识别数字媒体上的JPEG文件片段

2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) Pub Date : 2016-07-06 DOI: 10.1109/DIPDMWC.2016.7529378

A. Sorokin, Ekaterina Makushenko

引用次数: 2

Research of heart failure based on heart model and S1 complexity 基于心脏模型和S1复杂度的心力衰竭研究

2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) Pub Date : 2016-07-06 DOI: 10.1109/DIPDMWC.2016.7529357

Yu-Han Cheng, Chen-Xi Wang, Min Wei, Yun-yi Li, Xie-Feng Cheng

{"title":"Research of heart failure based on heart model and S1 complexity","authors":"Yu-Han Cheng, Chen-Xi Wang, Min Wei, Yun-yi Li, Xie-Feng Cheng","doi":"10.1109/DIPDMWC.2016.7529357","DOIUrl":"https://doi.org/10.1109/DIPDMWC.2016.7529357","url":null,"abstract":"To study the internal and external characteristics of heart failure, a lumped-parameter cardiovascular simulation model is designed to analyze the internal characteristics, and a method is proposed that uses the complexity of the first heart sound (S1) amplitude sequence to analyze the external characteristics. State variable method is used to establish the mathematical expression of the cardiovascular model which includes three sub-models: systemic circulation sub-model, pulmonary circulation sub-model and heart sub-model. Based on this model the heart physiological feature is studied, especially under heart failure. Then, the complexity of S1 amplitude sequence is analyzed based on multiscale base-scale entropy algorithm. Simulation experiment shows that, one of the internal characteristics of heart failure is the reduction of myocardial elasticity coefficient, and one of the external characteristics is the reduction of the complexity of S1 amplitude sequence. Simulation result being in accord with clinical data indicates that the proposed approach has feasibility and practicability in heart failure diagnosis.","PeriodicalId":298218,"journal":{"name":"2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125065663","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

An adaptive image mixed noise removal algorithm based on MMTD 基于MMTD的自适应图像混合噪声去除算法

2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) Pub Date : 2016-07-06 DOI: 10.1109/DIPDMWC.2016.7529370

Ningning Zhou, Shaobai Zhang

引用次数: 3

Recognition for objects by relationship between attributes 根据属性之间的关系进行对象识别

2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) Pub Date : 2016-07-01 DOI: 10.1109/DIPDMWC.2016.7529408

Hiroka Horiguchi, Kazuo Ikeshiro, H. Imamura

{"title":"Recognition for objects by relationship between attributes","authors":"Hiroka Horiguchi, Kazuo Ikeshiro, H. Imamura","doi":"10.1109/DIPDMWC.2016.7529408","DOIUrl":"https://doi.org/10.1109/DIPDMWC.2016.7529408","url":null,"abstract":"The object recognition method based on attributes has been studied. The conventional method recognizes objects by the presence or absence of attributes. However, the conventional method has two problems. Firstly, the conventional method is not able to recognize a target object of which a part of attribute is occluded. Secondly, the conventional method misrecognizes a target object which has irrelevant attributes. Therefore, to solve these two problems, we propose the object recognition by relationship between attributes. In this paper, we focus on the face as recognition object. The proposed method uses relationship as constraints for object recognition using attributes. The proposed method applies two major type constraints. First constraint is a local constraint which is applied to a part of attributes. To achieve robust face recognition against occlusion scenes, the proposed method uses the local constraint. And then, Second constraint is a global constraint which is applied to all attributes. To achieve robust face recognition against irrelevant attributes, the proposed method uses the global constraint. In this paper, to evaluate effectiveness of the proposed method, we compare the proposed method with the conventional method. We experimented in normal face, occlusion and irrelevant attributes. We used 43 images of a face which are changed in scale and rotation. Experimental results showed that the recognition ratio of the proposed method is equal to or more than the conventional method in normal face, occlusion and irrelevant attributes.","PeriodicalId":298218,"journal":{"name":"2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132778977","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