2017 International Conference on Intelligent Environments (IE)最新文献_第3页

Chronic Heart Failure Detection from Heart Sounds Using a Stack of Machine-Learning Classifiers 使用机器学习分类器堆栈从心音中检测慢性心力衰竭

2017 International Conference on Intelligent Environments (IE) Pub Date : 2017-08-01 DOI: 10.1109/IE.2017.19

M. Gjoreski, M. Simjanoska, A. Gradišek, A. Peterlin, M. Gams, G. Poglajen

{"title":"Chronic Heart Failure Detection from Heart Sounds Using a Stack of Machine-Learning Classifiers","authors":"M. Gjoreski, M. Simjanoska, A. Gradišek, A. Peterlin, M. Gams, G. Poglajen","doi":"10.1109/IE.2017.19","DOIUrl":"https://doi.org/10.1109/IE.2017.19","url":null,"abstract":"Chronic heart failure represents a global pandemic, currently affecting over 26 million of patients worldwide. It is a major contributor in the death rate of patients with cardiovascular diseases and results in more than 1 million hospitalizations annually in Europe and North America. Methods for chronic heart failure detection can be utilized to act preventive, improve early diagnosis and avoid hospitalizations or even life-threatening situations, thus highly enhance the quality of patient’s life. In this paper, we present a machine-learning method for chronic heart failure detection from heart sounds. The method consists of: filtering, segmentation, feature extraction and machine learning. The method was tested with a leave-one-subject-out evaluation technique on data from 122 subjects, gathered in the study. The method achieved 96% accuracy, outperforming a majority classifier for 15 percentage points. More specifically, it detects (recalls) 87% of the chronic heart failure subjects with a precision of 87%. The study confirmed that advanced machine learning applied on real-life sounds recorded with an unobtrusive digital stethoscope can be used for chronic heart failure detection.","PeriodicalId":306693,"journal":{"name":"2017 International Conference on Intelligent Environments (IE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124572066","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

Cooperative Game Theoretic Approach for Distributed Resource Allocation in Heterogeneous Network 异构网络中分布式资源分配的合作博弈方法

2017 International Conference on Intelligent Environments (IE) Pub Date : 2017-08-01 DOI: 10.1109/IE.2017.14

Khac-Hoai Nam Bui, Jason J. Jung

引用次数: 6

An Adaptive Sentence Representation Learning Model Based on Multi-gram CNN 基于多图CNN的自适应句子表示学习模型

2017 International Conference on Intelligent Environments (IE) Pub Date : 2017-08-01 DOI: 10.1109/IE.2017.18

Chunyun Zhang, Sheng Gao, Baolin Zhao, Lu Yang, Xiaoming Xi, C. Cui, Yilong Yin

{"title":"An Adaptive Sentence Representation Learning Model Based on Multi-gram CNN","authors":"Chunyun Zhang, Sheng Gao, Baolin Zhao, Lu Yang, Xiaoming Xi, C. Cui, Yilong Yin","doi":"10.1109/IE.2017.18","DOIUrl":"https://doi.org/10.1109/IE.2017.18","url":null,"abstract":"Nature Language Processing has been paid more attention recently. Traditional approaches for language model primarily rely on elaborately designed features and complicated natural language processing tools, which take a large amount of human effort and are prone to error propagation and data sparse problem. Deep neural network method has been shown to be able to learn implicit semantics of text without extra knowledge. To better learn deep underlying semantics of sentences, most deepneuralnetworklanguagemodelsutilizemulti-gramstrategy. However, the current multi-gram strategies in CNN framework are mostly realized by concatenating trained multi-gram vectors to form the sentence vector, which can increase the number of parameters to be learned and is prone to over fitting. To alleviate the problem mentioned above, we propose a novel adaptive sentence representation learning model based on multigram CNN framework. It learns adaptive importance weights of different n-gram features and forms sentence representation by using weighted sum operation on extracted n-gram features, which can largely reduce parameters to be learned and alleviate the threat of over fitting. Experimental results show that the proposed method can improve performances when be used in sentiment and relation classification tasks.","PeriodicalId":306693,"journal":{"name":"2017 International Conference on Intelligent Environments (IE)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132600889","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

vIRONy: A Tool for Analysis and Verification of ECA Rules in Intelligent Environments vIRONy:智能环境下ECA规则分析与验证工具

2017 International Conference on Intelligent Environments (IE) Pub Date : 2017-08-01 DOI: 10.1109/IE.2017.32

Claudia Vannucchi, Michelangelo Diamanti, Gianmarco Mazzante, D. Cacciagrano, F. Corradini, R. Culmone, Nikos Gorogiannis, L. Mostarda, F. Raimondi

引用次数: 11

A Realtime Spatiotemporal Data Acquisition System for Precision Agriculture 面向精准农业的实时时空数据采集系统

2017 International Conference on Intelligent Environments (IE) Pub Date : 2017-08-01 DOI: 10.1109/IE.2017.23

Seonghun Lee, Messaoudi Oumaima, Su-Hyeon Ryu, Jaehwa Park

引用次数: 1

An Approach for Hybrid Indoor/Outdoor Navigation 一种室内/室外混合导航方法

2017 International Conference on Intelligent Environments (IE) Pub Date : 2017-08-01 DOI: 10.1109/IE.2017.22

Stephan Wagner, Ngewi Fet, M. Handte, P. Marrón

引用次数: 8

MADMACS - Multiadaptive Dialogue Management in Cyber-Physical Environments MADMACS -信息物理环境中的多适应对话管理

2017 International Conference on Intelligent Environments (IE) Pub Date : 2017-08-01 DOI: 10.1109/IE.2017.33

Yannick Körber, Vanessa Hahn, M. Moniri, Tim Schwartz, M. Feld

引用次数: 1

Parades Aiding System (PAR.AID.S): Intelligent Management of Carnival Parades 游行辅助系统(par . aids . s):狂欢节游行的智能化管理

2017 International Conference on Intelligent Environments (IE) Pub Date : 2017-08-01 DOI: 10.1109/IE.2017.24

Zois Koukopoulos, D. Koukopoulos

{"title":"Parades Aiding System (PAR.AID.S): Intelligent Management of Carnival Parades","authors":"Zois Koukopoulos, D. Koukopoulos","doi":"10.1109/IE.2017.24","DOIUrl":"https://doi.org/10.1109/IE.2017.24","url":null,"abstract":"Social events like carnival parades are major activities for modern urban life. However, public authorities should tackle a series of challenges in order to successfully manage such spectacles that take place in specific time periods and urban areas inside a city. Such challenges mainly concern crowd management and resource planning operations. Local public authorities should be able to take fast and efficient decisions prior, during and after the carnival parade. Decisions taken prior to the event are non-dynamic by nature, while during the event officials should make decisions in real-time based on instant data collected on the spot. Smartphone technology provides the appropriate critical infrastructure that facilitates the efficient collection and distribution of collected data. In this work, we present a specific architecture that aspires to help public authorities in managing carnival parades efficiently. Proposed architecture supports the establishment of an intelligent environment providing specific services which promote crowd management in terms of safety, cost effectiveness concerning better personnel management and spectator perceived usefulness and entertainment.","PeriodicalId":306693,"journal":{"name":"2017 International Conference on Intelligent Environments (IE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123655821","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