2017 9th International Conference on Knowledge and Smart Technology (KST)最新文献_第2页

Automatic discovering success factor relationship entities in articles using named entity recognition 使用命名实体识别自动发现文章中的成功因素关系实体

2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886123

Supattra Niboonkit, Worarat Krathu, P. Padungweang

引用次数: 1

Study on extracting implicit patterns of patent data based on timeline 基于时间轴的专利数据隐含模式提取方法研究

2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886132

Athita Onuean, Jangwon Gim, Yunji Jang, Hanmin Jung

引用次数: 0

Research of mining algorithms for uncertain spatio-temporal co-occurrence pattern 不确定时空共现模式挖掘算法研究

2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886070

Zhanquan Wang, Bowen Lu, Fangli Ying, Man Kong, Minwei Tang

引用次数: 3

Sentiment analysis for short Chinese text based on character-level methods 基于字符级方法的中文短文本情感分析

2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886093

Y. An, Xinhuai Tang, Bin Xie

引用次数: 8

Text mining, patent retrieval and its evaluation: Finding paths in the Labyrinth between legal and technical challenges 文本挖掘、专利检索及其评价:在法律与技术挑战之间的迷宫中寻找路径

2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886065

Thomas Mandl

引用次数: 1

Automated segmentation of media-adventitia and lumen from intravascular ultrasound images using non-parametric thresholding 使用非参数阈值从血管内超声图像中自动分割中外膜和腔

2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886110

Anusorn Wong-od, Annupan Rodtook, Suwanna Rasmequan, K. Chinnasarn

引用次数: 6

Electronic nose based wireless sensor network for soil monitoring in precision farming system 基于电子鼻的无线传感器网络在精准农业系统中的土壤监测

2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886087

U. Dorji, T. Pobkrut, T. Kerdcharoen

引用次数: 11

Rule extraction from electroencephalogram signals using support vector machine 基于支持向量机的脑电信号规则提取

2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886104

A. Chatchinarat, Kok Wai Wong, C. Fung

{"title":"Rule extraction from electroencephalogram signals using support vector machine","authors":"A. Chatchinarat, Kok Wai Wong, C. Fung","doi":"10.1109/KST.2017.7886104","DOIUrl":"https://doi.org/10.1109/KST.2017.7886104","url":null,"abstract":"Emotion classification and recognition from electroencephalogram (EEG) signals have been studied extensively due to its potential benefits such as entertainment and health care. Concerning classification, various techniques have been developed and applied. Support Vector Machines (SVMs) has been reported as the most used because of its accuracy. Nevertheless, although SVMs has satisfactory performance, it is unable to provide explanation of the relationships between a model's inputs and outputs. Specifically, it is desirable for a medical application for diagnosis to provide comprehensible rules. Consequently, SVM might not be suitable. In this study, SVM is treated as a black-box and then rules are extracted using the Classification And Regression Trees (CART) approach. A dataset from the Database for Emotion Analysis using Physiological Signals (DEAP) is used in this study. The experimental results show that although a classic SVM model has provided the best accuracy, a rule extraction model from SVM output by CART (SVM-CART) is better than a basic CART model. Therefore, the proposed SVM-CART approach is suitable for applications which need explanations and comprehensibility, such as medical applications.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116887203","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

Distinguishing ACL patients from healthy individuals using multilayer perceptron on motion patterns 基于运动模式的多层感知器识别ACL患者与健康人

2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886068

Worapan Kusakunniran, Nantawat Prachasri, Nattaporn Dirakbussarakom, Duangkamol Yangchaem

{"title":"Distinguishing ACL patients from healthy individuals using multilayer perceptron on motion patterns","authors":"Worapan Kusakunniran, Nantawat Prachasri, Nattaporn Dirakbussarakom, Duangkamol Yangchaem","doi":"10.1109/KST.2017.7886068","DOIUrl":"https://doi.org/10.1109/KST.2017.7886068","url":null,"abstract":"Anterior Cruciate Ligament (ACL) injury is an injury of knee joints happened to many athletes. It has the significant impact on the patients' movement in their daily life and sport activities. Thus, it is important to detect the ACL injury in an early stage. So that, the proper treatment can be operated in time. This paper proposes a novel method to seek out differences of gait patterns between the ACL reconstructed patients and the healthy individuals, along with classifying the class where the gait data belongs to. The multilayer perceptron (MLP) will then be applied as the classification tool. In the experiment, 8 subjects are used to validate the proposed method. Each subject contains nine different observed variables of gait information gathered from the 3D motion-capture camera system. The proposed method can achieve a very promising performance of about 90% accuracy. Also, the weights gaining from the MLP model with respect to each particular gait variable are plotted, in order to determine the key variables that are significant to discriminate the ACL reconstructed patients from the healthy individuals. The detected three dominant variables are the ground reaction force, the ankle joint moment, and the ankle joint angle.","PeriodicalId":190832,"journal":{"name":"2017 9th International Conference on Knowledge and Smart Technology (KST)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115615418","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

3DDIR: The distance interior ratio of volumetric models for object recognition 3DDIR:用于物体识别的体积模型的距离内比

2017 9th International Conference on Knowledge and Smart Technology (KST) Pub Date : 2017-02-01 DOI: 10.1109/KST.2017.7886100

Chinnawat Wongwises, Natsuda Kaothanthong, W. Limprasert

引用次数: 0