The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014)最新文献_第7页

An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers 利用神经网络和集成分类器对心电信号进行异常心跳检测

The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014) Pub Date : 2014-04-08 DOI: 10.1109/SKIMA.2014.7083561

Diptangshu Pandit, Li Zhang, N. Aslam, Chengyu Liu, Md. Alamgir Hossain, Samiran Chattopadhyay

{"title":"An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers","authors":"Diptangshu Pandit, Li Zhang, N. Aslam, Chengyu Liu, Md. Alamgir Hossain, Samiran Chattopadhyay","doi":"10.1109/SKIMA.2014.7083561","DOIUrl":"https://doi.org/10.1109/SKIMA.2014.7083561","url":null,"abstract":"This paper presents an investigation into the development of an efficient scheme to detect abnormal beat from lead II Electro Cardio Gram (ECG) signals. Firstly, a fast ECG feature extraction algorithm was proposed which could extract the locations, amplitudes waves and interval from lead II ECG signal. We then created 11 customized features based on the outputs of the feature extraction algorithm. Then, we used these 11 features to train an artificial neural network and an ensemble classifier respectively for detecting the abnormal ECG beats. Three manually annotated databases were used for training and testing our system: MIT-BIH Arrhythmia, QT and European ST-T database availed from Physionet databank. The results showed that for an abnormal beat detection, the neural network classifier had an overall accuracy of 98.73% and the ensemble classifier with AdaBoost had 99.40%. Using time domain processing approach, the proposed scheme reduced overall computational complexity as compared to the existing methods with an aim to deploy on the mobile devices in the future to promote early and instant abnormal ECG beat detection.","PeriodicalId":22294,"journal":{"name":"The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014)","volume":"7 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2014-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72657913","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: 14

JuiceGen: The JUnit test generation tool from the UML state machine diagram JuiceGen:来自UML状态机图的JUnit测试生成工具

The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014) Pub Date : 2014-01-01 DOI: 10.1109/SKIMA.2014.7083390

C. Doungsa-ard, K. Dahal, Zeeshan Pervez

{"title":"JuiceGen: The JUnit test generation tool from the UML state machine diagram","authors":"C. Doungsa-ard, K. Dahal, Zeeshan Pervez","doi":"10.1109/SKIMA.2014.7083390","DOIUrl":"https://doi.org/10.1109/SKIMA.2014.7083390","url":null,"abstract":"This paper proposes a JUnit test code generation tool from the UML state machine diagram which is referred to here as JuiceGen tool. Genetic algorithm (GA) based approach is used to generate the test data because of its simplicity and effectiveness. The generated test data are sequences of triggers which change the status of the state machine diagram. The GAs can generate sequences of triggers which can cover more than 95% transition coverage. The triggers are mapped as methods called in the test code. Junit test code is generated not only from the sequences of triggers. The mapping information between the state machine diagram and the class under tests are also required. This detail includes: the methods which map to the triggers; the class members which map to the attribute; and the initial value of the attributes of the state machine. The generated JUnit test code has been tested by finding the code coverage of the program under test. The experimental results show that JUnit code generated from JuiceGen can represent all behaviours which the sequence of triggers could cover.","PeriodicalId":22294,"journal":{"name":"The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014)","volume":"262 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77442534","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

Test data generation from Hibernate constraints 从Hibernate约束中生成测试数据

The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014) Pub Date : 2014-01-01 DOI: 10.1109/SKIMA.2014.7083566

Krittaya Marin, C. Doungsa-ard

引用次数: 2

Cover page 封面页

The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014) Pub Date : 2012-01-01 DOI: 10.1093/jpids/pis016

引用次数: 0