2013 Eleventh International Conference on ICT and Knowledge Engineering最新文献_第2页

Utilising fuzzy logic to improve Wi-Fi security 利用模糊逻辑提高Wi-Fi安全性

2013 Eleventh International Conference on ICT and Knowledge Engineering Pub Date : 2013-11-01 DOI: 10.1109/ICTKE.2013.6756275

A. Naqvi

引用次数: 2

Secure online exams on thin client 在瘦客户机上确保在线考试的安全

2013 Eleventh International Conference on ICT and Knowledge Engineering Pub Date : 2013-11-01 DOI: 10.1109/ICTKE.2013.6756287

Tassanan Treenantharath, P. Sutheebanjard

引用次数: 5

Workflow mining: Discovering process patterns & data analysis from MXML logs 工作流挖掘:从MXML日志中发现流程模式和数据分析

2013 Eleventh International Conference on ICT and Knowledge Engineering Pub Date : 2013-11-01 DOI: 10.1109/ICTKE.2013.6756289

P. Porouhan, N. Jongsawat, W. Premchaiswadi

引用次数: 6

Multiscale entropy based compressive sensing for electrocardiogram signal compression 基于多尺度熵的心电图信号压缩感知

2013 Eleventh International Conference on ICT and Knowledge Engineering Pub Date : 2013-11-01 DOI: 10.1109/ICTKE.2013.6756267

L. Sharma

{"title":"Multiscale entropy based compressive sensing for electrocardiogram signal compression","authors":"L. Sharma","doi":"10.1109/ICTKE.2013.6756267","DOIUrl":"https://doi.org/10.1109/ICTKE.2013.6756267","url":null,"abstract":"Classically, signal information is believed to be retrieved, if it is sampled at Nyquist rate. Since last decade compressive sensing is evolving which shows the signal reconstruction ability from insufficient data points. It reconstructs the signal from a set of reduced number of sparse samples that is lesser than Nyquist rate. It is required that the signal should be sparse in some basis. In wavelet domain, electrocardiogram signal shows sparseness. This paper suggests applying compressive sensing at wavelet scales. Also, the number of measurements taken at wavelet scales plays important role for successful reconstruction and to capture the maximum diagnostic information of electrocardiogram signal. At wavelet scales, the numbers of measurements are taken based on multiscale entropy. At scales, it uses random sensing matrix with independent identically distributed (i.i.d.) entries formed by sampling a Gaussian distribution. The compressed measurements are encoded using Huffman coding scheme. The reconstruction of signal is achieved by convex optimization problem by L1-norm minimization. Reconstruction error introduced due to L1-norm minimization and coding is evaluated using percentage root mean square difference (PRD), wavelet energy based diagnostic distortion (WEDD), root mean square error (RMSE), normalized maximum amplitude error (NMAX) and maximum absolute error (MAE). The highest compression ratio value is found 6.92:1 with PRD and WEDD values 8.18% and 2.33% respectively.","PeriodicalId":122281,"journal":{"name":"2013 Eleventh International Conference on ICT and Knowledge Engineering","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126497074","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

Three-input single-output current-mode universal filter using a MCDTA 使用MCDTA的三输入单输出电流模式通用滤波器

2013 Eleventh International Conference on ICT and Knowledge Engineering Pub Date : 2013-11-01 DOI: 10.1109/ICTKE.2013.6756281

M. Kumngern, Kulasak Khwama, S. Junnapiya

引用次数: 4

Tunable sinusoidal oscillator using CCII with variable current gain 使用可变电流增益的CCII可调谐正弦振荡器

2013 Eleventh International Conference on ICT and Knowledge Engineering Pub Date : 2013-11-01 DOI: 10.1109/ICTKE.2013.6756280

M. Kumngern, P. Phatsornsiri, K. Dejhan

引用次数: 3

Sentiment classification by a hybrid method of greedy search and multinomial naïve bayes algorithm 一种贪婪搜索与多项贝叶斯算法混合的情感分类方法naïve

2013 Eleventh International Conference on ICT and Knowledge Engineering Pub Date : 2013-11-01 DOI: 10.1109/ICTKE.2013.6756285

N. Chirawichitchai

引用次数: 15

Visualization of zone activity and possession in soccer games 足球比赛中区域活动和控球的可视化

2013 Eleventh International Conference on ICT and Knowledge Engineering Pub Date : 2013-11-01 DOI: 10.1109/ICTKE.2013.6756274

Sanetoshi Yamada, Keita Yagi, Syohei Munataka, Yoshiro Yamamoto

引用次数: 0

Solving the NP-hard computational problem in Bayesian networks using apache hadoop MapReduce 利用apache hadoop MapReduce解决贝叶斯网络中的NP-hard计算问题

2013 Eleventh International Conference on ICT and Knowledge Engineering Pub Date : 2013-11-01 DOI: 10.1109/ICTKE.2013.6756288

N. Jongsawat, W. Premchaiswadi

引用次数: 3

A predictive model using improved Normalized Point Wise Mutual Information (INPMI) 基于改进归一化逐点互信息(INPMI)的预测模型

2013 Eleventh International Conference on ICT and Knowledge Engineering Pub Date : 2013-11-01 DOI: 10.1109/ICTKE.2013.6756284

S. Sasikala, S. Geetha, A. Christopher, S. Balamurugan

{"title":"A predictive model using improved Normalized Point Wise Mutual Information (INPMI)","authors":"S. Sasikala, S. Geetha, A. Christopher, S. Balamurugan","doi":"10.1109/ICTKE.2013.6756284","DOIUrl":"https://doi.org/10.1109/ICTKE.2013.6756284","url":null,"abstract":"In machine learning, selection of optimal features for the classifier is a critical problem. In order to address this problem a novel feature selection method named “Improved Normalized Point wise Mutual Information (INPMI)” is proposed. The proposed INPMI method coupled with Sequential forward search (SFS) finds the best feature subset to aid feature selection process. The key properties of evaluating feature subset i.e. relevancy and redundancy are analysed well. The classifiers like Naive Bayes, Support Vector Machine and J48 are used to determine the accuracy for the choice of features selected. Experimental results with benchmark medical datasets from UCI (University of California Irvine) machine learning database show that proposed INPMI-NB model with SFS, INPMI-SVM model with SFS, INPMI-J48model with SFS achieves 98.36 %, 98.90 %, 94.53 % classification accuracy and selects 22 features for erythemato-squamous diseases. Also the proposed work is evaluated on a World Aircraft dataset to prove its generalization ability. Experimental results prove that the proposed INPMI method outperforms the existing systems.","PeriodicalId":122281,"journal":{"name":"2013 Eleventh International Conference on ICT and Knowledge Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114624763","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