2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)最新文献_第3页

Integrated Data Compression and Pulse Rate Extraction Scheme Using Differential Coding for Wireless PPG Monitoring Devices 基于差分编码的无线PPG监测设备综合数据压缩和脉冲率提取方案

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721314

G. L. K. Reddy, M. Sabarimalai Manikandan, N. V. L. N. Murty

{"title":"Integrated Data Compression and Pulse Rate Extraction Scheme Using Differential Coding for Wireless PPG Monitoring Devices","authors":"G. L. K. Reddy, M. Sabarimalai Manikandan, N. V. L. N. Murty","doi":"10.1109/ICIINFS.2018.8721314","DOIUrl":"https://doi.org/10.1109/ICIINFS.2018.8721314","url":null,"abstract":"This paper presents a novel integrated data compression and pulse rate measurement scheme for power reduction in Internet-of-Things (IoT) enabled wireless PPG monitoring devices. In the proposed scheme, the recorded photoplethysmograph (PPG) signal is compressed using the differential pulse code modulation and Huffman coding techniques and then the pulse rate (PR) is simultaneously extracted from the differential signal, which is the difference between the original and predicted signals. The proposed scheme achieves an average compression ratio, percentage root-mean-squared difference (PRD), signal-to-noise ratio (SNR), and normalized cross correlation (NCC) of 4.76, 0.19%, 27.90 dB and 0.9991 respectively for the 5 s PPG signals digitized with a sampling rate of 100 Hz and resolution of 12 bits, on the three standard PPG databases (MIT-BIH SLP, MIMIC-II and CSL). Evaluation results show that the method yields peak detection rates of 98.94% and average PR measurement error of 0–2.6 samples. The proposed integrated scheme can enable timely measurement of the pulse rate (PR) and to significantly reduce the overall power consumption and therefore it is highly suited for IoT wireless wearable PPG sensors.","PeriodicalId":397083,"journal":{"name":"2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124384468","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: 8

SentiCon: A Concept Based Feature Set For Sentiment Analysis SentiCon:一个基于概念的情感分析特征集

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721408

Satanik Mitra, M. Jenamani

{"title":"SentiCon: A Concept Based Feature Set For Sentiment Analysis","authors":"Satanik Mitra, M. Jenamani","doi":"10.1109/ICIINFS.2018.8721408","DOIUrl":"https://doi.org/10.1109/ICIINFS.2018.8721408","url":null,"abstract":"Selection and extraction of appropriate numerical features to do sentiment analysis on text data with greater accuracy remain an open problem. In supervised machine learning based sentiment analysis, Term Frequency- Inverse Document Frequency (TF-IDF) scores are used as a feature for classifying polarity of text data. TF-IDF features are a high dimensional representation of the importance of a word in the document. TF-IDF features are sparse and do not consider the correlation among the words which constructs the latent concepts in the document. Latent Semantic Analysis (LSA) removes sparseness of the TF-IDF features by representing it in a low dimensional matrix and extracts those hidden concepts. On the other hand, a natural property of text document is its information content. The quantitative estimation of Parts-of-Speech tags, negation words, sentiment lexicons etc. represent the quality of information shared in a text data. In this work, we propose an approach to generate a concept based domain specific feature set SentiCon by consolidating LSA with the quality of information of the corpus. We have applied Singular Value Decomposition (SVD) on TF-IDF features to find the LSA. We have tested SentiCon with two benchmark datasets IMDB movie review and Epinion Cars, Books datasets using four well-known classifiers - Decision Tree, Random Forrest, Support Vector Machine, and K-Nearest Neighbour classifiers. We have used standard performance measures precision, recall and F-measure to analyze the results.","PeriodicalId":397083,"journal":{"name":"2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127409964","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

Secrecy Outage Probability for MRC Receiver in Presence of Multiple Antenna Eavesdropper 多天线窃听器存在下MRC接收机保密中断概率

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721395

B. Kumbhani, H. Sharma

引用次数: 0

Spherical Directional Feature Extraction with Artificial Neural Networkfor Diabetic Retinopathy Classiftcation 基于人工神经网络的糖尿病视网膜病变球面定向特征提取

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721392

S. Randive, R. K. Senapati, N. Bhosle

{"title":"Spherical Directional Feature Extraction with Artificial Neural Networkfor Diabetic Retinopathy Classiftcation","authors":"S. Randive, R. K. Senapati, N. Bhosle","doi":"10.1109/ICIINFS.2018.8721392","DOIUrl":"https://doi.org/10.1109/ICIINFS.2018.8721392","url":null,"abstract":"Since last two decades one of the fast advancing and most sensitive research area is observed to be detection of diabetic retinopathy (DR). In fundus images detection of retinal lesions depends on grading of diabetic retinopathy and computer-aided screening which led to development of automatic telemedicine system. The detection accuracy is still a matter of concern even after existence of huge contribution in area of detection. The existing algorithm for classification of DR images is not able to encode the directional information in 2D and 3D plane. The proposed approach encodes in four different directions (0°, 45°, 90° and 135°) from the reference pixel to its surrounding pixel in 3D plane. The proposed model includes preprocessing, feature extraction using spherical directional local ternary pattern (SDLTP) and classification using traditional distance measure and learning based distance measure using artificial neural network (ANN). SDLTP is used for extracting the directional feature in 3D plane and to reduce the feature vector length, a principle component analysis (PCA) technique is adopted. Further, two techniques are used for classification purpose (distance measure and ANN). The proposed method classification accuracy is measured in terms of precision. From experimental analysis, the proposed method give significant improvement in classification accuracy in both unsupervised and supervised domain because feature extraction in implemented considering the directional information.","PeriodicalId":397083,"journal":{"name":"2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116983161","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

GLCM and ANN based Approach for Classification of Radiographics Weld Images 基于GLCM和ANN的射线照相焊缝图像分类方法

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721421

J. Kumar, S. P. Srivastava, R. Anand, P. Arvind, Shivain Bhardwaj, Ankit Thakur

引用次数: 7

Network Coding Based Multiple Fault Tolerance Scheme in P2P Cloud Storage System 基于网络编码的P2P云存储系统多重容错方案

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721316

N. Arya, R. R. Rout, G. Lingam

引用次数: 3

Adaptive Over-current Protection Algorithm for a Microgrid 微电网自适应过流保护算法

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721367

Mandeep Singh, A. V. Ravi Teja

引用次数: 4

Interference Aware Resource Allocation (IARA) in Cognitive Radio Networks 认知无线网络中的干扰感知资源分配

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721375

M. Pavan, Sushil Kumar, Gajendra Nayak

引用次数: 1

DNN based Acoustic Scene Classification using Score Fusion of MFCC and Inverse MFCC 基于深度神经网络的MFCC与逆MFCC评分融合声学场景分类

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721379

Chandrasekhar Paseddula, S. Gangashetty

引用次数: 11

Local pattern-based descriptors for iris recognition: A comparative analysis 基于局部模式的虹膜识别描述符:比较分析

2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2018-12-01 DOI: 10.1109/ICIINFS.2018.8721368

Ritesh Vyas, T. Kanumuri, G. Sheoran, V. Kumari

引用次数: 0