2019 IEEE Conference on Information and Communication Technology最新文献_第3页

Undani – A System for Enhanced Farming Undani -一个提高农业水平的系统

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066235

Ashish Srivastava, R. Mahajan, D. Sagar, Pratik Shende

引用次数: 0

An Authenticated and Secure Electronic Health Record System 认证和安全的电子健康记录系统

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066168

Varun Shukla, Arpit Mishra, Anchal Yadav

引用次数: 1

Abnormality classification in the kidney ultrasound images using singular value decomposition features 基于奇异值分解特征的肾脏超声图像异常分类

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066200

S. Sudharson, Priyanka Kokil

引用次数: 2

An Unsupervised Neural Network Approach for Inverse Kinematics Solution of Manipulator following Kalman Filter based Trajectory 基于卡尔曼滤波的机械臂运动学逆解的无监督神经网络方法

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066197

G. Bhardwaj, N. Sukavanam, Ruchi Panwar, R. Balasubramanian

引用次数: 1

SHEMB:A secure approach for healthcare management system using blockchain SHEMB:使用区块链的医疗保健管理系统的安全方法

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066237

Nitish Andola, Raghav, S. Prakash, S. Venkatesan, S. Verma

引用次数: 7

Early classification of time series based on uncertainty measure 基于不确定性测度的时间序列早期分类

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066213

Anshul Sharma, S. Singh

{"title":"Early classification of time series based on uncertainty measure","authors":"Anshul Sharma, S. Singh","doi":"10.1109/CICT48419.2019.9066213","DOIUrl":"https://doi.org/10.1109/CICT48419.2019.9066213","url":null,"abstract":"The early classification of time series data is a critical problem in many time-sensitive applications such as health informatics. Where the prediction of class value, as early as possible, is highly valuable while preserving the accuracy as on full-length sequence data. For example, early diagnosis can provide better treatment to the patient or even save their lives. The aim of early classification is to analyse the sequence data at each time point continuously and predict the class label when a sufficient amount of data is available. Thus, the decision of early classification is a challenging task that needs to be addressed. Therefore, in this work, we propose an early classification model which relies on a set of probabilistic classifier and a confidence threshold that is measured in term of uncertainty. Formally, our model is divided into two parts. i) Learning phase, define the safeguard point for each class so that it makes sense to predict the label of any sequence with some acceptable accuracy. These safeguard points are identified based on user-defined accuracy. ii) Prediction phase, classify the time series only if the uncertainty of probabilistic output lie under the confidence threshold, that is obtained in the learning phase. We have evaluated our proposed model for 15 UCR datasets and compared with baseline state-of-art methods. Results clearly show that our proposed model is sianificantlv better in term of early classification.","PeriodicalId":234540,"journal":{"name":"2019 IEEE Conference on Information and Communication Technology","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132218642","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

Improving Spectral Efficiency for Device-to-Device Data Offloading in Underlay Cellular Networks 提高底层蜂窝网络中设备到设备数据卸载的频谱效率

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066179

Palash Kundu, Mohit Mahata, M. Rana, B. Sardar

{"title":"Improving Spectral Efficiency for Device-to-Device Data Offloading in Underlay Cellular Networks","authors":"Palash Kundu, Mohit Mahata, M. Rana, B. Sardar","doi":"10.1109/CICT48419.2019.9066179","DOIUrl":"https://doi.org/10.1109/CICT48419.2019.9066179","url":null,"abstract":"Device-to-Device (D2D) communications underlying cellular networks have been becoming a promising technology to improve spectral efficiency in next generation wireless networks. As an emerging paradigm, it decreases latency, increases coverage, and enhances performance of the network in terms of spectral efficiency (SE) of the network. However, the problem of interference imposes a great technical challenge to radio resource allocation in underlay D2D communications. Due to inherent nature of consuming high speed low cost data, D2D enabled cellular devices can switch from licensed cellular network to integrated unlicensed cellular networks by changing underlay D2D mode to unlicensed cellular mode named as global offloading. We study how in global offloading, D2D devices release scare shared resources and reduce interference resulting improvement of SE of the cellular network. We also propose a probabilistic model based on macro-to-femto cell changing probability of D2D devices, and apply the proposed modeling technique on basic Shannon based interference management scheme, optimal resource sharing scheme, and interference-aware graph based scheme to obtain SE of the cellular network. Numerical results confirm that our proposed modeling technique dramatically improves SE of the cellular network with respect to different percentages (e.g., 25%, 50%, and 75%) of resource blocks released by D2D devices during global offloading in all three schemes.","PeriodicalId":234540,"journal":{"name":"2019 IEEE Conference on Information and Communication Technology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134189157","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

Noise Analysis of Quantum Approximate Optimization Algorithm on Weighted MAX-CUT 加权MAX-CUT量子近似优化算法的噪声分析

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066254

Lakshya Priyadarshi, Utkarsh Azad

引用次数: 0

ResNet Convolution Neural Network Based Hyperspectral Imagery Classification for Accurate Cancerous Region Detection 基于ResNet卷积神经网络的高光谱图像分类精准癌区检测

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066215

Pandia Rajan Jeyaraj, Edward Rajan Samuel Nadar, B. K. Panigrahi

{"title":"ResNet Convolution Neural Network Based Hyperspectral Imagery Classification for Accurate Cancerous Region Detection","authors":"Pandia Rajan Jeyaraj, Edward Rajan Samuel Nadar, B. K. Panigrahi","doi":"10.1109/CICT48419.2019.9066215","DOIUrl":"https://doi.org/10.1109/CICT48419.2019.9066215","url":null,"abstract":"Classification of cancer image based on Region of Interest (ROI) is the central issues of hyperspectral application. Using the available spatial information on images, the classification is a difficult task. However, due to the advancement of image processing algorithm, the processing of mixed pixel image is a notable research topic. Most of the classification techniques use only dimension reduction and depends on reference method. In this research, we merged both spectral and spatial characteristics information's about classification of mixed pixel image was presented. First, we perform training in the standard cancerous image dataset. Then proposed a deep classification architecture framework based on the Convolution Neural Network (CNN) based ResNet architecture of cancer region detection. Then in testing we present the image for classification based on ROI. To verify the performance of designed ResNet based CNN network, we calculated the performance index like accuracy, training time and classification error for detecting region of interest calcification. Then, we compared the performance with other conventional classifiers for experimental verification to oral cancer region detection. From the obtained results, we identified that the designed ResNet based CNN network can accurately classify the oral cancer in a mixed pixel complex image.","PeriodicalId":234540,"journal":{"name":"2019 IEEE Conference on Information and Communication Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122190480","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

Tamper-Proof Certificate Management System 防篡改证书管理系统

2019 IEEE Conference on Information and Communication Technology Pub Date : 2019-12-01 DOI: 10.1109/CICT48419.2019.9066236

Raghav, Nitish Andola, Rakhi Verma, S. Venkatesan, S. Verma

{"title":"Tamper-Proof Certificate Management System","authors":"Raghav, Nitish Andola, Rakhi Verma, S. Venkatesan, S. Verma","doi":"10.1109/CICT48419.2019.9066236","DOIUrl":"https://doi.org/10.1109/CICT48419.2019.9066236","url":null,"abstract":"Certificates are a proof of achievement/membership like University degrees and school certificates etc. Certificates as a proof are essential in the society but our current certificate management system is mostly analog, inefficient and amenable to forgery. Due to the ineffective anti-forge mechanism, forged certificates are becoming prevalent. To solve this problem, many secure certificate management are proposed, then also security problems like privacy, transparency and forgeries still exist. We proposes a tamper-proof certificate management using hyper-ledger which provides secure anti-forge mechanism. Hyperledger has unmodifiablity and other suitable properties of the blockchain that helps to minimize the problem of forgery. We use IPFS (Inter Planetary File System) for storing the certificate. The procedure for issuing the certificates is to first generate the degree of a student using portal, meanwhile then calculate its hash value and encrypt it using asymmetric encryption. Then store file into IPFS. Finally, we make a transaction that contains metadata of certificate which stores in the blockchain system. Then, chaincode used for verification of user's document. we also reduce the time complexity of searching and verifying the multiple of document of a same user. Our proposed work enhances the credibility of paper-based certificates, and also reduces the risk of forging certificates. We also show the performance of generating transactions in hyperledger.","PeriodicalId":234540,"journal":{"name":"2019 IEEE Conference on Information and Communication Technology","volume":"10 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131490188","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: 4