2016 Ninth International Conference on Contemporary Computing (IC3)最新文献_第5页

Image forgery detection using QR method based on one dimensional cellular automata 基于一维元胞自动机的QR图像伪造检测方法

2016 Ninth International Conference on Contemporary Computing (IC3) Pub Date : 2016-08-01 DOI: 10.1109/IC3.2016.7880197

Sandarbh Singh, R. Bhardwaj

引用次数: 3

OE-LEACH: An optimized energy efficient LEACH algorithm for WSNs OE-LEACH:一种优化的无线传感器网络节能LEACH算法

2016 Ninth International Conference on Contemporary Computing (IC3) Pub Date : 2016-08-01 DOI: 10.1109/IC3.2016.7880225

S. Gambhir, Parul

{"title":"OE-LEACH: An optimized energy efficient LEACH algorithm for WSNs","authors":"S. Gambhir, Parul","doi":"10.1109/IC3.2016.7880225","DOIUrl":"https://doi.org/10.1109/IC3.2016.7880225","url":null,"abstract":"Wireless Sensor Networks (WSNs) are designed by hundreds or thousands of tiny, low cost and multifunctional sensor nodes. Each sensor node has very low battery life. Sensor nodes have finite storage capabilities, transmission and processing range and energy resources are also limited. There are many design issues in WSNs such as mobility, energy consumption, network topology, data aggregation, localization, production cost, security, network size and density etc. Routing protocols provide efficient working of the network, increase network lifetime, responsible for maintaining the routes in the network and perform reliable multi-hop communication under various conditions. LEACH (Low Energy Adaptive Clustering Protocol) is one of the hierarchical protocol in WSNs. LEACH uses TDMA MAC Protocol. During random data distribution, a number of TDMA slots are wasted. Because sensor nodes don't know either they have data to send or not, they continuously listen to the medium and this result in idle listening problem. This paper proposed OE-LEACH (An Optimized Energy Efficient LEACH Algorithm for WSNs) to enhance the performance of the LEACH Protocol, reduce time delay and energy consumption. Network Lifetime and throughput of WSN also increases. The Proposed method is simulated in MATLAB 2010a.","PeriodicalId":294210,"journal":{"name":"2016 Ninth International Conference on Contemporary Computing (IC3)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126568832","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: 10

BRC-KEP: A secure biometric and reversible CA based 2-party key exchange protocol BRC-KEP:一个安全的生物识别和可逆的基于CA的双方密钥交换协议

2016 Ninth International Conference on Contemporary Computing (IC3) Pub Date : 2016-08-01 DOI: 10.1109/IC3.2016.7880203

S. Choudhury, A. Kalwar, A. Goswami, M. Bhuyan

引用次数: 0

Feature selection using Markov clustering and maximum spanning tree in high dimensional data 基于马尔可夫聚类和最大生成树的高维数据特征选择

2016 Ninth International Conference on Contemporary Computing (IC3) Pub Date : 2016-08-01 DOI: 10.1109/IC3.2016.7880208

Neha Bisht, Annappa Basava

{"title":"Feature selection using Markov clustering and maximum spanning tree in high dimensional data","authors":"Neha Bisht, Annappa Basava","doi":"10.1109/IC3.2016.7880208","DOIUrl":"https://doi.org/10.1109/IC3.2016.7880208","url":null,"abstract":"Feature selection is the most important preprocessing step for classification of high dimensional data. It reduces the load of computational cost and prediction time on classification algorithm by selecting only the salient features from the data set for learning. The main challenges while applying feature selection on high dimensional data (HDD) are: handling the relevancy, redundancy and correlation between features. The proposed algorithm works with the three main steps to overcome these issues. It focuses on filtering strategy for its effectiveness in handling the data sets with large size and high dimensions. Initially to measure the relevancy of features with respect to class, fisher score is calculated for each feature independently. Next, only relevant features are passed to the clustering algorithm to check the redundancy of features. Finally the correlation between features is calculated using maximum spanning tree and the most appropriate features are filtered out. The classification accuracy of the presented approach is validated by using C4.5, IB1 and Naive Bayes classifier. The proposed algorithm gives high classification accuracy when compared against the accuracies given by three different classifiers on the datasets containing features extracted from fisher score method and dataset containing all the features or full-featured dataset.","PeriodicalId":294210,"journal":{"name":"2016 Ninth International Conference on Contemporary Computing (IC3)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133133897","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

Prioritizing and optimizing risk factors in agile software development 对敏捷软件开发中的风险因素进行优先级排序和优化

2016 Ninth International Conference on Contemporary Computing (IC3) Pub Date : 2016-08-01 DOI: 10.1109/IC3.2016.7880232

R. Agrawal, Deepali Singh, Ashish Sharma

引用次数: 15

Wheelchair control using speech recognition 轮椅控制使用语音识别

2016 Ninth International Conference on Contemporary Computing (IC3) Pub Date : 2016-08-01 DOI: 10.1109/IC3.2016.7880214

P. Ghule, M. Bhalerao, Rajan H. Chile, V. G. Asutkar

引用次数: 1

Image forgery detection using Markov features in undecimated wavelet transform 基于未消差小波变换马尔可夫特征的图像伪造检测

2016 Ninth International Conference on Contemporary Computing (IC3) Pub Date : 2016-08-01 DOI: 10.1109/IC3.2016.7880221

Saurabh Agarwal, S. Chand

{"title":"Image forgery detection using Markov features in undecimated wavelet transform","authors":"Saurabh Agarwal, S. Chand","doi":"10.1109/IC3.2016.7880221","DOIUrl":"https://doi.org/10.1109/IC3.2016.7880221","url":null,"abstract":"Image forgery has become common due to the availability of high-quality image editing softwares. For detecting image forgery there is a need to have important features of the image. For obtaining the image features we need a suitable transform. One of the important and commonly used transform is discrete wavelet transform that can provide spatial and frequency related information of a signal. However, it provides ambiguous information due to its shift variant property. This ambiguity can be overcome using the UWT due to its shift invariance property. In forgery, some operations are applied to an image at different locations. For instance same type of details at different locations, the UWT provides the output of same nature, whereas the DWT doesn't. Due to this property, the features extracted in undecimated wavelet transform (UWT) domain provide better results in many applications like denoising, change detection, etc. In this paper, image features are extracted using the Markov model after transforming it into UWT domain. To evaluate the performance CASIA v1.0, Columbia Color and DSO-1 databases are used. The support vector machine with the linear kernel applied to separate the forged and pristine images. We experimentally obtain better results using the UWT transform as compared to the DWT transform on all these three databases.","PeriodicalId":294210,"journal":{"name":"2016 Ninth International Conference on Contemporary Computing (IC3)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122426423","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: 9

An effective multi-objective workflow scheduling in cloud computing: A PSO based approach 云计算中有效的多目标工作流调度:一种基于粒子群算法的方法

2016 Ninth International Conference on Contemporary Computing (IC3) Pub Date : 2016-08-01 DOI: 10.1109/IC3.2016.7880196

Shubham, Rishabh Gupta, Vatsal Gajera, P. K. Jana

引用次数: 21

Web services based path guidance to rescue team alert system during flood 基于Web服务的洪水救援预警系统路径引导

2016 Ninth International Conference on Contemporary Computing (IC3) Pub Date : 2016-08-01 DOI: 10.1109/IC3.2016.7880261

Manik Chandra, R. Niyogi

引用次数: 1

Identification and removal of different noise patterns by measuring SNR value in magnetic resonance images 通过测量磁共振图像的信噪比值来识别和去除不同的噪声模式

2016 Ninth International Conference on Contemporary Computing (IC3) Pub Date : 2016-08-01 DOI: 10.1109/IC3.2016.7880212

R. B. Yadav, Subodh Srivastava, R. Srivastava

{"title":"Identification and removal of different noise patterns by measuring SNR value in magnetic resonance images","authors":"R. B. Yadav, Subodh Srivastava, R. Srivastava","doi":"10.1109/IC3.2016.7880212","DOIUrl":"https://doi.org/10.1109/IC3.2016.7880212","url":null,"abstract":"In MR image Rician noise is one of the prominent noise, however Gaussian and Rayleigh noise are also present. These types of noises in the MRI can be identified by measuring SNR value of image data. In the literature, there are many methods available to remove Rician noise. But little method has been reported for the removal of Rayleigh and Gaussian noise in MRI. So in this paper we concentrate on removal of Rayleigh and Gaussian noise from MRI. This method is automatically identify various type of noise present into the MRI and filters them by choosing an appropriate filter. The proposed filter consists of two terms namely data fidelity and prior. The data fidelity term i.e. likelihood term is derived from Gaussian pdf and Rayleigh pdf and a nonlinear complex diffusion (CD) based prior is used. The performance analysis and comparative study of the proposed method with other standard methods is presented for Brain Web dataset at varying noise levels in terms of MSE and SSIM. From the simulation results, it is observed that the proposed framework with CD based prior is performing better in comparison to other priors in consideration.","PeriodicalId":294210,"journal":{"name":"2016 Ninth International Conference on Contemporary Computing (IC3)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115907535","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