2012 IEEE International Conference on Computational Intelligence and Computing Research最新文献_第4页

A novel index measure imputation algorithm for missing data values: A machine learning approach 缺失数据值的一种新的指标度量输入算法:一种机器学习方法

2012 IEEE International Conference on Computational Intelligence and Computing Research Pub Date : 2012-12-01 DOI: 10.1109/ICCIC.2012.6510198

G. Madhu, T. Rajinikanth

{"title":"A novel index measure imputation algorithm for missing data values: A machine learning approach","authors":"G. Madhu, T. Rajinikanth","doi":"10.1109/ICCIC.2012.6510198","DOIUrl":"https://doi.org/10.1109/ICCIC.2012.6510198","url":null,"abstract":"The problem of missing data in the real world datasets has very significant role in the real time data mining process and becomes more complex in large databases. The presence of missing values influences data set features and the class attributes, thus affecting the predictive accuracies of the classifiers. For the last one decade, many researchers have come out with different techniques for dealing with missing attribute values in databases with homogeneous and/or numeric attributes. In this research work, we proposed a new indexing measure to the imputation algorithm for missing data values of the attributes to compute the similarity measure between any two typical elements in the dataset. It can also be applied on any dataset be it a nominal and/or real. The proposed algorithm is evaluated by extensive experiments and comparison with KNNI, SVMI, WKNNI, KMI and FKMI algorithms. The results showed that the proposed algorithm has better performance than the existing imputation algorithms in terms of classification accuracy and also our decision tree algorithm employs highly accurate decision rules.","PeriodicalId":340238,"journal":{"name":"2012 IEEE International Conference on Computational Intelligence and Computing Research","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131489306","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: 16

CPW fed dual V-shaped Implantable monopole antenna for biomedical applications 生物医学用CPW馈电双v型植入式单极天线

2012 IEEE International Conference on Computational Intelligence and Computing Research Pub Date : 2012-12-01 DOI: 10.1109/ICCIC.2012.6510212

S. A. Kumar, T. Shanmuganantham

引用次数: 3

Multi-objective test suite minimisation using Quantum-inspired Multi-objective Differential Evolution Algorithm 基于量子启发的多目标差分进化算法的多目标测试套件最小化

2012 IEEE International Conference on Computational Intelligence and Computing Research Pub Date : 2012-12-01 DOI: 10.1109/ICCIC.2012.6510272

A. Charan Kumari, K. Srinivas, M. P. Gupta

{"title":"Multi-objective test suite minimisation using Quantum-inspired Multi-objective Differential Evolution Algorithm","authors":"A. Charan Kumari, K. Srinivas, M. P. Gupta","doi":"10.1109/ICCIC.2012.6510272","DOIUrl":"https://doi.org/10.1109/ICCIC.2012.6510272","url":null,"abstract":"This paper presents the solution for multi-objective test suite minimisation problem using Quantum-inspired Multi-objective differential Evolution Algorithm. Multi-objective test suite minimisation problem is to select a set of test cases from the available test suite while optimizing the multi objectives like code coverage, cost and fault history. As test suite minimisation problem is an instance of minimal hitting set problem which is NP-complete; it cannot be solved efficiently using traditional optimization techniques especially for the large problem instances. This paper presents Quantum-inspired Multi-objective Differential Evolution Algorithm (QMDEA) for the solution of multi-objective test suite minimisation problem. QMDEA combines the preeminent features of Differential Evolution and Quantum Computing. The features of QMDEA help in achieving quality Pareto-optimal front solutions with faster convergence. The performance of QMDEA is tested on two real world applications and the results are compared against the state-of-the-art multi-objective evolutionary algorithm NSGA-II. The comparison of the obtained results indicates superior performance of QMDEA.","PeriodicalId":340238,"journal":{"name":"2012 IEEE International Conference on Computational Intelligence and Computing Research","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133517049","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: 13

Equal gain combining (EGC) SC-FDMA performance over Land Mobile Satellite (LMS) Rice fading channel 等增益组合(EGC) SC-FDMA在陆地移动卫星(LMS)稻米衰落信道上的性能

2012 IEEE International Conference on Computational Intelligence and Computing Research Pub Date : 2012-12-01 DOI: 10.1109/ICCIC.2012.6510275

J. Gangane, M. Aguayo-Torres, J. Sánchez, S. Wagh

引用次数: 1

Decentralized control of multi-area power system restructuring for LFC optimization 面向LFC优化的多区域电力系统重构分散控制

2012 IEEE International Conference on Computational Intelligence and Computing Research Pub Date : 2012-12-01 DOI: 10.1109/PEDES.2012.6484456

A. K. Thirukkovulur, H. Nandagopal, V. Parivallal

引用次数: 10

Text independent language recognition system for indic languages with new features 文本独立语言识别系统为印度语言提供了新的功能

2012 IEEE International Conference on Computational Intelligence and Computing Research Pub Date : 2012-12-01 DOI: 10.1109/ICCIC.2012.6510219

M. Sadanandam, A. Nagesh, V. Prasad, V. Janaki

引用次数: 1

Integrated approach to handwritten character recognition using ANN and it's implementation on ARM 基于人工神经网络的手写体字符识别集成方法及其在ARM上的实现

2012 IEEE International Conference on Computational Intelligence and Computing Research Pub Date : 2012-12-01 DOI: 10.1109/ICCIC.2012.6510250

G. R. Rakate, A. G. Mahurkar

引用次数: 0

A new image denoising algorithm based on adaptive threshold and fourth order partial diffusion equation 一种基于自适应阈值和四阶部分扩散方程的图像去噪算法

2012 IEEE International Conference on Computational Intelligence and Computing Research Pub Date : 2012-12-01 DOI: 10.1109/ICCIC.2012.6510318

G. Santhanamari, J. S. V. Viveka, B. Purushothaman, U. Shanthini, M. Vanitha

{"title":"A new image denoising algorithm based on adaptive threshold and fourth order partial diffusion equation","authors":"G. Santhanamari, J. S. V. Viveka, B. Purushothaman, U. Shanthini, M. Vanitha","doi":"10.1109/ICCIC.2012.6510318","DOIUrl":"https://doi.org/10.1109/ICCIC.2012.6510318","url":null,"abstract":"In this paper, a dual-tree complex wavelet transform (DTCWT) based hybrid image denoising algorithm which combines fourth order Partial Diffusion Equation (PDE) and adaptive thresholding is proposed for Gaussian noise corrupted images by considering the significant dependences of the wavelet coefficients across different scales. The DTCWT has the advantage of improved directional selectivity, approximate shift invariance, and perfect reconstruction over the discrete wavelet transform. The wavelet filter bank is used to decompose the image into approximation sub band and detail sub band. Though the noise affects both the sub bands the existing wavelet thresholding methods have the final noise reduced image with limited improvement. In the proposed algorithm the fourth order PDE technique is applied on the detail sub band and the adaptive thresholding is applied to the approximate sub band and tested on Gaussian noise corrupted images. The observation of visual quality and quantitative performance in terms of PSNR and SSIM shows improvement of the proposed method over the existing wavelet-based image denoising namely anisotropic diffusion, median filtering and diffusion equation techniques.","PeriodicalId":340238,"journal":{"name":"2012 IEEE International Conference on Computational Intelligence and Computing Research","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115162904","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: 3

Tumor detection using threshold operation in MRI brain images MRI脑图像中阈值操作的肿瘤检测

2012 IEEE International Conference on Computational Intelligence and Computing Research Pub Date : 2012-12-01 DOI: 10.1109/ICCIC.2012.6510299

P. Natarajan, N. Krishnan, Natasha Sandeep Kenkre, S. Nancy, Bhuvanesh Singh

引用次数: 126

Estimation of longitudinal aerodynamic coefficients of a technology demonstrator aircraft using modified maximum likelihood algorithm 基于改进最大似然算法的技术验证机纵向气动系数估计

2012 IEEE International Conference on Computational Intelligence and Computing Research Pub Date : 2012-12-01 DOI: 10.1109/ICCIC.2012.6510222

Sunilkumar Kanayath, Dr. M. Jayakumar, Dr. G. R. Bindu

引用次数: 3