International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)最新文献_第2页

Reflective code for gray block embedding 灰色块嵌入的反射代码

International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012) Pub Date : 2012-03-21 DOI: 10.1109/ICPRIME.2012.6208346

S. Janakiraman, N. Suriya, V. Nithiya, B. Radhakrishnan, J. Ramanathan, Rengarajan Amirtharajan

引用次数: 7

An improved support vector machine kernel for medical image retrieval system 一种改进的支持向量机核医学图像检索系统

International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012) Pub Date : 2012-03-21 DOI: 10.1109/ICPRIME.2012.6208354

M. S. Kumar, Y. S. Kumaraswamy

{"title":"An improved support vector machine kernel for medical image retrieval system","authors":"M. S. Kumar, Y. S. Kumaraswamy","doi":"10.1109/ICPRIME.2012.6208354","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208354","url":null,"abstract":"Digital medical images take up most of the storage space in the medical database. Digital images are in the form of X-Rays, MRI, CT. These medical images are extensively used in diagnosis and planning treatment schedule. Retrieving required medical images from the database in an efficient manner for diagnosis, research and educational purposes is essential. Image retrieval systems are used to retrieve similar images from database by inputting a query image. Image retrieval systems extract features in the image to a feature vector and use similarity measures for retrieval of images from the database. So the efficiency of the image retrieval system depends upon the feature selection and its classification. In this paper, it is proposed to implement a novel feature selection mechanism using Discrete Sine Transforms (DST) with Information Gain for feature reduction. Classification results obtained from existing Support Vector Machine (SVM) is compared with the proposed Support Vector Machine model. Results obtained show that the proposed SVM classifier outperforms conventional SVM classifier and multi layer perceptron neural network.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129161360","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: 7

Cauchy-Euler model, cellular automata simulation of the rate of recovery of the infected airway from COPD Cauchy-Euler模型，细胞自动机模拟COPD感染气道的恢复率

International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012) Pub Date : 2012-03-21 DOI: 10.1109/ICPRIME.2012.6208368

B. M. Vaganan, D. Pandiaraja, S. Sundar, E. E. Priya

引用次数: 2

Modified backpropagation algorithm with adaptive learning rate based on differential errors and differential functional constraints 基于微分误差和微分函数约束的自适应学习率改进反向传播算法

International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012) Pub Date : 2012-03-21 DOI: 10.1109/ICPRIME.2012.6208288

T. Kathirvalavakumar, S. J. Subavathi

引用次数: 4

Mammogram image segmentation using granular computing based on rough entropy 基于粗糙熵的乳房x线图像分割的颗粒计算

International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012) Pub Date : 2012-03-21 DOI: 10.1109/ICPRIME.2012.6208365

R. Roselin, K. Thangavel

引用次数: 7

Image segmentation using nearest neighbor classifiers based on kernel formation for medical images 基于核形成的医学图像最近邻分类器图像分割

International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012) Pub Date : 2012-03-21 DOI: 10.1109/ICPRIME.2012.6208355

R. Harini, C. Chandrasekar

引用次数: 10

A new approach to reduce flooding in Grid Fisheye state routing (GFSR) protocol by propagation neighborhood 基于传播邻域的网格鱼眼状态路由(GFSR)协议减少泛洪的新方法

International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012) Pub Date : 2012-03-21 DOI: 10.1109/ICPRIME.2012.6208341

S. Nithya, Rekha C Chandrasekar, R. Kaniezhil

{"title":"A new approach to reduce flooding in Grid Fisheye state routing (GFSR) protocol by propagation neighborhood","authors":"S. Nithya, Rekha C Chandrasekar, R. Kaniezhil","doi":"10.1109/ICPRIME.2012.6208341","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208341","url":null,"abstract":"Mobile Ad-hoc Network (MANET) is the self organizing collection of mobile nodes. Ad hoc wireless networks have massive commercial and military potential because of their mobility support. Quality of Service (QoS) routing in mobile Ad-Hoc networks is challenging due to rapid change in network topology. In this paper, we focused to reduce flooding performance of the Fisheye State Routing (FSR) protocol in Grid using ns-2 network simulator under different performance metrics scenario in respect to number of Nodes and Pause-Time. The connection establishment is costly in terms of time and resource where the network is mostly affected by connection request flooding. The proposed approach presents a way to reduce flooding in MANETs. Flooding is dictated by the propagation of connection-request packets from the source to its neighborhood nodes. The proposed architecture promotes on the concept of sharing neighborhood information. The proposed approach focuses on exposing its neighborhood peer to another node that is referred to as its friend-node, which had requested/forwarded connection request. If there is a high probability for the friend node to communicate through the exposed routes, this could improve the efficacy of bandwidth utilization by reducing flooding, as the routes have been acquired, without any broadcasts. Friendship between nodes is quantized based on empirical computations and heuristic algorithms. The nodes store the neighborhood information in their cache that is periodically verified for consistency. Simulation results show the performance of this proposed method.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116960098","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

Increasing cluster uniqueness in Fuzzy C-Means through affinity measure 通过亲和度量提高模糊c均值聚类唯一性

International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012) Pub Date : 2012-03-21 DOI: 10.1109/ICPRIME.2012.6208282

A. Banumathi, A. Pethalakshmi

{"title":"Increasing cluster uniqueness in Fuzzy C-Means through affinity measure","authors":"A. Banumathi, A. Pethalakshmi","doi":"10.1109/ICPRIME.2012.6208282","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208282","url":null,"abstract":"Clustering is a widely used technique in data mining application for discovering patterns in large dataset. In this paper the Fuzzy C-Means algorithm is analyzed and found that quality of the resultant cluster is based on the initial seed where it is selected either sequentially or randomly. Fuzzy C-Means uses K-Means clustering approach for the initial operation of clustering and then degree of membership is calculated. Fuzzy C-Means is very similar to the K-Means algorithm and hence in this paper K-Means is outlined and proved how the drawback of K-Means algorithm is rectified through UCAM (Unique Clustering with Affinity Measure) clustering algorithm and then UCAM is refined to give a new view namely Fuzzy-UCAM. Fuzzy C-Means algorithm should be initiated with the number of cluster C and initial seeds. For real time large database it's difficult to predict the number of cluster and initial seeds accurately. In order to overcome this drawback the current paper focused on developing the Fuzzy-UCAM algorithm for clustering without giving initial seed and number of clusters for Fuzzy C-Means. Unique clustering is obtained with the help of affinity measures.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"341 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113955953","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

Genetic clustering with Bee Colony Optimization for flexible protein-ligand docking 基于蜂群优化的柔性蛋白配体对接遗传聚类

International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012) Pub Date : 2012-03-21 DOI: 10.1109/ICPRIME.2012.6208291

E. K. Nesamalar, C. P. Chandran

{"title":"Genetic clustering with Bee Colony Optimization for flexible protein-ligand docking","authors":"E. K. Nesamalar, C. P. Chandran","doi":"10.1109/ICPRIME.2012.6208291","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208291","url":null,"abstract":"In this paper Flexible Protein Ligand Docking is carried out using Genetic Clustering with Bee Colony Optimization. The molecular docking problem is to find a good position and orientation for docking and a small molecule ligand to a large receptor molecule. It is originated as an optimization problem consists of optimization method and the clustering technique. Clustering is a data mining task which groups the data on the basis of similarities among the data. A Genetic clustering algorithm combine a Genetic Algorithm (GA) with the K-medians clustering algorithm. GA is one of the evolutionary algorithms inspired by biological evolution and utilized in the field of clustering. K-median clustering is a variation of K-means clustering where instead of calculating the mean for each cluster to determine its centroid, one instead calculates the median. Genetic Clustering is combined with Bee Colony Optimization (BCO) algorithm to solve Molecular docking problem. BCO is a new Swarm Intelligent algorithm that was first introduced by Karaboga. It is based on the Fuzzy Clustering with Artificial Bee Colony Optimization algorithm proposed by Dervis Karaboga and Celal Ozturk. In this work, we propose a new algorithm called Genetic clustering Bee Colony Optimization (GCBCO). The performance of GCBCO is tested in 10 docking instances from the PDB bind core set and compared the performance with PSO and ACO algorithms. The result shows that the GCBCO could find ligand poses with best energy levels than the existing search algorithms.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122893305","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

Location-aware service discovery in next generation wireless networks 下一代无线网络中的位置感知服务发现

International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012) Pub Date : 2012-03-21 DOI: 10.1109/ICPRIME.2012.6208296

K. R. Nanthagobal, C. Chandrasekar

引用次数: 1