Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

Improved Particle Swarm Optimization Based K-Means Clustering

Abstract: Clustering is a popular data analysis and data mining technique K-Means is one of the most popular data mining algorithms for being simple, scalable and easily modifiable to a variety of contexts and application domains The major issue of traditional K-Means algorithm is that its performance depends on the initialization of centroid and requires the number of clusters to be specified in advance Many evolutionary based clustering algorithms have been developed in recent years for selecting optimum initial centroid to optimize clustering results Particle Swarm Optimization algorithm is a population-based memetic-evolution-motivated meta-heuristic algorithm that mimics the capability of swarm The K-Means algorithm typically uses Euclidean or squared Euclidean distance to measure the distortion between a data object and its cluster centroid The Euclidean and squared Euclidean distances are usually computed from raw data and not from standardized data Normalization is one of the important preprocessing steps, to transform values of all attributes Effective data clustering can only occur if an equally effective technique for normalizing the data is applied This paper proposes an effective partitional clustering algorithm which is developed by integrating the merits of Particle Swarm Optimization and normalization with traditional K-Means clustering algorithms Experiments are conducted on real dataset to prove the efficiency of the proposed algorithm

Parkinson's disease prediction using machine learning approaches

Abstract: This paper proposes the application of a Fully Complex-Valued Radial Basis Function network (FC-RBF), Meta-Cognitive Fully Complex-Valued Radial Basis Function network (Mc-FCRBF) and Extreme Learning Machine (ELM) for the prediction of Parkinson's disease. With the help of Unified Parkinson's Disease Rating Scale (UPDRS), the severity of the Parkinson's disease is predicted and for untreated patients, the UPDRS scale spans the range (0–176). The FC-RBF network uses a fully complex valued activation function sech, which maps cn → c. The performance of the complex RBF network depends on the number of neurons and initialization of network parameters. The implementation of the self-regulatory learning mechanism in the FC-RBF network results in Mc-FCRBF network. It has two components: a cognitive component and a meta-cognitive component. The meta-cognitive component decides how to learn, what to learn and when to learn based on the knowledge acquired by the FC-RBF network. Extreme learning mechanism uses sigmoid activation function and it works with fast speed. In ELM network, the real valued inputs and targets are applied to the network. The result indicates that the Mc-FCRBF network has good prediction accuracy than ELM and FC-RBF network.

New approach to solve symmetric fully fuzzy linear systems

Abstract: In this paper, we present a method to solve fully fuzzy linear systems with symmetric coefficient matrix The symmetric coefficient matrix is decomposed into two systems of equations by using Cholesky method and then a solution can be obtained Numerical examples are given to illustrate our method

Effective watermarking algorithm to protect Electronic Patient Record using image transform

Abstract: Due to development of latest technologies in the areas of communication and networking, the present businesses are moving to the digital world for effectiveness, convenience and security. Medical images require special safety and confidentiality because critical judgment is done on the information provided by medical images. Digital watermarking is an emerging technology to protect multimedia data for security purpose. This project suggests a comparative performance of digital image watermarking scheme using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) separately and their performance has been measured by using metrics like PSNR, Quality Index and Elapsed time. Initially, the Medical image is decomposed using image transforms like DCT or DWT. Subsequently, the watermark embedding and extraction process are to be performed in frequency domain transform along with LSB substitution algorithm which is of spatial domain. The performance of the proposed watermarking method is explained with the aid of experimental results.