2.Conference Paper (03)

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    ELECTROCARDIOGRAM BEAT CLASSIFICATION USING SUPPORT VECTOR MACHINE AND EXTREME LEARNING MACHINE
    (Springer Link, 2014) Banupriya, C V; Karpagavalli, S
    The Electrocardiogram (ECG) is of significant importance in assessing patients with abnormal activity in their heart. ECG Recordings of the patient taken for analyzing the abnormality and classify what type of disorder present in the heart functionality. There are several classes of heart disorders including Premature Ventricular Contraction (PVC), Atrial Premature beat (APB), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Paced Beat (PB), and Atrial Escape Beat (AEB).To analyze ECG various feature extraction methods and classification algorithms are used. The proposed work employed discrete wavelet transform (DWT) in feature extraction on ECG signals obtained from MIT-BIH Arrhythmia Database. The Machine Learning Techniques, Support Vector Machine (SVM) and Extreme Learning Machine (ELM) have been used to classify four types of heart beats that include PVC, LBBB, RBBB and Normal. The Performance of the classifiers are analyzed and observed that ELM-Radial Basis Function Kernel taken less time to build model and out performs SVM in predictive accuracy.
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    MAMMOGRAM CLASSIFICATION USING EXTREME LEARNING MACHINE AND GENETIC PROGRAMMING
    (IEEE, 2014-10-16) Menaka, K; Karpagavalli, S
    Mammogram is an x-ray examination of breast. It is used to detect and diagnose breast disease in women who either have breast problems such as a lump, pain or nipple discharge as well as for women who have no breast complaints. Digitized mammographic image is analysed for masses, calcifications, or areas of abnormal density that may indicate the presence of cancer. Automated systems to analyse and classify the mammogram images as benign or malignant will drive the medical experts to take timely clinical decision. In this work, the mammogram classification task carried out using powerful supervised classification techniques namely Extreme Learning Machine with kernels like linear, polynomial, radial basis function and Genetic Programming. The various task involved in this work are image preprocessing, feature extraction, building models through training and testing the classifier. The two types of mammogram image, Benign and Malignant are considered in this work and 50 images for each type collected from Mini MIAS database. Selection of Region of Interest (ROI) from the original image and Adaptive Histogram Enhancement are applied on the mammogram image before extracting the intensity histogram and gray level co-occurrence matrix features. In the dataset, for training 80% of the data are used and for testing 20% of data are used. Models are built using Extreme Learning Machine and Genetic Programming. The performances of the models are tested with test dataset and the results are compared. The predictive accuracy and training time of the classifier Genetic Programming is substantially better than the classifier built using Extreme Learning Machine with kernels linear, polynomial and radial basis function.
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    ENHANCED SECURE MULTIPATH ROUTING SCHEME IN MOBILE ADHOC AND SENSOR NETWORKS
    (IEEE, 2014-12-01) Anna Saro, Vijendran; Viji Gripsy, J
    Routing in Mobile ad hoc Sensor Network (MASNet) have become a dynamic research area owing to its impact on various science and engineering applications. A secure multipath algorithm has been presented in this research which facilitates the nodes in MASNet to execute on-demand discovery and forming set of paths. An Enhanced Secure Multipath Routing (ESMR) is formed with an adaptive behaviour on route discovery and route maintenance phase. There are two algorithms were presented in the route discovery and route maintenance phase known as DMPR and QUAD. This proposal messages are broadcasted only to limited nodes using QUAD scheme which can efficiently reduce the time to establish the path between source and destination nodes. This paper investigates the proposed algorithms with SeMuRAMAS algorithm. To achieve efficient, secure and reliable multi-path routing for MASNETs, propose a routing mechanism, which allows nodes in MASNet to perform an on-demand discovery and generation of a set of paths, based on Dynamic MPR (DMPR) protocol for route discovery process A set of security mechanisms, based on the utilization of Watchdog and digital signature, are used to protect the route discovery process. The simulation results indicate that the proposed approach provides significant performance with lesser overhead, energy efficient and better network lifetime.