Department of Computer Science (UG)
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Item AN EFFECTIVE CLASSIFICATION OF HEART RATE DATA USING PSO-FCM CLUSTERING AND ENHANCED SUPPORT VECTOR MACHINE(IJST, 2015) R, Kavitha; T, ChristopherBackground/Objectives: Heart Rate Variability is an essential feature which decides the condition of human heart. ECG is used as diagnostic tool to access the electrical function of the heart. Methods/Statistical Analysis: The nine linear and nonlinear features are derived from the HRV signals. The feature extraction is carried out with the help of Particle Swarm Optimization (PSO) for data reduction. In proposed scheme Fuzzy C-Means (FCM) clustering and classifier integrated to enhance the accuracy result for ECG beat classification. Findings: The Enhanced SVM classifier classifies the heart rate data. Enhanced SVM classifier groups the linear and non-linear parameters as inputs, which are derived from the HRV signal. The denoise signals are classified and identifies the pattern for better classification of ECG signal. Application/Improvements: The proposed scheme is experimented with the assistance of the most commonly used MIT-BIH arrhythmia database and adequate results were obtained with an accuracy level of 98.38% than the other well-known approachesItem DATA WAREHOUSE AUTOMATION-A REVIEW(CIIT JOURNALS, 2010) A S, Kavitha; R, KavithaBusiness enterprises invest lots of money to develop data warehouse that gives them real, constant and up to date data for decision making. To keep data warehouse update, traditionally, data warehouses are updated periodically. Periodic updates make a delay between operational data and warehouse data. These updates are triggered on time set; some may set it to evening time when there is no load of work on systems. This fixing of time does not work in every case. Many companies run day and night without any break, then in these situations periodic updates stale warehouse. This delay depends upon the periodic interval, as interval time increase the difference between operational and warehouse data also increase. The most recent data is unavailable for the analysis because it resides in operational data sources. For timely and effective decision making warehouse should be updated as soon as possible. Extraction, Transformation and Loading (ETL) are designed tools for the updating of warehouse. When warehouse is refreshed for the update purpose, it often gets stuck due to overloading on resources. Perfect time should be chosen for the updating of warehouse, so that utilize our resources can be utilized efficiently. Warehouse is not updated once, this is cyclic process. Here this paper is introducing automation for ETL , the proposed framework will select best time to complete the process, so that warehouse gets updated automatically as soon as resources are available without compromising on data warehouse usage.Item CLASSIFICATION OF HEART RATE DATA USING BFO-KFCM CLUSTERING AND IMPROVED EXTREME LEARNING MACHINE CLASSIFIER(Sri Sakthi Institute of Enginerring and Technology, 2016-01-07) R, Kavitha; T, ChristopherThe Electrocardiogram is a tool used to access the electrical recording and muscular function of the heart and in last few decades it is extensively used in the investigation and diagnosis of heart related diseases. It must be noted that the heart rate fluctuates not only because of cardiac demand, however is also influenced as a result of the occurrence of cardiac disease and diabetes. In addition, it has been shown that Heart Rate Variability (HRV) may well be utilized as an early indicator of cardiac disease susceptibility and the existence of diabetes. As a result, the HRV can be exercised for early clinical test of these diseases. Most existing systems make use of Support Vector Machine (SVM), owing to the generalization performance, it is not sufficient for the accurate classification of heart rate data. In order to overcome this complication, Improved Extreme Learning Machine (IELM) classifier is used, to obtain the best parameter value and best feature subset through the use of Bacterial Foraging Optimization (BFO) that feed the classifier. Here in this work, features of linear and nonlinear are extracted from the HRV signals. Following the preprocessing, feature extraction is done effectively together with feature selection with the assistance of BFO for the purpose of data reduction. Subsequently, proposed a scheme to integrate Kernel Fuzzy C-Means (KFCM) clustering and classifier to adequately enhance the accuracy result for ECG beat classification. The accuracy result for classification of heart rate data is shown in the proposed scheme.Item PREDICTING ACCURACY IN ECG SIGNAL CLASSIFICATION: A COMPARATIVE METHOD FOR FEATURE SELECTION(Journal of Advanced Research in Dynamical and Control Systems, 2018) R, KavithaIn this paper, new modified techniques are being used to choose the most relevant feature which is weighed against the typical techniques and offered to the classifier for exact and appropriate prediction of the diseases. The improved techniques CBMPSO, MBFO, BB-BAT are used for feature selection. These techniques determine best features which are provided to the ESVM, IELM and SADE ELM classifier and the effect are weighed against the standard algorithm. It has been proved that the improved method provides better accuracy in comparison to standard SVM and ELM algorithm.Item HEART RATE VARIABILITY CLASSIFICATION USING SADE-ELM CLASSIFIER WITH BAT FEATURE SELECTION.(ICTACT, 2017) R, Kavitha; T, ChristopherThe electrical activity of the human heart is measured by the vital bio medical signal called ECG. This electrocardiogram is employed as a crucial source to gather the diagnostic information of a patient’s cardiopathy. The monitoring function of cardiac disease is diagnosed by documenting and handling the electrocardiogram (ECG) impulses. In the recent years many research has been done and developing an enhanced method to identify the risk in the patient’s body condition by processing and analysing the ECG signal. This analysis of the signal helps to find the cardiac abnormalities, arrhythmias, and many other heart problems. ECG signal is processed to detect the variability in heart rhythm; heart rate variability is calculated based on the time interval between heart beats. Heart Rate Variability HRV is measured by the variation in the beat to beat interval. The Heart rate Variability (HRV) is an essential aspect to diagnose the properties of the heart. Recent development enhances the potential with the aid of non-linear metrics in reference point with feature selection. In this paper, the fundamental elements are taken from the ECG signal for feature selection process where Bat algorithm is employed for feature selection to predict the best feature and presented to the classifier for accurate classification. The popular machine learning algorithm ELM is taken for classification, integrated with evolutionary algorithm named Self-Adaptive Differential Evolution Extreme Learning Machine SADEELM to improve the reliability of classification. It combines Effective Fuzzy Kohonen clustering network (EFKCN) to be able to increase the accuracy.Item EFFICIENT MYOCARDIAL SEGMENTATION USING LOCAL PHASE QUANTIZATION (LPQ) AND AUTOMATIC SEGMENTATION TECHNIQUE(IJCA, 2016) A, Gayathri; R, KavithaThe low and high arrhythmic risk of myocardial infarction is classified based on size, location, and textural information of scarred myocardium. These features are extracted from late gadolinium (LG) enhanced cardiac magnetic resonance images (MRI) of post-MI patients. The risk level caused by features are evaluated by using various classifiers including knearest neighbor (k-NN), support vector machine (SVM), decision tree, and random forest classifier. Here, high risk patients are separated from low risk patients based on the decision made by Left Ventricular Ejection Fraction (LVEF) and biomarkers based on scar characteristics. However, additional image processing techniques are needed to have clear visibility for differentiating scar texture between two risk groups. In order to maintain balanced risk groups, synthetic minority over-sampling technique (SMOTE) is used in existing system. But accuracy is limited further because of imbalance risk groups and manual segmentation of classifier. So to improve accuracy, proposed method uses automatic segmentation and Local Phase Quantization (LPQ)Item A COMPARATIVE STUDY ON PERFORMANCE PARAMETER FOR CARDIOVASCULAR DISEASE USING VARIOUS IMAGING TECHNIQUES(IJSRSET, 2016) A, Gayathri; R, KavithaHeart disease is one of the most leading issues of death. Hence to predict this disease in advance, early detection and diagnosis is required. This plays a major role in disease severity identification, predicts the outcome of disease and helps to improve the patient management. Though there are several cardiac imaging modalities used for this purpose, less-invasive imaging modalities like coronary CT angiography, cardiac magnetic resonance imaging, cardiac radionuclide imaging such as SPECT and PET modalities are widely used for assessment of heart diseases. This study works on applications of above mentioned imaging modalities in assessing various heart diseases and provides comparison among them.Item AN IMPROVED PRIVACY POLICY INFERENCE OVER THE SOCIALLY SHARED IMAGES WITH AUTOMATED ANNOTATION PROCESS(Pearl Media Publications, 2015) J, Sangeetha; R, KavithaUsage of social media’s increased considerably in today world which enables the user to share their personal information like images with the other. This improved technology leads to privacy violation where the users are sharing the large volumes of images across more number of peoples. To provide security for the information, automated annotation of images are introduced which aims to create the meta data information about the images by using the novel approach called Semantic annotated Markovian Semantic Indexing(SMSI) for retrieving the images. The proposed system automatically annotates the images using hidden Markov model and features are extracted by using color histogram and Scale-invariant feature transform (or SIFT) descriptor method. After annotating these images, semantic retrieval of images can be done by using Natural Language processing tool namely Word Net for measuring semantic similarity of annotated images in the database. Experimental result provides better retrieval performance when compare with the existing systemItem CLASSIFICATION OF HEART RATE USING BACK PROPAGATION NEURAL NETWORKS(Science and Research support Center, Republic of Korea, 2015) R, Kavitha; T, ChristopherA condition of abnormal electrical activity in the heart which is a threat to humans is shown by this electrocardiogram. It is a representative signal containing information about the condition of the heart. The of the P-QRS-T wave shape and size and their time intervals between its various peaks these are all contain useful information about the nature of disease affecting the heart. This paper presents a technique to examine electrocardiogram (ECG) signal, take out the features for the heart beats classification. Collect data from MIT-BIH database. The heart rate is used as the base signal from which certain parameters are extracted and presented to the BPN for classification.Item A STUDY ON ECG SIGNAL CLASSIFICATION TECHNIQUES(Foundation of Computer Science, 2014-01-01) R, Kavitha; T, ChristopherThe abnormal condition of the electrical activity in the heart is using electrocardiogram shows a threat to human beings. It is a representative signal containing information about the condition of the heart. The P-QRS-T wave shape, size and their time intervals between its various peaks contain useful information about the nature of disease affecting the heart. This paper presents a technique to examine electrocardiogram (ECG) signal, by taking the features form the heart beats classification. ECG Signals are collected from MIT-BIH database. The heart rate is used as the base signal from which certain parameters are extracted and presented to the network for classification. This survey provides a comprehensive overview for the classification of heart rate