National Journals

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/165

Browse

Filters

2010 - 20153

Settings

Start date: 2010End date: 2019

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    AN EFFECTIVE CLASSIFICATION OF HEART RATE DATA USING PSO-FCM CLUSTERING AND ENHANCED SUPPORT VECTOR MACHINE
    (IJST, 2015) R, Kavitha; T, Christopher
    Background/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 approaches
  • No Thumbnail Available
    Item
    DATA WAREHOUSE AUTOMATION-A REVIEW
    (CIIT JOURNALS, 2010) A S, Kavitha; R, Kavitha
    Business 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.
  • No Thumbnail Available
    Item
    DATA WAREHOUSE AUTOMATION- A REVIEW
    (CIIT International Journal of Data Mining and Knowledge Engineering, 2010-10) A S, Kavitha; R, Kavitha
    Business 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.