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Articles by K.N. Sivabalan
Total Records ( 2 ) for K.N. Sivabalan
  K.N. Sivabalan and D. Gnanadurai
  The applications of Visual Inspection System is used in commercial and industry like a wide spectrum. There are many algorithms propounded for defect detection in textile fabrics. This research work is related with such an algorithm of identifying defects in textile fabric. In this study, researchers have used wavelets for eliminating the texture background of the images for isolating the defects. Later an algorithm is deployed based on pixel homogeneity and intensity variation to segment the defects. This is one of the algorithm which identifies the texture defects based on non texture techniques. The algorithm is suitable for high texture background images. The results of the algorithm is compared with Morphological Analysis Method and wavelet Reconstruction Method. The algorithm is capable of detecting any broken edges in the fabric pattern.
  P. Senthil Pandian , K. Karthikeyan and K.N. Sivabalan
  Enormous amount of informationís are gathered and viewed through world wide web by different users. The user practices their views by entering hypertext credentials by internet with a large repository of web pages and web usage mining process is essential for efficient web site management, personalization, business and support services and network traffic flow analysis, etc., web page contains images, text, videos and other multimedia and web log file holds the information of the user accesses in the websites. The log file shall have some noisy and ambiguous data which may affect the data mining process and large quantity of web traffic should be handled effectively to acquire desired information. So the log file should be preprocessed to improve the quality of data. Preprocessing consists of data cleaning and data filtering, user identification and session identification. Two sets of log files are collected and processed to obtain experimental results. This study presents a framework for user and session preprocessing and clustering with Hidden Damage Data algorithm (HDD) and also analyzes the navigational behavior of users through an enhanced Conviction Frequent Pattern Mining Algorithm (CFPMA) to identify frequent patterns in web log data. The experimental result shows that the proposed technique achieves low execution time and higher accuracy when compared with the other existing methods.
 
 
 
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