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Ulagamuthalvi, V.
- Detecting and Removing Duplicate Records from Multiple Web Databases
Abstract Views :156 |
PDF Views:3
Authors
Affiliations
1 Sathyabama University, Chennai, IN
1 Sathyabama University, Chennai, IN
Source
Software Engineering, Vol 3, No 5 (2011), Pagination: 208-211Abstract
The basic scenario is that the queries3 given by the users are matched and the results are got from multiple web databases. The complexity in this concept arises due to the presence of duplicate and redundant records. To solve this, an unsupervised online record matching method, UDD is used which is mainly for identifying the duplicates from the query results of multiple web databases. The duplicate records are identified and ignored. Only the original records are displayed to the user. For these purpose two classifiers namely Weighted Component Similarity Summing classifier (WCSS) & Support Vector Machine (SVM) classifier are used iteratively to find the duplicates and filter those records from multiple web databases. The other concept is to avoid duplication of websites. Generally static weightage were allocated for URLs. Instead of static weightage, the idea of dynamic weightage is introduced here. Dynamic weightage is allocated to the respective URLs to avoid unauthorized users to create duplicate sites. This proves that the UDD works much better than the existing methods.Keywords
Duplicate, Record Matching, Weightage, Database and Multiple Result.
- Automatic Segmentation of Liver Lesion from Ultrasound Images
Abstract Views :172 |
PDF Views:5
Authors
Affiliations
1 Sathyabama University, Chennai-600119, IN
2 Anna University, Chennai-600025, IN
1 Sathyabama University, Chennai-600119, IN
2 Anna University, Chennai-600025, IN
Source
Digital Image Processing, Vol 4, No 12 (2012), Pagination: 647-649Abstract
In this study, we focus on liver lesion Segmentation from ultrasound images using Region Growing algorithm with texture features parameters on spatial information. Segmentation of Ultrasound liver lesion is very challenging due to low contrast nature of image and occurrence of speckle noise. Liver cancer is the fifth most common cancer worldwide in men and eighth in women, and is one of the few cancers still on the rise. The goal of our study is that Region growing with texture parameters in gray space map on spatial information algorithm is supporting better for segmenting ultrasound liver lesion.Keywords
Ultrasound Liver Lesion, Region Growing, Thresholding, Segmentation, Gray Space Map.- A Comparative Study on Tumour Classification
Abstract Views :179 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, IN
2 Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, IN
1 Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, IN
2 Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, IN
Source
Research Journal of Pharmacy and Technology, Vol 12, No 1 (2019), Pagination: 407-411Abstract
Cancer detection is the most significant method to identify the early tumor. Enlargement of the tumor is being a huge task due to the complex characteristics of the medical images which provides high divergent, intensive and uncertain boundaries. Designing and developing computer-aided image processing systems are to help doctors improve their diagnosis and then received huge benefits over the past years. Classification is an important task within the field of computer vision. Image classification refers to the labelling of images into one of a number of predefined categories that includes image sensors, image pre-processing, object detection, object segmentation, feature extraction and object classification. Many classification techniques have been developed for image classification. The aim of literature survey is to provide a brief summary about some of common most image classification technique and comparison among them. In this survey various classification techniques are considered; Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM), Fuzzy Classification and more.Keywords
Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machine (SVM), Fuzzy Classification.References
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