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Kumar, Akshaya
- Leukemia Detection using Image Processing
Abstract Views :285 |
PDF Views:3
Authors
Affiliations
1 Department of ECE, SNS College of Engineering, Coimbatore, IN
1 Department of ECE, SNS College of Engineering, Coimbatore, IN
Source
Digital Image Processing, Vol 9, No 6 (2017), Pagination: 119-123Abstract
Leukemia is a type of cancer which causes death among human. Only its detection and diagnosis helps to increase its cure rate. Presently, identification of cancer cells or blood disorders is by inspecting the microscopic images visually. This is done by analyzing the variations in texture, geometry, colour and statistical analysis of images. This paper describes various feature extraction techniques that can be used to detect leukemia using microscopic blood sample images. Image analysis plays an important in this method. Here first the cell biology basics are discussed and then the implementation of our proposed technique is carried out. Since our aim is to provide the cheapest method, only images are used. The tool we have used for the detection of cancer cells is MATLAB.Keywords
Leukemia, Blood Cells, Edge Detection, GLCM, Gabor, Wavelet, MATLAB.References
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- Feature Based Community Detection by Extracting Facebook Profile Details
Abstract Views :231 |
PDF Views:3
Authors
Rajeswari Sridhar
1,
Akshaya Kumar
1,
S. Bagawathi Roshini
1,
Ramya Kumar
1,
Sundaresan
1,
Suganthini Chinnasamy
1
Affiliations
1 Department of Computer Science and Engineering, Anna University, Chennai, IN
1 Department of Computer Science and Engineering, Anna University, Chennai, IN
Source
ICTACT Journal on Soft Computing, Vol 8, No 4 (2018), Pagination: 1706-1713Abstract
The rise of social networks had marked the revolution and transformation of human relationships and the information age. Social networks, Facebook in specific, have more than a billion daily active users which means petabytes of data are generated every second and there are so many social interactions occurring simultaneously. Community detection revolves around the study of these social interactions and common interests to derive the most efficient method of communication to specialized groups. Considering a preferred set of features such as the posts, likes, education background and the location of users for an optimal data structure, the selection of significant users for community analysis is implemented with the unique approach to investment score and dynamic threshold allocations for the graph creation. The community detection process focuses on the analysis of cliques and map-overlay. The emphasis on the detection of overlapping communities enhances the analysis of community relationships.Keywords
Community Detection, Data Structure, Link Weights, Influence Metric, Cliques, Map Overlay.References
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