Institution
Kongu Engineering College
About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.
Topics: Computer science, Cluster analysis, Control theory, Response surface methodology, Wireless sensor network
Papers published on a yearly basis
Papers
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TL;DR: While comparing three algorithms, SACOP has higher detection probability of malicious nodes at the expense of increased storage and communication overheads over EMABRD and FZKA, and among theThree algorithms, EMAbrD is better in terms of overheads and SACop is betterIn terms of detection probability.
14 citations
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TL;DR: In this article, a nebulizer assisted spray pyrolysis technique was employed to deposit the CZTS absorber thin films on bare soda lime glass substrate at a temperature of 350°C without annealing and sulfurization.
14 citations
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TL;DR: In this article, the surface and optical properties of undoped and Cu doped ZnO nanostructures were characterized by SEM, EDAX, FTIR, UV-vis and XRD analysis.
14 citations
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TL;DR: Fetching of Real time data and manipulation addresses the flexibility issue of hardware based Traffic Light Control system and designed Library Management system enhances ease of tracking.
14 citations
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TL;DR: A novel framework for the detection of mammographic masses that leads to early diagnosis of breast cancer with Crow search optimization based Intuitionistic fuzzy clustering approach with neighborhood attraction (CrSA-IFCM-NA) for identifying the region of interest.
Abstract: Objective: Generally, medical images contain lots of noise that may lead to uncertainty in diagnosing the abnormalities. Computer aided diagnosis systems offer a support to the radiologists in identifying the disease affected area. In mammographic images, some normal tissues may appear to be similar to masses and it is tedious to differentiate them. Therefore, this paper presents a novel framework for the detection of mammographic masses that leads to early diagnosis of breast cancer. Methods: This work proposes a Crow search optimization based Intuitionistic fuzzy clustering approach with neighborhood attraction (CrSA-IFCM-NA) for identifying the region of interest. First order moments were extracted from preprocessed images. These features were given as input to the Intuitionistic fuzzy clustering algorithm. Instead of randomly selecting the initial centroids, crow search optimization technique is applied to choose the best initial centroid and the masses are separated. Experiments are conducted over the images taken from the Mammographic Image Analysis Society (mini-MIAS) database. Results: CrSA-IFCM-NA effectively separated the masses from mammogram images and proved to have good results in terms of cluster validity indices indicating the clear segmentation of the regions. Conclusion: The experimental results show that the accuracy of the proposed method proves to be encouraging for detection of masses. Thus, it provides a better assistance to the radiologists in diagnosing breast cancer at an early stage.
14 citations
Authors
Showing all 2001 results
Name | H-index | Papers | Citations |
---|---|---|---|
Thalappil Pradeep | 76 | 581 | 24664 |
Kumarasamy Thangaraj | 47 | 361 | 11869 |
Pagavathigounder Balasubramaniam | 46 | 268 | 6935 |
J. Prakash Maran | 34 | 56 | 3636 |
S. Saravanan | 30 | 209 | 3308 |
Rathanasamy Rajasekar | 23 | 86 | 2142 |
V. Sivakumar | 23 | 93 | 2265 |
K. Thirugnanasambandham | 21 | 31 | 1759 |
Subramaniam Shankar | 20 | 104 | 1510 |
P. Sivakumar | 19 | 132 | 1464 |
N. Sivarajasekar | 18 | 60 | 1025 |
S. Selvakumar | 18 | 68 | 1155 |
Zaharias D. Zaharis | 17 | 128 | 1179 |
P. Balasubramanie | 16 | 27 | 469 |
P. N. Palanisamy | 16 | 47 | 754 |