Refine your search
Collections
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Divya, A.
- A Wavelet Transform Based Watermarking Algorithm for Protecting Copyrights of Digital Images
Abstract Views :194 |
PDF Views:0
Authors
A. Divya
1,
H. K. Priya
2
Affiliations
1 Depatment of Electronics and Communication Engineering, Dr. Ambedkar Institute of Technology, IN
2 Department of Telecommunication Engineering, Dr. Ambedkar Institute of Technology, IN
1 Depatment of Electronics and Communication Engineering, Dr. Ambedkar Institute of Technology, IN
2 Department of Telecommunication Engineering, Dr. Ambedkar Institute of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 4, No 1 (2013), Pagination: 657-660Abstract
This paper proposes an algorithm of Digital Watermarking based on Biorthogonal Wavelet Transform. Digital Watermarking is a technique to protect the copyright of the multimedia data. The position of the watermark can be detected without using the original image by utilizing the correlation between the neighbours of wave co-efficient. The strength of Digital watermark is obtained according to the edge intensities resulting in good robust and Imperceptible. Results show that the proposed watermark algorithm is invisible and has good robustness against common image processing operations.Keywords
Digital Watermark, Biorthogonal Wavelet Transform, Correlation.
- Real Time Dengue Prediction Using Machine Learning
Abstract Views :519 |
PDF Views:0
Authors
A. Divya
1,
S. Lavanya
2
Affiliations
1 Assistant Professor, Department of CSE, IFET College of Engineering, Villupuram, IN
2 UG Scholar, Department of CSE, IFET College of Engineering, Villupuram, IN
1 Assistant Professor, Department of CSE, IFET College of Engineering, Villupuram, IN
2 UG Scholar, Department of CSE, IFET College of Engineering, Villupuram, IN
Source
Indian Journal of Public Health Research & Development, Vol 11, No 2 (2020), Pagination: 406-412Abstract
Context: Dengue is generally spreading the endemic zones for atmosphere zones. In a whole world, transmitted to an individual by an Aedes Aegyptus mosquito, dengue load in India is expanding at an upsetting rate. The commitments of expanded versatility, both vector and human populaces, urbanization and atmosphere changes are the most critical factors to clarify the expanding episode of dengue. Generally, the different calculations looked at, it was wasteful to evaluate the exactness for early dengue illness expectation. The recommended framework is to build up an application for Smart Prognosis Dengue (SPD) Model for AI development to foresee constant Dengue illness. It will continue with unmistakable AI approaches going from basic classifiers like Decision Tree, Logistic Regression. Thus, the Logistic Regression Algorithm gives the most extreme exactness precision will analyse for the dengue expectation. By utilizing both the equipment and programming setup, it joins the AI ideas with the expectation calculation and furthermore gives the framework can be altered to produce risk alert and area explicit forecasts.Keywords
Machine Learning, Logistics Regression Algorithm, Raspberry Pi 3, GSM Module, GPS Module.- An Efficient Vector Quantization Based Image Compression using Fruit Fly Algorithm
Abstract Views :144 |
PDF Views:1
Authors
A. Divya
1,
Dr. S. Sukumaran
1
Affiliations
1 Department of Computer Science, Erode Arts and Science College, Erode, Tamil Nadu, IN
1 Department of Computer Science, Erode Arts and Science College, Erode, Tamil Nadu, IN
Source
Digital Signal Processing, Vol 12, No 1 (2020), Pagination: 16-19Abstract
Image compression is a searing research topic because of the large-scale increase in multimedia applications. The goal of the image compression is not only to reduce the quantity of bits needed to represent the images but also to get used to the image quality in accordance with the users’ requirements. The existing methodology CSGRVQ is evaluated by the performance of the parameter. Fruity Fly (FF) optimization algorithm proposed to further improving the FF-GRVQ. Extensive experiment demonstrates our proposed FF-GRVQ algorithm outperforms existing algorithm in term of quantization accuracy and computation accuracy.