Volume 06 Issue 03, MAY 2018

A REVIEW ON DIGITAL IMAGE DATA SECURE SYSTEM BASED ON HISTOGRAM TECHNIQUE

Imroze Aslam, Prof. Ratan Singh Rajput

Page no:01-04

Abstract

Digital image data secure techniques have recently grows area because in this field great awareness due to secure image data or its importance for a large number of multimedia applications. Digital images data are increasingly transmitted over non-secure multimedia channels or Internet. Some important area like military, medical and quality control images data must be protected against attempts to important work. Important work could corrupt image data problem insecure important image data. To protect the authenticity of multimedia im-ages, several approaches have been proposed. Encryption is used to transmit data securely in open networks. Infor-mation contents may be image data. Encryption of text or images, which cover the highest percentage of the multi-media, is most important during secure transmission of information. There are so many different techniques that should be used to protect confidential image data from unauthorized access. The three most important factors of image data secure design imperceptibility or undetectabil-ity, capacity, and security as a performance measure for image distortion due to image data embedding, the well-known peak-signal-to noise ratio. It compute peak signal to noise ratio between two images, in decibels unit if im-age. This ratio is often used as a quality measurement be-tween the original image data and a secure hard image data. Main parameter finds higher the PSNR, image data the better the quality of the hard or reconstructed image.

Improved Performance Analysis Based on Self-Organizing Map and PAKS Using Health Care Data

Anushka Pandey, Prof. Rajesh Nigam

Page no:05-10

Abstract

Improving performance analysis based on self-organizing map and proposed algorithm k-means with self-organizing map (PAKS) Using Health Care Data. The use of self-organizing map in neural network is very wide in data mining due to some feature like parallel performance, Self-organizing adaptive, robustness and fault tolerance. Data mining processing model based on task they achieve some process like association rules, clustering, prediction and classification. Neural network is used to find pattern in data. The grouping of self-organizing map based on neural network model and data mining method can greatly increase the efficiency data mining methods. In this process using general data, health care professionals, pharmaceutical companies, and medical specialty researchers and public health agencies to share, discuss, inquire, and report health care data exploitation early and innovative tools. Background support health care professionals and pharmaceutical corporations to face, observation and coverage the adverse events and proposed scheme can be more efficient than the common ground schemes for health care data recovery and identical time improve health care outcomes. Self-organizing map is a type of mathematical cluster analysis which particularly well suited for recognizing and classifying features in complex, multidimensional data. This paper proposes an improved Self-organizing map clustering algorithm which based on neighborhood mutual information correlation measure. Our propose approach PAKS is better as compare self-organizing map .because PAKS is minimize flat in dataset and improve accuracy of dataset.