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.

Enhanced Digital Image Using Histogram Equalization Method and HELNN

Rohit Purohit, Dharmendra Kumar Singh

Page no:11-15

Abstract

Image improvement is one among the difficult problems in low level image process. Distinction improvement techniques are used for rising visual quality of low distinction pictures. Histogram equalization (HE) technique is one such technique used for distinction improvement. The image histogram requirement for an improvement in existing Automatic contrast improvement methodologies that are applied in many low level image process techniques has LED to usage of the many bar graph leveling techniques. During this paper, numerous techniques of image improvement through bar graph leveling are overviewed. To guage the effectiveness of the strategies illustrated, we've used the PSNR, tenengrad, and distinction as parameters. These parameters show that however the results vary on applying completely different techniques of improvement. The work is implemented on the MATLAB background. The varied techniques are reviewed.

Improved Error Minimize in WSN Based on Data Packet Delivery Analysis of Range Based Algorithms

Kapil Dev Yadav, Prof. Dharmendra Kumar Singh

Page no:16-20

Abstract

Localization error minimization based on positioning techniques and focus on moving sensor node in improving the accuracy analysis of range based location algorithms in wireless sensor networks. A sensor localization primarily based techniques awareness of the physical location for every node is needed by several wireless device network applications. The discovery of the position will be complete utilizing range measurements as well as sensor localization received signal strength in time of arrival and sensor localization received signal strength in time difference of arrival and angle of arrival. Positioning techniques supported angle of arrival info between neighbor nodes. A wireless sensor network using positioning techniques primarily based Techniques wireless sensor network nodes position estimation in area is thought as localization. Node Localization in wireless device network is very important for several applications and to seek out the position with Received Signal Strength Indicator needs variety of anchor nodes. Accessible wireless device system procedure traditional signal strength and angle of arrival primarily based localization technique for WSN .A purposed algorithm as a PA for wireless sensor network genetic algorithmic based on positioning techniques as proposed techniques. In study this paper problem that the positioning accuracy is low with minimum anchor nodes. Find the optimum location by satisfying each the factors with minimal error and best possible solution. WSNs, the closely located sensor nodes sensing and collecting the data about the same event will result in better accuracy and reduced uncorrelated noise.