Volume 07 Issue 02, March 2019

Reversible Data Hiding in Encrypted Image Based on Histogram Shifting Method and RIEHM

Rakhi Lande, Richa Singh

Page no:01-05


Histogram shifting method using image data hiding or image encryption. This technology has advanced and almost of the people like using the internet because the primary medium to transfer information from one end to another end overall the world. The information or data transfer one end to another end using internet very easy, quick and correct. But different issues with sending information or data over the internet are that the security threat. In this process non-public or confidential information will be hacked are modified original information or data. Existing method existing method based on block shifting so image is visible and low robustness. It is a very important requirement information security and it is also important requirement transfer information through internet and safety. There are several analysis process techniques related with internet security likes image data hiding, watermarking, cryptography, and steganography. Our proposed method a robust Image encryption histogram method (RIEHSM). .Proposed method a histogram is the generalized ways for image data hiding and improves robustness of encrypted image. Our proposed method enhances the standard of the encrypted image and data or information hiding. It is good security & privacy and data image recovery. In information hiding in encrypted image and highest robustness, so security of encrypted image also as maintaining the standard of original image during transfer and exchange of original image or image data.


Mohd. Naveen Maansoori, Dharmendra Kumar Singh

Page no:06-09


There are varied applications that need actual position of the occurring event in a very Wireless device Network (WSN) with low location computation value. A study within the field of mobile wireless device networks. The evolution of mobile wireless device networks, with advancements in hardware style, communication protocols, resource potency, and different aspects. Additionally if some nodes have quality (either anchor or target), correct localization method becomes quite difficult. This paper proposes a technique supported swarm intelligence for locating nodes in moving anchors WSN surroundings that is computationally efficient. The simulation primarily based localization is finished for fourteen counts at that the anchor nodes have totally different positions because of quality. The advantage of the rule employed in this paper is that there's just one anchor is needed for the localization of a target node (no would like for 3 anchors). the only anchor used for the localization of a target node can create its own 2 virtual anchor nodes for localization. The nodes that are with efficiency localized with its localization error and proportion localization error are ascertained during this paper. All simulations victimization totally different eventualities are done on MATLAB software package.


Hirdesh Kumar Sahu, Dharmendra Kumar Singh

Page no:10-13


Image improvement is one in every of the tough issues in low level image method. Image improvement completely different methods like bar graph equalization, multipoint bar graph equalizations and movie part dependent distinction protecting, but of this system are not up to marks. projected technique a picture reciprocity linear perception network technique for image improvement that contains a lot of sturdy result for distinction improvement with brightness preservation. Image part reciprocity linear perception network supported curve let transform and perceptron network. Curve let transform image remodel into multi-resolution mode. it's a realize part distinction of component for the dependency of characteristic and matrix work as a weight vector for perception network and thus the perceptron network is in work to vary the load of input image or values. Image mutuality linear perceptron network for distinction improvement has applied on several photos and compared the results of our projected methodology with various image improvement methods like bar graph equalization. Absolute mean brightness error is used to measure the degree of brightness preservation. Smaller AMBE is best and Peak signal to noise quantitative relation (PSNR) is employed to measure the degree of distinction improvement, larger PSNR is best. By examination image secure encryption improvement technique exploitation bar graph equalization with supported the AMBE and PSNR. Image secure cryptography improvement have found that projected technique (PIILPNM) is best than existing technique (HE).

Image Processing and Machine Learning on Chest X-Ray Dataset

Anamika Gupta, Anurag Joshi

Page no:14-16


In this study, we conducted a performance comparison of multiple machine-learning models using a chest X-ray image dataset obtained from Kaggle. The images underwent preprocessing using various image processing techniques. We extracted both first-order and second-order texture features from the images. The extracted data was then standardised, and any outliers were removed. We applied the Redundant Feature Elimination technique to identify the most informative features. Subsequently, we applied several classification models, including Decision Trees, K-nearest neighbours (KNN), Naive Bayes, Neural Networks, and Support Vector Machines (SVM), to the refined dataset. We employed ten-fold cross-validation in our experiments to evaluate the models’ performance. Our results indicate that SVM outperformed the other models, achieving an accuracy of 89% and an F1 Score of 91%.