S. Selvakumar

High payload image steganography with reduced distortion using octonary pixel pairing scheme

The crucial challenge that decides the success of any steganographic algorithm lies in simultaneously achieving the three contradicting objectives namely—higher payload capacity, with commendable perceptual quality and high statistical un-detectability. This work is motivated by the interest in developing such a steganographic scheme, which is aimed for establishing secure image covert channel in spatial domain using Octonary PVD scheme. The goals of this paper are to be realized through: (1) pairing a pixel with all of its neighbors in all the eight directions, to offer larger embedding capacity (2) the decision of the number of bits to be embedded in each pixel based on the nature of its region and not done universally same for all the pixels, to enhance the perceptual quality of the images (3) the re-adjustment phase, which sustains any modified pixel in the same level in the stego-image also, where the difference between a pixel and its neighbor in the cover image belongs to, for imparting the statistical un-detectability factor. An extensive experimental evaluation to compare the performance of the proposed system vs. other existing systems was conducted, on a database containing 3338 natural images, against two specific and four universal steganalyzers. The observations reported that the proposed scheme is a state-of-the-art model, offering high embedding capacity while concurrently sustaining the picture quality and defeating the statistical detection through steganalyzers.

Extended Finite State Machine Model-Based Regression Test Suite Reduction Using Dynamic Interaction Patterns

An EFSM (Extended Finite State Machine) model-based regression test suite (RTS) reduction method based on dynamic dependence analysis is proposed. Our approach automatically identifies the difference between the original model and the modified model as a set of elementary model modifications. This proposed method reduces the size of a given RTS by examining the various interaction patterns covered by each test case in the given RTS.

High performance reversible data hiding scheme through multilevel histogram modification in lifting integer wavelet transform

This paper proposes a digital image reversible data hiding method in integer lifting transform domain. Owing to the characteristics of the natural image statistics, the neighbor pixel values are similar mostly and hence their differences are observed to be close or equal to zero. A histogram constructed out of this difference factor is exploited for reversible data embedding. Further, data is embedded at multiple levels in the integer lifting wavelet transform domain and hence the proposed scheme facilitates higher payload capacity and exceptional perceptual quality than the conventional single level histogram based techniques. The additional information involved for restoring the cover image and the secret payload is also less compared to the conventional schemes, as the proposed method employs a single parameter called “Embedding Level” for both hiding as well as extraction. Extensive experimentation with huge database of images, five existing RDH schemes and against seven steganalysers, shows that the proposed RDH scheme outperforms other schemes and proves to be a high performance RDH scheme in terms of all the desirable features of a reversible data hiding system like high payload, imperceptible, robustness, losslessness and minimal side information.

Identification of the Risk Factors of Type II Diabetic Data Based Support Vector Machine Classifiers upon Varied Kernel Functions

In the innovation in making a data driven decision model, the technological impacts across medical data processing has been increasing rapidly. Type II diabetes is one among the majorly increasing non-communicable disease across India. Advancements in the field of medicine its therapeutic procedures have to be considered in determining the risk factor related to disease prediction and its major cause. This research work, focus towards the applicability of Support Vector Machine (SVM) classifiers for predicting the risk related to type II diabetes. The model involves the deployment of various kernel functions upon the building of SVM models. The experimental results shows that data classification using SVM upon varied kernel function has improvement over the accuracy with 86.65%, precision 76.21% and recall with 81.11% respectively. Among the kernels experimented, polynomial kernel performed better than the other kernels with increased correlation results. The variation in the kernel with model enhancements can be deployed for risk factor prediction in type II diabetes.

Ultra-HEVC using frame frequency error optimization technique for IPTV realization

Internet Protocol Television (IPTV) is an emerging network application in the internet world. One of the most reliable networks is IPTV which gives high speed for internet services. As IPTV offers many live services on user demand and it has many advantages. But still, some problem exists in the existing implementation such as degradation of quality and delay while maintaining limited frames and efficient bandwidth consumption over the network channel. The efficient bandwidth utilization is a major issue in IPTV platforms. Integrating the video processing on network platform is the challenging task in video on demand (VoD) application. This paper overcomes the drawbacks of existing IPTV by using Frame Frequency Error Optimization (FFEO) based HEVC approach which is called as U-HEVC. The FFEO method upgrades the video quality by interpolation of frames. U-HEVC delivers 50% better compression similar to the existing HEVC standard and it also provides better visual quality at half the bit rates. The Analysis of proposed U-HEVC attain better results compared to existing HEVC compression algorithms that higher number of packets get affected at different bit rate levels. In HEVC the Frame loss of 1 Mbps is 0.38%, 2 Mbps is 0.46%, 4 Mbps is 0.63% and 8 Mbps is 0.94%. When compared to the U-HEVC the Frame loss is somewhat high in HEVC. This paper presents the studies on IPTV environment based on U-HEVC using frame frequency error optimization technique.

A survey on evolutionary techniques for feature selection

The most important classification difficulty is feature selection. Feature selection is widely used data analytics and machine learning tasks and it is one of the data preprocessing tasks. It does not produce new features. It attempts diminish the attributes by removing irrelevant attributes using an efficient and robust feature selection method. If the dataset contains irrelevant attributes it may lead to poor performance of the classifier. A significant method is used to select the appropriate or relevant set of features. In this paper, several feature selection algorithms using evolutionary techniques are presented. Mostly evolutionary techniques are used for finding the relevant features corresponding to the disease.

Computing with Enhanced Test Suite Reduction Heuristic

Software computing is a goal-oriented activity requiring, benefiting from, or creating applications for commercial, network communications etc. The process of development includes designing, building and testing software systems for a wide range of purposes, processing, structuring, and managing various kinds of information. To validate the developed one, time and resource constraints are taken into account; therefore test suite reduction technique is the important stage to be followed to test the developed software. Several test suite reduction techniques are present. Our approach is a new type of test suite reduction heuristic in which we propose an algorithm for test suite reduction based on changed condition/coverage criteria (CC/CC) to test the software on satisfying complete user’s requirement with less time, cost and more coverage of requirements this increases computing efficiency.

Reducing the Size of the Test Suite by Genetic Algorithm and Concept Analysis

Test-suite reduction can provide us with a smaller set of test cases that preserve the original coverageoften a dramatically smaller set. One potential drawback with test suite reduction is that this might affect the quality of the test suite in terms of fault finding the problem and determine its effect when testing. Based on observations from our previous experimental studies on test suite reduction, we believe there is a need for optimized test suite with increase in fault detection. We examine the effectiveness of a test suite reduction process based on a combination of both concept analysis and Genetic algorithm. We also suggest a method for handling the tie between the groups in the lattice which will yield the cases that are most suitable for covering the requirements at that level. Our experimental study suggests that integrating concept analysis and Genetic algorithm has a positive impact on the effectiveness of the resulting test suites.

Test Suite Diminuition Using GRE Heuristic with Selective Redundancy Approach

A testing process involves testing the given program with the designed test cases. A testing objective has to be defined before testing the program. The test cases depend upon the testing objective of the program. As new test cases are generated over time due to software modifications, test suite sizes may grow significantly. Because of time and resource constraints for testing, test suite minimization techniques are needed to remove those test cases from a suite that, due to code modifications over time have become redundant with respect to the coverage of testing requirements for which they were generated. For reducing the cost of the test execution only efficient test cases should be taken into consideration. Existing algorithms like GRE reduces the test suite by removing redundancy that may sometimes can significantly diminish the fault detection effectiveness (FDE) of suites. We present the modified GRE heuristic with selective redundancy (GSRE) for test suite reduction that attempts to use additional coverage information of test cases to selectively retain some additional test cases in the reduced suites that are partially redundant with respect to the testing criteria used for suite minimization, with the goal of improving the FDE retention of the test suites. Our experiments show that our GSRE approach can significantly improve the FDE retention of test suites without severely affecting the extent of suite size reduction.