C. Nagaraju

Efficient DNA-Based Cryptographic Mechanism to Defend and Detect Blackhole Attack in MANETs

This paper addresses a novel method to detect and defend against the blackhole attack and cooperative blackhole attack using hybrid DNA-based cryptography (HDC) mechanism. Moreover, the proposed method upsurge the security issue with the underlying AODV routing protocol. Eventually, this HDC is one of the high potential candidates for advanced wireless ad hoc networks, which require less communication bandwidth and memory in comparison with other cryptographic systems. The simulation results of this proposed method provide better security and network performances as compared to existing schemes.

Generation of Random Fields for Image Segmentation Using Manifold Learning Technique

The manifold learning technique is a class of machine learning techniques that converts the intrinsic geometry of the data from higher to lower dimensional representation. The manifold learning tec...

A Secure Routing Protocol against Heterogeneous Attacks in Wireless Adhoc Networks

This paper presents designing an efficient security solution that can protect wireless adhoc networks from heterogeneous attacks. Over the last few years, researchers had addressed various attacks and its countermeasures against numerous adhoc routing protocols in an isolated fashion. In this paper, initially, we modelled heterogeneous attack to investigate the weaknesses of the existing AODV routing protocols of wireless ad hoc network that exploits the vulnerabilities of adhoc environments, which will harm the system and results in a vulnerability assessment. Thus, deployment of security in these routing protocols of wireless adhoc networks against heterogeneous attack is still in their infancy and unsolved. To address the security issue against these attacks, we proposed inspired biotic hybrid cryptographic (IBHC) mechanism to defend the heterogeneous attacks. IBHC is one of the prospective methodology for sophisticated wireless adhoc network that provides efficient storage, transmission and secure against heterogeneous attack.

A Novel Approach to Adopt Scrum by an Enterprise

It is very important to understand how poor quality can bring down a software project development. In the early software developing methodologies, customer requirements are ambiguous because of which specifications can be solidified. This is due to the unbending structure of old and traditional methodologies. Over the last decade, more than a few innovative software development methodologies have been developed particularly to resolve problems in the region of software quality. Such group of methodologies is called “agile.” These methodologies mostly focus on how to maximize flexibility and minimize transparency in the process of software development. These methodologies are not only based on theory, but also rather derived from experiences of successful project teams. Based on the current trend in the software industry, software developing companies are choosing agile methodologies in which scrum is widely used. In this work, the adoption process of scrum by a software company is discussed.

Evaluation of LBP-Based Facial Emotions Recognition Techniques to Make Consistent Decisions

Decision making is one of the smouldering problems in day to day works. Human emotions play crucial role in decision-making systems. While person is in high emotion he cannot make proper decision. Robust local binary pattern (RLBP) operator is more powerful to recognize the emotions and extends the features of local binary pattern (LBP). However, there are some precincts like discriminating bright faces against dark features and vice versa and intra-class variances increase. The RLBP solves this problem by finding minimum of LBP codes and their complements. However, it miss the mark for different local structures a similar feature is obtained, weak contrast local patterns and similar strong contrast local patterns. Hence, the discriminative robust local binary pattern (DRLBP) method is proposed to retain the contrast information of image patterns next to considering both edge and texture information. Nevertheless, LBP family methods are highly sensitive to noise. To trounce these drawbacks this paper extends fuzzy rule-based DRLBP which is more robust to noise, low contrasted, uneven lighting conditions, variations in expressions and rotation variant images.

Light-Weighted DNA-Based Cryptographic Mechanism Against Chosen Cipher Text Attacks

DNA cryptography is a new cryptographic paradigm from hastily growing biomolecular computation, as its computational power will determine next generation computing. As technology is growing much faster, data protection is getting more important and it is necessary to design the unbreakable encryption technology to protect the information. In this paper, we proposed a biotic DNA-based secret key cryptographic mechanism, seeing as DNA computing had made great strides in ultracompact information storage, vast parallelism, and exceptional energy efficiency. This Biotic Pseudo DNA cryptography method is based upon the genetic information on biological systems. This method makes use of splicing system to improve security and random multiple key sequence to increase the degree of diffusion and confusion, which makes resulting cipher texts difficult to decipher and makes to realize a perfect secrecy system. Moreover, we also modeled the DNA-assembled public key cryptography for effective storage of public key as well as double binded encryption scheme for a given message. The formal and experimental analysis not only shows that this method is powerful against brute force attack and chosen cipher text attacks, but also it is very efficient in storage, computation as well as transmission.

Generation of Random Fields for Image Segmentation Based on MRF Energy Level Set Method

This research paper focuses on image segmentation by random fields generated by using MRF energy level set function. The focus of the researcher is on this technique of how MRF energy is embedded in the standard level set energy method. The connection among all the pixels and its neighborhood is established by MRF energy function, and it attempts to have all the pixels in the same region. Comparing the MRF level set technique with surviving manifold learning technique on different standard images. In comparison, the proposed technique is fast and healthy for image segmentation tough the statistical tables present the researcher the superiority of the proposed technique.

A Fast Adaptive Fuzzy Unsymmetric Trimmed Mean Filter for Removal of Impulse Noise from Digital Images

Impulse noise reduction from images is an indispensable research issue in the area of image processing. In this paper, we are proposing a fast adaptive fuzzy filter for restoring the pixels that are damaged through salt and pepper noise or impulse noise. The process of filtering consists of three stages. In first stage, we are concentrating on identifying the window dimension for processing pixel using fuzzy detector. In second stage, noise effected pixel can be identified with help of mathematical 3N rule and in the last stage, we restored noise pixel with unsymmetric trimmed mean value. In restoration process, identification of noise pixel is a complex task. This can be simplified in our proposed filtering method with effective performance of 3N rule. Experiments on standard image and medical image sets were conducted to compare our restoration algorithm with two previous competitors. The results show that our method is superior to existing algorithms considering PSNR and elapsed time. The proposed method also indicates to be strong to high ranges of noise, as excessive as 90% with conserving the key details of the image. And it is useful in many applications.

Removal of High Density Salt and Pepper Noise from the Image Using CMA

The quality of the image plays a vital role in numerous image processing applications such as medical image analysis, pattern recognition, satellite image processing, etc. One of the most important noises that affect the quality of the image is impulse noise. This noise alters the value of the pixels to either extreme. An efficient noise reduction algorithm is required to improve the quality of the image by detecting the noisy pixels and then replacing it with the appropriate value. The algorithm should have high noise reduction efficiency and computational efficiency especially when dealing with high noise density images. This paper proposes an improved algorithm which detects the noisy pixels using Cloud Model method and replaces the value of the corrupted pixel by Cloud Model Average (CMA) method, which improves computational efficiency by 2.94 % without compromising the noise reduction efficiency compared to the existing methods in the literature.

Identifying and removal of impulse noise with fuzzy certainty degree

Numerous techniques are proposed for impulse noise removal. However, they are hard to keep excellent denoising performance with high computational power. In response to this difficulty, this paper presents a new fast fuzzy filter to recognize and expel the impulse noise. First fuzzy based filter distinguishes the pixels debased by impulse noise. A fuzzy weighted mean method is applied to supplement corrupted pixel. The exploratory results demonstrate that the proposed technique gives better results contrast with other switching filters if the noise levels are beneath the 50%.