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George A.,Aloy Labs | Valarmathi I.R.,Aloy Labs | Swamy S.M.,Aloy Labs
Journal of Theoretical and Applied Information Technology | Year: 2014

In this contemporary world of ours, a lot of people have tried to store their multimedia datasets as a contour of binary waves. During transmission, if a prohibited course of action occurs during an intermittent stage of transmission it could lead to delay in accessing data for individuals who have been provided authorization. In order to eliminate this undesired course of action, an efficient method has been adopted. Steganography is an efficient technique that can eliminate this undesired course of action and can be utilized for writing an eclipse missive. In this paper we have proposed an efficient optimal robust video steganography technique using the Biorthogonal Wavelet Transform (BWT) that has been incorporated with a hybrid model of the Artificial Bee Colony (ABC) with Genetic Algorithm (GA).The BWT is utilized to split the image into Low-Low (LL), Low-High (LH), High-Low (HL) and High-High (HH). The optimization technique ABC and GA are then utilized to attain best fitness values in the embedding and extraction processes. Analysis on the proposed technique is carried out with respect to the Peak signal to Noise ratio (PSNR) and the Normalized Correlation (NC). Experimental results show that the proposed technique can achieve good imperceptibility and robustness for an image. © 2005 - 2014 JATIT & LLS. All rights reserved.

Dennis B.,Aloy Labs. | Muthukrishnan S.,Aloy Labs.
Applied Soft Computing Journal | Year: 2014

A Genetic Fuzzy System (GFS) is basically a fuzzy system augmented by a learning process based on a genetic algorithm (GA). Fuzzy systems have demonstrated their ability to solve different kinds of problems in various application domains. Currently, there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities. Two of the most successful approaches to hybridize fuzzy systems with learning and adaptation methods have been made in the realm of soft computing. The GA can be merged with Fuzzy system for different purposes like rule selection, membership function optimization, rule generation, co-efficient optimization, for data classification. Here we propose an Adaptive Genetic Fuzzy System (AGFS) for optimizing rules and membership functions for medical data classification process. The primary intension of the research is 1) Generating rules from data as well as for the optimized rules selection, adapting of genetic algorithm is done and to explain the exploration problem in genetic algorithm, introduction of new operator, called systematic addition is done, 2) Proposing a simple technique for scheming of membership function and Discretization, and 3) Designing a fitness function by allowing the frequency of occurrence of the rules in the training data. Finally, to establish the efficiency of the proposed classifier the presentation of the anticipated genetic-fuzzy classifier is evaluated with quantitative, qualitative and comparative analysis. From the outcome, AGFS obtained better accuracy when compared to the existing systems. © 2014 Elsevier B.V. All rights reserved.

Rajakumar B.R.,Aloy Labs | George A.,Aloy Labs
2012 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2012 | Year: 2012

Genetic algorithm is a promising heuristic search algorithm, which searches the solution space for optimal solution using the genetic operations. Mutation is one among the genetic operators that plays a vital role in searching the solution space. This paper proposes a new adaptive mutation technique to improve the performance of genetic algorithm. The proposed technique intends to mutate the genes in such a way that the mutation aids both global and local searching options. This leads to faster convergence rather than the conventional techniques. The comparative results show that the proposed mutation technique exhibits a drastic performance improvement over the conventional static mutation techniques. © 2012 IEEE.

Binu D.,Aloy Labs | Selvi M.,Aloy Labs
Journal of Medical Imaging and Health Informatics | Year: 2015

Optimization algorithms are applied on Fuzzy system for various purposes like membership function optimization, co-efficient optimization, rule generation, rule selection, etc. Here we amalgamate Bat algorithm with fuzzy classifier to generate optimized rules and membership functions effectively. The key contributions in our classifier are (i) generating and selecting optimized rules using bat algorithm, (ii) simplifying the design and discretizing process in membership function, (iii) formulating a fitness function based on frequency of occurrence of the rules in the learning data. The proposed Bat algorithm based fuzzy classifier is subjected to quantitative and qualitative analysis for performance comparisons. Experimental results demonstrate that BFC has achieved 75.21% accuracy when Lung cancer data is used. Moreover, BFC has accomplished 76.67% accuracy for Indian Liver data. Copyright © 2015 American Scientific Publishers.

Binu D.,Aloy Labs
Expert Systems with Applications | Year: 2015

Clustering finds various applications in the field of medical and telecommunication for unsupervised learning which is much required in expert system and its application. Various algorithms have been developed to clustering for the past fifty years after the introduction of k-means clustering. Recently, optimization algorithms are applied for clustering to find optimal clusters with the help of different objective functions. Accordingly, in this research, clustering is performed using three newly designed objective functions along with four existing objective functions with the help of optimization algorithms like, genetic algorithm, cuckoo search and particle swarm optimization algorithm. Here, three different objective functions are designed including the cumulative summation of fuzzy membership and distance value with normal data space, kernel space as well as multiple kernel space. In addition to the existing seven objective functions, totally, 21 different clustering algorithms are discussed and the performance is validated with 16 different datasets which are synthetic, small and large scale real data. The comparison is made with five different evaluation metrics to validate the effectiveness and efficiency. From the research outcome, the suggestion is presented to select a suitable algorithm among 21 algorithms for a particular data and results proved that the effectiveness of cluster analysis is mainly dependent on objective function and the efficiency of cluster analysis is based on search algorithm. © 2015 Elsevier Ltd All rights reserved.

Swamy S.M.,Aloy Labs | Rajakumar B.R.,Aloy Labs | Valarmathi I.R.,Aloy Labs
IET Seminar Digest | Year: 2013

In recent decades, depletion of fossil fuels and its demand emerge as renewable energy in the field of power generation. Amid eco-friendly based renewable energy, wind and photovoltaic play a vital role in power generation. Nonetheless, this form of power generation needs more advancement to retrieve optimal power flow under economical conditions. This paper aims to predict optimal sizing for hybrid wind and photovoltaic (PV) power generation under minimized cost. This optimal sizing of hybrid Wind-PV is accomplished by satisfying the average annual load demand. This process happens via opposition based genetic algorithm with Cauchy mutation (OGA-CM) and the proposed OGA-CM performance measure is compared with Opposition based Genetic Algorithm and Genetic Algorithm. The result shows that our proposed OGA-GA produces superior result than those of the other two. The overall computation process is done in the working platform of MATLAB R2013.

Binu D.,Aloy Labs | George A.,Aloy Labs
Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013 | Year: 2013

In recent times, clustering has been well known for various researchers due to various applications in most of the fields like, telecommunication, networking, biomedical domain and so on. So, various attempts have been already made by the researchers to develop a better algorithm for clustering. One of the procedures well known among the researchers is optimization that has been effectively utilized for clustering. In most of the clustering, the objective function is to minimize the intra cluster distance among the data points. Here, we have made significant trial in developing optimization based clustering algorithm utilizing kernel-based FCM and PSO algorithm. The objective function for minimization is taken from kernel-based FCM and the same objective is solved using PSO algorithm. The algorithm developed based on these scenarios are named as, KF-PSO. At first, the input data is given to PSO algorithm and the final best cluster centers are chosen from PSO algorithm useful for grouping based on the objective of the kernel clustering. Finally, the experimentation have been performed in various datasets and from the results, we have showed that the proposed hybrid algorithm achieved 98% and 90.5 % accuracy in iris and wine dataset. © 2013 IEEE.

Rajakumar B.R.,Aloy Labs
International Journal of Computational Science and Engineering | Year: 2013

In this paper, a systematic comparative analysis is presented on various static and adaptive mutation techniques to understand their nature on genetic algorithm. Three most popular random mutation techniques such as uniform mutation, Gaussian mutation and boundary mutation, two recently introduced individual adaptive mutation techniques, a self-adaptive mutation technique and a deterministic mutation technique are taken to carry out the analysis. A common experimental bench of benchmark test functions is used to test the techniques and the results are analysed. The analysis intends to identify a best mutation technique for every benchmark problem and to understand the dependency behaviour of mutation techniques with other genetic algorithm parameters such as population sizes, crossover rates and number of generations. Based on the analytical results, interesting findings are obtained that would improve the performance of genetic algorithm. Copyright © 2013 Inderscience Enterprises Ltd.

Selvi M.,Aloy Labs | George A.,Aloy Labs
2013 4th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2013 | Year: 2013

Fingerprint image enhancement plays a vital role in fingerprint recognition process which is one of the commonly used biometrics traits. In fingerprint recognition, the initial process is enhancement. An efficient enhancement process will give high accuracy in human authentication. The quality of the fingerprint image is more important for increasing the performance of the recognition system. Our proposed methodology is designed to enhance the fingerprint image, i.e. reproduce the noise free image. The proposed method is designed to identify the noise pixel area and enhance that portion alone by using Fuzzy based filtering technique and adaptive thresholding. The proposed technique comprises of 4 stages such as 1) preprocessing 2) Fuzzy based filtering 3) adaptive thresholding 4) Morphological operation. The experimentation is done by using FVC2002 database and we obtained better PSNR when compared to the existing filtering techniques. © 2013 IEEE.

Rajakumar B.R.,Aloy Labs | George A.,Aloy Labs
2013 4th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2013 | Year: 2013

Heart disease is the most important reason of morbidity and mortality in the modern society. For that reason, it is important to have a proper diagnosis of heart disease for patients to live to tell the tale. In order to make the diagnosis system as the efficient one, heart diseases should be classified accurately. In the existing technique, the quality of the extracted rules is poor. So as to increase the quality of the extracted rules, an efficient technique should be used. In our proposed methodology, we are using firefly algorithm in Fuzzy Min-Max Neural Network. Firefly algorithm has high convergence tempo. It works individually and finds a superior position for itself in contemplation with its recent position as well as the situation of other fireflies. And it escapes from the local optima and finds a global optimum which has a smaller amount number of iterations. Since it is a robust algorithm, the classification of heart diseases can be done fastly and as a result the accuracy and performance of the proposed technique becomes encouraging. © 2013 IEEE.

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