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14th International Conference on Advanced Computing and Communications, ADCOM 2006

作者： Raja, K.B. Vikas Venugopal, K.R. Patnaik, L.M. Department of Computer Science Engineering University Visvesvaraya College of Engineering Bangalore University Bangalore India Microprocessor Applications Laboratory Indian Institute of Science Bangalore India

ISBN: (纸本)142440715X

The modern steganography presents a challenging task of embedding data that should be imperceptible to the Human Visual System (HVS) and also escape the detection of powerful machine vision of computers. In this paper we present a high capacity, lossless, secure wavelet steganographic algorithm in which Payload bitstream is encrypted and embedded into the wavelet coefficients of the cover image to derive a stego-image. The payload is embedded in the Approximation band of the wavelet domain that increases its robustness. It is observed through simulations that Mean Square Error (MSE), Mean Absolute Error (MAE), Bit Error Rate (BER) and Histogram analysis that the pay load is retrieved without any errors and its performance is better than the earlier Insignificant Coefficient Replacement (ICR) technique. © 2006 IEEE.

关键词： Steganography

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Indexing XML documents using self adaptive genetic algorithms for better retrieval

Indexing XML documents using self adaptive genetic algorithm...

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8th Asia-Pacific Web Conference, APWeb 2006: Frontiers of WWW Research and Development

作者： Srinivasa, K.G. Sharath, S. Venugopal, K.R. Patnaik, Lalit M. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore - 560001 India Infosys Technologies Bangalore India Microprocessor Applications Laboratory Indian Institute of Science India

ISBN: (纸本)3540311424

The next generation of web is often characterized as the Semantic Web. Machines which are adept in processing data, will also perceive the semantics of the data. The XML technology, with its self describing and extensible tags, is significantly contributing to the semantic web. In this paper, a framework for information retrieval from XML documents using Self Adaptive Migration model Genetic Algorithms(SAGAXsearch) is proposed. Experiments on real data performed to evaluate the precision and the query execution time indicate that the framework is accurate and efficient compared to the existing techniques. © Springer-Verlag Berlin Heidelberg 2006.

关键词： XML

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An efficient fuzzy based neuro – genetic algorithm for stock market prediction

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International Journal of Hybrid Intelligent Systems 2006年第2期3卷 63-81页

作者： Srinivasa, K.G. Venugopal, K.R. Patnaik, L.M. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore 560001 India Microprocessor Applications Laboratory Indian Institute of Science Bangalore 560012 India

Stock market prediction is a complex and tedious task that involves the processing of large amounts of data, that are stored in ever growing databases. The vacillating nature of the stock market requires the use of data mining techniques like clustering for stock market analysis and prediction. Genetic algorithms and neural networks have the ability to handle complex data. In this paper, we propose a fuzzy based neuro-genetic algorithm – Fuzzy based Evolutionary Approach to Self Organizing Map(FEASOM) to cluster stock market data. Genetic algorithms are used to train the Kohonen network for better and effective prediction. The algorithm was tested on real stock market data of companies like Intel, General Motors, Infosys, Wipro, Microsoft, IBM, etc. The algorithm consistently outperformed regression model, backpropagation algorithm and Kohonen network in predicting the stock market values.

关键词： Machine learning Kohonen network genetic algorithms data mining prediction clustering

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Steganalysis of LSB Embedded Images Using Variable Threshold Color Pair Analysis

Steganalysis of LSB Embedded Images Using Variable Threshold...

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International Conference on Intelligent Sensing and Information Processing (ICISIP)

作者： K B Raja N Shankara K R Venugopal L M Patnaik Department of Computer Science Engineering Bangalore University Bangalore India Department of Computer Science Engineering Bangalore University Bangalore Microprocessor Applications Laboratory Indian Institute of Science Bangalore India

Steganography and steganalysis are important areas of research in recent years involving a number of applications. Steganography is the science of embedding information into the cover image without causing statistically significant perturbations to the cover image. Steganalysis is the technology that attempts to defeat steganography by detecting the hidden information and extracting or destroying it. In this paper we present twenty four bits BMP image color pair analysis with variable threshold (CPAVT) to detect stego-object with 10% payload. In earlier works 20% payload was used in close color pair analysis. It is observed that with new variable threshold technique, the performance parameters, i.e. false detection rate (FDR) and false alarm rate (FAR) are better in comparison with the earlier works.

关键词： Image color analysis Image analysis Steganography Discrete wavelet transforms Discrete cosine transforms Discrete transforms Fast Fourier transforms Image coding Filtering Object detection

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High Capacity Lossless Secure Image Steganography using Wavelets

High Capacity Lossless Secure Image Steganography using Wave...

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International Conference on Advanced Computing and Communications (ADCOM)

作者： K B Raja Vikas K R Venugopal L M Patnaik Department of Computer Science Engineering Visvesvaraya College of Engineering Bangalore University Bangalore India Microprocessor Applications Laboratory Indian Institute of Science Bangalore India

The modern steganography presents a challenging task of embedding data that should be imperceptible to the human visual system (HVS) and also escape the detection of powerful machine vision of computers. In this paper we present a high capacity, lossless, secure wavelet steganographic algorithm in which payload bitstream is encrypted and embedded into the wavelet coefficients of the cover image to derive a stego-image. The payload is embedded in the approximation band of the wavelet domain that increases its robustness. It is observed through simulations that mean square error (MSE), mean absolute error (MAE), bit error rate (BER) and histogram analysis that the pay load is retrieved without any errors and its performance is better than the earlier insignificant coefficient replacement (ICR) technique.

关键词： Steganography Payloads Bit error rate Humans Visual system Machine vision computer vision Embedded computing Cryptography Wavelet coefficients

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A secure image steganography using LSB, DCT and compression techniques on raw images 3

A secure image steganography using LSB, DCT and compression ...

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3rd International Conference on Intelligent Sensing and Information Processing, ICISIP 2005

作者： Raja, K.B. Chowdary, C.R. Venugopal, K.R. Patnaik, L.M. Department of Computer Science Engineering Bangalore University Bangalore560001 India Microprocessor Applications Laboratory Indian Institute OfScience Bangalore560012 India

ISBN: (纸本)0780395883

Steganography is an important area of research in recent years involving a number of applications. It is the science of embedding information into the cover image viz., text, video, and image (payload) without causing statistically significant modification to the cover image. The modern secure image steganography presents a challenging task of transferring the embedded information to the destination without being detected. In this paper we present an image based steganography that combines Least Significant Bit(LSB), Discrete Cosine Transform(DCT), and compression techniques on raw images to enhance the security of the payload. Initially, the LSB algorithm is used to embed the payload bits into the cover image to derive the stego-image. The stego-image is transformed from spatial domain to the frequency domain using DCT. Finally quantization and runlength coding algorithms are used for compressing the stego-image to enhance its security. It is observed that secure images with low MSE and BER are transferred without using any password, in comparison with earlier works. © 2005 IEEE.

关键词： Frequency domain analysis

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An efficient fuzzy based neuro - Genetic algorithm for stock market prediction

An efficient fuzzy based neuro - Genetic algorithm for stock...

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2nd Indian International Conference on Artificial Intelligence, IICAI 2005

作者： Srinivasa, K.G. Venugopal, K.R. Patnaik, L.M. Department of Computer Science and Engineering College of Engineering University Visvesvaraya Bangalore - 560001 India Microprocessor Applications Laboratory Indian Institute of Science Bangalore - 560012 India

ISBN: (纸本)0972741216

Stock market prediction is a complex and tedious task that involves the processing of large amounts of data, that are stored in ever growing databases. The vacillating nature of the stock market requires the use of data mining techniques like clustering for stock market analysis and prediction. Genetic algorithms and neural networks have the ability to handle complex data and are immune to noise in the input. In this paper, we propose a fuzzy based neuro-genetic algorithm - Fuzzy based Evolutionary Approach to Self Organizing Map(FEASOM) to cluster stock market data. Genetic algorithms are used to train the Kohonen network for better and effective prediction. The algorithm was tested on real stock market data of companies like Intel, General Motors, Infosys, Wipro, Microsoft, IBM, etc. The algorithm consistently outperformed regression model, backpropagation algorithm and Kohonen network in predicting the stock market values. Copyright © IICAI 2005.

关键词： Genetic algorithms

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A self-adaptive migration model genetic algorithms for data mining applications

A self-adaptive migration model genetic algorithms for data ...

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2nd Indian International Conference on Artificial Intelligence, IICAI 2005

ISBN: (纸本)0972741216

Data mining involves the process of extracting nontrivial knowledge or hidden patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously per-forming a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabtese database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others. Copyright © IICAI 2005.

关键词： Genetic algorithms

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GaXsearch: An XML information retrieval mechanism using genetic algorithms

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18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence

ISBN: (纸本)3540304622

The XML technology, with its self-describing and extensible tags, is significantly contributing to the next generation semantic web. The present search techniques used for HTML and text documents are not efficient to retrieve relevant XML documents. In this paper, Genetic Algorithms are presented to learn about the tags, which are useful in indexing. The indices and relationship strength metric are used to extract fast and accurate semantically related elements in the XML documents. The Experiments are conducted on the DataBase systems and Logic Programming (DBLP) XML corpus and are evaluated for precision and recall. The proposed GaXsearch outperforms XSEarch [1] and XRank [2] with respect to accuracy and query execution time. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Information retrieval

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A dynamic Migration Model for self-adaptive Genetic Algorithms

A dynamic Migration Model for self-adaptive Genetic Algorith...

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6th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2005

作者： Srinivasa, K.G. Sridharan, K. Shenoy, P. Deepa Venugopal, K.R. Patnaik, Lalit M. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore 560001 India Department of CSE Sunny Buffalo United States Microprocessor Applications Laboratory Indian Institute of Science India

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA). © Springer-Verlag Berlin Heidelberg 2005.

关键词： Genetic algorithms

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