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Introduction of a genetic algorithm with two new crossover operators for searching over discrete probability spaces is discussed.

With the crucial growth of technology, data security over the network and internet has achieved immense of prominence today and achieving good security is always a talk of a good security method being in place. Therefore, there is need of better security method with better efficiency in order to increase the security and authenticity and to efficiently decrease computational complexity. Although there are many symmetric key algorithms, we proposed a content-based algorithm, which follows the Symmetric key cryptography method. This is an algorithm implementing binary addition operation, a circular bit shifting operation and folding method and as symmetric key cryptography needs the transmission of the secret key along with the ciphered text through the network, a deep concern has given to make the key secure. [ABSTRACT FROM AUTHOR]

Removal of miscible hazardous materials from aqueous solutions is an alarming problem for the environmental scientists. Several linear and nonlinear regression models like Langmuir, Freundlich, D–R, Tempkin isotherm models are in vogue for determining the adsorbing capacity of standard adsorbents used for this purpose. In this article, we propose a novel quantum inspired backpropagation multilayer perceptron (QBMLP) based on quantum gates (single qubit rotation gates and two qubit controlled-not gates) for the prediction of this adsorption behavior exhibited by calcareous soil oftentimes used in adsorbing miscible iron from aqueous solutions. The backpropagation learning formulae for the proposed QBMLP architecture has also been generalized for multiple number of layers in both field homogeneous and field heterogeneous configurations characterized by three standard activations, viz., sigmoid , tanh and tan 1.5h functions. Applications of the efficiency of the proposed QBMLP over the regression models are demonstrated with regards to the prediction behavior of the adsorption of iron by calcareous soil from an aqueous solution with effect to various characteristic adsorbent parameters. The adsorption process is considered to be a physical one since the activation energy ( E A ) of ferrous ion adsorption is 9.469 kJ mol −1 due to Arrhenius. Moreover, the thermodynamic parameters of Gibb's free energy ( G 0 ), enthalpy ( H 0 ) and entropy ( S 0 ) values indicate it be spontaneous. Results indicate that QBMLP predicts the adsorption behavior of calcareous soil to a very closer extent thereby obviating the need for further regression/experimental analysis. Comparison with the performance of a similar classical multilayer perceptron (MLP) architecture also reveals the prediction and time efficiency of the proposed QBMLP architecture. [ABSTRACT FROM AUTHOR]

Several classical techniques have evolved over the years for the purpose of denoising binary images. But the main disadvantages of these classical techniques lie in that an a priori information regarding the noise characteristics is required during the extraction process. Among the intelligent techniques in vogue, the multilayer self organizing neural network (MLSONN) architecture is suitable for binary image preprocessing tasks. In this article, we propose a quantum version of the MLSONN architecture. Similar to the MLSONN architecture, the proposed quantum multilayer self organizing neural network (QMLSONN) architecture comprises three processing layers viz., input, hidden and output layers. The different layers contains qubit based neurons. Single qubit rotation gates are designated as the network layer interconnection weights. A quantum measurement at the output layer destroys the quantum states of the processed information thereby inducing incorporation of linear indices of fuzziness as the network system errors used to adjust network interconnection weights through a quantum backpropagation algorithm. Results of application of the proposed QMLSONN are demonstrated on a synthetic and a real life binary image with varying degrees of Gaussian and uniform noise. A comparative study with the results obtained with the MLSONN architecture and the supervised Hopfield network reveals that the QMLSONN outperforms the MLSONN and the Hopfield network in terms of the computation time. [ABSTRACT FROM AUTHOR]

Abstract: The pragmatic realism of the high dimensionality incurs limitations in many pattern recognition arena such as text classification,data mining, information retrieval and face recognition. The unsupervised PCA ante up no attention to the class labels of the existing training data. LDA is not stable due to the small sample size problem and but corroborate best directions when each class has a Gaussian density with a common covariance matrix. But it can flop if the class densities are more general and interpreted class separability in between-class-matrices are inadequate. Maximum Margin Criterion (MMC) having lower computational cost, is more efficient than LDA for calculating the discriminant vectors barring the computation for inverse of within-class-scatter matrix. But traditional MMC disregards the discriminative information within the local structure of samples and performance is depended on choosing of a coefficient. In this paper we delineate the locality of data points by counting a distances among data points considering the supervised knowledge. We have computed the entire scatter matrix in Laplacian graph embedded space and finally the produced stastically uncorrelated discriminant vectors reduces redundancy among the extracted features and there is no constant to be chosen. Our experiment with Reauter dataset recommends this algorithm is more efficient than LDA, MMC and it manifests similar or sometimes better result than other locality based algorithm like LPP and LSDA. [Copyright &y& Elsevier]