In the present paper we have developed a new method for constructing magic cube by using the folded magic square technique. The proposed method considers a new step towards the magic cube construction that applied a good insight and provides an easy generalized technique. This method generalized the design of magic cube with N order regardless the type of magic square whether odd order, singly even order or doubly even order. The proposed method is fairly easy, since it have depended mainly on the magic square construction methods, and all what the designer need is just how to builds six magic square sequentially or with constant difference value between each pair of the numbers in the square matrix, whereby each one of this magic square will represents the surface or dimension for magic cube configuration. The next step for the designer will be how to arrange each square in the proper order to constitute the regular cube in order to maintain the properties of magic cube, where the sum of rows, columns and the diagonals from all directions are the same.

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Biometrics are short of revocability and privacy while cryptography cannot adjust the user’s identity.  By obtaining cryptographic keys using biometrics, one can obtain the features such as revocability, assurance about user’s identity, and privacy. Multi-biometrical based cryptographic key generation approach has been proposed, subsequently, left and right eye and ear of a person are uncorrelated from one to other, and they are treated as two independent biometrics and combine them in our system. None-the-less, the encryption keys are produced with the use of an approach of swarm intelligence.  Emergent collective intelligence in groups of simple autonomous agents is collectively termed as a swarm intelligence. The Meerkat Clan Key Generation Algorithm (MCKGA) is a method for the generation of a key stream for the encryption of the plaintext. This method will reduce and distribute the number of keys.  Testing of system, it was found that the keys produced by the characteristics of the eye are better than the keys produced by the characteristics of the ear. The advantages of our approach comprise generation of strong and unique keys from users’ biometric data using MCKGA and it is faster and accurate in terms of key generation.

Abstract-In this paper, we have proposed a new iterated symmetric cipher, which is designed with Substitution and Permutation Network (SPN) structure and depends on strong mathematical built. It uses a compact algorithm for encryption and decryption processes, which consists of four main stages that roughly similar in its work to the Advance Encryption Standard (AES) stages. Starting by the SubByte operation, ReversibleShiftrows operation, ReversibleMixcolumns operation, and Round key addition. The proposed operations in this cipher have implemented in a straightforward manner relatively in both Encryption/Decryption by an elegant way. These four stages designed to reduce the hardware requirements and to produces high efficiency, which keeps the encryption and decryption process at the same speed in the hardware devices and eliminates the difference of execution times as well as creates a balance in forward and backward operations. The proposed cipher interested with modern design by adopted new algebraic operations and clear mathematical notations to ensure a high level of security. The proposed cipher did not build suddenly or arbitrarily but it acts as a sequence of developments and represents as a long process of design for long time, since several proposed ciphers have been published recently by us that paved the way to its new design, so the designed cipher inherited a good properties from a proven famous algorithms' features to produce high resistance against all known attacks and to submit a high performance on many platforms and in a wide range of hardware and software applications.

Feature selection (FS) (or feature dimensional reduction, or feature optimization) is an essential process in pattern recognition and machine learning because of its enhanced classification speed and accuracy and reduced system complexity. FS reduces the number of features extracted in the feature extraction phase by reducing highly correlated features, retaining features with high information gain, and removing features with no weights in classification. In this work, an FS filter-type statistical method is designed and implemented, utilizing a t-test to decrease the convergence between feature subsets by calculating the quality of performance value (QoPV). The approach utilizes the well-designed fitness function to calculate the strength of recognition value (SoRV). The two values are used to rank all features according to the final weight (FW) calculated for each feature subset using a function that prioritizes feature subsets with high SoRV values. An FW is assigned to each feature subset, and those with FWs less than a predefined threshold are removed from the feature subset domain. Experiments are implemented on three datasets: Ryerson Audio-Visual Database of Emotional Speech and Song, Berlin, and Surrey Audio-Visual Expressed Emotion. The performance of the F-test and F-score FS methods are compared to those of the proposed method. Tests are also conducted on a system before and after deploying the FS methods. Results demonstrate the comparative efficiency of the proposed method. The complexity of the system is calculated based on the time overhead required before and after FS. Results show that the proposed method can reduce system complexity.