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This paper presents a One-Time Pad (OTP) encryption and a genetic algorithm (GA) audio steganography, respectively. In the proposed scheme, genetic algorithm is used for both encryption and steganography. In the encryption process, the key is randomly generated using genetic algorithm. The generated key is used as a key for the One-Time Pad algorithm. Then, the encrypted message is embedded in audio in the best position for embedding using signal-to-noise ratio (SNR) in genetic algorithm fitness function. Results had been discussed for the proposed scheme. It dissolves the drawbacks of key generating and distribution for One-Time Pad by using genetic algorithm. The proposed method satisfies the main steganography requirements pointed in perceptual transparency, hidden data capacity and robustness. Experimental results had been made, namely text analysis, waveform analysis and signal-to-noise ratio. Besides, a comparison of SNR for the proposed method withsome known algorithm. All are to indicate that the proposed method can significantly outperform many steganography techniques in terms of security and audio quality.

Efficient algorithms for mining frequent itemsets are crucial for mining association rules as well as for many other data mining tasks. It is well known that countTable is one of the most important facility to employ subsets property for compressing the transaction database to new lower representation of occurrences items. One of the biggest problem in this technique is the cost of candidate generation and test processing which are the two most important steps to find association rules. In this paper, we have developed this method to avoid the costly candidate-generation-and-test processing completely. Moreover, the proposed methods also compress crucial information about all itemsets, maximal length frequent itemsets, minimal length frequent itemsets, avoid expensive, and repeated database scans. The proposed named CountTableFI and BinaryCountTableF are presented, the algorithm has significant difference from the Apriori and all other algorithms extended from Apriori. The idea behind this algorithm is in the representation of the transactions, where, we represent all transactions in binary number and decimal number, so it is simple and fast to use subset and identical set properties. A comprehensive performance study shows that our techniques are efficient and scalable comparing with other methods.

Process mining refers to the extraction of process models from event logs. This paper presents a new process mining approach based on the combination of Honey Bee algorithm and Genetic Algorithm in which the benefits of Honey Bee algorithm is used where the concept of neighborhood search for a solution emerges from intelligent behavior of honeybee and the diversity of Genetic algorithm to find the global optimum. The new process mining approach presented in this paper is implemented as a plug-in in the process mining framework http://www.processmining.org. Computational experiments show that the process mining approach present in this paper gives a significant improvement over the basic Genetic algorithm.

Data security is one of major challenges in the recent literature. Cryptography is the most common phenomena used to secure data. One main aspect in cryptography is creating a hard to guess cipher. Artificial Neural Networks (ANN) is one of the machine learning techniques widely employed in several fields based on its characters, depending on the application area. One of these fields is data security. The state of art in this paper is the use of self organizing map (SOM) algorithm concept as a core idea to construct a pad; this pad is used to generate the cipher at one end. At the other end of communication the same process is synchronized to generate the same pad as the deciphering key. The security of the proposed model depends on the complex nature of ANN's. The algorithm could be categorized under symmetric cryptography, merging both stream and block cipher. A modified version of the same algorithm also presented employs permutation and variable SOM neighborhoods. The proposal can be applied over several file formats like videos, images, text files, data benchmarks, etc as show in experimental results.

In this paper, we present a modified on-demand routing algorithm for mobile ad-hoc networks (MANETs). The proposed algorithm is based on both the standard Ad-hoc On-demand Distance Vector (AODV) protocol and ant colony based optimization. The modified routing protocol is highly adaptive, efficient and scalable. The main goal in the design of the protocol was to reduce the routing overhead, response time, end-to-end delay and increase the performance. We refer to the new modified protocol as the Multi-Route AODV Ant routing algorithm (MRAA).

In this paper, we present the convergence rate of the error in a neural network which was learnt by a constructive method. The constructive mechanism is used to learn the neural network by adding hidden units to this neural network. The main idea of this work is to find the eigenvalues of the transformation matrix concerning the error before and after adding hidden units in the neural network. By using the eigenvalues, we show the relation between the convergence rate in neural networks without and with thresholds in the output layer.