Satyendra Nath Mandal

Diffusion and Encryption of Digital Image Using Genetic Algorithm

The security level of any symmetric key algorithm is directly proportional to its execution time. The algorithm will be more secured if the number of iteration is increase. Soft computing methods are trying to reduce time complexity without compromise the security level. In this paper, an image has been encrypted by number of steps. At first, a key set has been developed based on 16 arbitrary characters and a large number. The image has been diffused in the next step. The key set and diffused image have been computed by genetic algorithm. Here, ring crossover and order changing mutation operator is used in genetic algorithm. The encrypted image is constructed based on logical operation between the diffused image and key. The effectiveness of the algorithm has been tested by number of statistical tests. Finally, a comparative study has been made between our proposed algorithm and some other algorithms. It has been observed that the proposed algorithm has given better result.

PSO-GA Hybrid Approach in Image Encryption

In this paper, a hybrid PSO-GA approach has been used to encrypt an image. The key is produced based on genetic and particle swarm optimization algorithm. The different numbers have been produced by genetic algorithm and elements of key set have been chosen based on particle swarm optimization. The encrypted image is constructed using the logical operation between image and key value. The effectiveness of the proposed algorithm has been proved based on number of tests applied between plain and cipher image. Finally, it has been observed that the algorithm has been given better result almost all tests.

A novel image encryption algorithm using pixel shuffling and pixel intensity reversal

Visual cryptography is process to hide information of digital images which has become an active area of research during the last decades. Because of intrinsic features of images (i.e. bulk data capacity, high redundancy etc) encryption of images is different from that of texts and it is generally difficult to handle by traditional methods. Present study proposes a novel symmetric image encryption algorithm using pixel shuffling and reversed binary string of pixel intensity value. Here, the size of the proposed Key_row and Key_col vectors are same as the no of rows and no of columns of the original image. For each pixel, initially binary pattern of pixel intensity value is reversed. In the next step, the new intensity value is stored at a shuffled position. The pixel position is shuffled using two key vectors-Key_row and Key_col. The proposed method is applied on benchmark images and assessment of performance is done using different standard metric for key testing-histogram, correlation, entropy, NPCR and UACI between the original and encrypted image. Theoretical analysis and experimental results both confirm that proposed algorithm possesses high security and can resist statistical and differential attacks.

Prediction of productivity of mustard plant at maturity using harmony search

The production of a plant can be measured by various parameters like shoot length, number of leaves, root length, number of roots etc. During the growth of a plant, new leaves may appear and some leaves may fall. As a result, it is very difficult to predict the growth of the plant based on the leaf numbers. It is also difficult to measure the plant growth on the basis of root numbers as it grows underground. So, it is very convenient to measure the plant growth with respect to shoot length. In this paper, an effort has been made to predict the shoot length of a mustard plant by harmony search. The average error has been calculated based on the actual shoot length and predicted shoot length by harmony search. The result obtained in this paper has been compared with other soft computing and statistical methods which have been applied to the same problem. It has been observed that harmony search has given the minimum error compared to that of the others. The least square method has been applied on the predicted shoot length to find the shoot length at maturity. Finally, the pod yield has been predicted based on the shoot length at maturity.

Application of Neuro-Statistic Model on a Time Series Data

The output of some physical problem is dependent on huge number of time dependent parameters. But many of them are not significant or they are highly correlated with others parameters. Some parameters which are play significant role in the problem and give the information which is mandatory and not correlated with the others. So, same result can be produced by fewer parameters instead of considering all parameters. In this paper, an effect has been made to find the significant environmental parameters in production of mustard plant using principal component and factor analysis. Finally, artificial neural network has been applied on highly significant parameters to predict the production of mustard plant at maturity.

Prediction of Productivity of Mustard Plant Using Variable Reduction and Artificial Neural Network Model

The productivity of mustard plant is dependent on huge number of time dependent parameters. But many of them are not significant or they are highly correlated with others parameters. Some parameters playing significant role in growth of the plant and give the information which is mandatory but not correlated with the others. So, same result can be produced by fewer parameters instead of considering all parameters. In this paper, an effect has been made to reduce the significant environmental parameters which have dominated the growth of mustard plant using principal component and factor analysis. These two methods have been used as variable reduction model. The artificial neural network has been applied on highly significant parameters to predict the shoot length of mustard plant. The linear equation has been used to find the shoot length at maturity. Finally, the productivity of the plant has been predicted based on shoot length of the plant at maturity.