‎We consider a linear programming problem in a general form and suppose that all coefficients may vary in some prescribed intervals‎. ‎Contrary to classical models‎, ‎where parameters can attain any value from the interval domains independently‎, ‎we study problems with linear dependencies between the parameters‎. ‎We present a class of problems that are easily solved by reduction to the classi...

‎we consider a linear programming problem in a general form and suppose that all coefficients may vary in some prescribed intervals‎. ‎contrary to classical models‎, ‎where parameters can attain any value from the interval domains independently‎, ‎we study problems with linear dependencies between the parameters‎. ‎we present a class of problems that are easily solved by reduction to the classi...

Among all measures of independence between random variables, mutual information is the only one that is based on information theory. Mutual information takes into account of all kinds of dependencies between variables, i.e., both the linear and non-linear dependencies. In this paper we have classified some well-known bivariate distributions into two classes of distributions based on their mutua...

In this paper we will survey three forms of statistical dependency found in block ciphers. Each dependency is based on some notion of linearity, forming the basis of several attacks which are particularly applicable to product ciphers. We consider linear relationships between the plaintext and ciphertext bits, using elementary arguments from linear algebra, and then using linear relationships u...

An artificial neural network can be used as an intelligent controller to control non-linear, dynamic system through learning. It can easily accommodate non-linearities and time dependencies. Most common multi-layer feed-forward neural networks have the drawbacks of large number of neurons and hidden layers required to deal with complex problems and require large training time. To overcome these...