The BNL toolbox is a set of Matlab functions for defining and estimating the
parameters of a Bayesian network for discrete variables in which the conditional
probability tables are specified by logistic regression models. Logistic regression can be
used to incorporate restrictions on the conditional probabilities and to account for the
effect of covariates. Nominal variables are modeled with multinomial logistic regression,
whereas the category probabilities of ordered variables are modeled through a cumulative
or adjacent-categories response function. Variables can be observed, partially observed,
or hidden.
This project demonstrates the use of secure hash functions technique
to implement a file encryption / decryption system.
This implemented application can encrypt / decrypt multiple files
on the fly using a password. The password supplied by the user
is used as the source message from which the hash code (key) is
generated using the SHA algorithm. Then this key is used to
enctypted the data in the file(s). This key is stored in the
encrypted file along with the encrypted data.
This project demonstrates the use of secure hash functions technique
to implement a file encryption / decryption system.
This implemented application can encrypt / decrypt multiple files
on the fly using a password. The password supplied by the user
is used as the source message from which the hash code (key) is
generated using the SHA algorithm. Then this key is used to
enctypted the data in the file(s). This key is stored in the
encrypted file along with the encrypted data.
