Personal identification based on handprint has been gaining more attention with the
increasing needs of high level of security. In this study a novel approach for human
recognition based on handprint is proposed. Wavelet transform was used to extra
ct features
presented in the palm image based on wavelet zero-crossing method. Firstly the wavelet
transform of the whole palm image at the fourth level was worked out, which results in
four matrices; three of them are detail matrices (i.e., horizontal, vertical and diagonal) as
well as one approximation matrix. Throughout this study, only the detail matrices were
used because the required information (i.e., hand lines and curves) is included in those
matrices. Sixteen features were extracted from each detail matrix, and then arranged in one
vector. Consequently, for each palm sample a feature vector consisting of 48 input features
of the used neural network was obtained. For this purpose, a database consisting of 400
palm images belonging to 40 people at the rate of 10 images per person was built. Practical
tests outcome showed that the designed system successfully indentified 91.36% of the
tested images.
Rainfall is highly non-linear and complicated phenomena, which require nonlinear
mathematical modeling and simulation for accurate prediction. This study
comparing the performance of the prediction of one-day-ahead, where Two
Feed Forward Neural N
etwork FFNN models were developed and implemented
to predict the rainfall on daily for three months (December, January, February).
These models are Artificial Neural Network traditional (ANN) model and artificial
neural network technique combined with wavelet decomposition (Wavelet-
Neural) According to two different methods to build a model using two types of
wavelets of Daubechies family (db2, db5). In order to compare the performance
of the models in their ability to predict the rains on short-term (for one and two
and three-days-ahead) the last months of the period of study, used some statistical
standards, These parameters include the Root Mean Square Error RMSE,
Coefficient Of Correlation (R).
