In this paper, new statistical features based approach (SFBA) for hourly energy consumption prediction using Multi-Layer Perceptron is presented. The model consists of four stages: data retrieval, data pre-processing, feature extraction and prediction. In the data retrieval stage, historical hourly consumed energy data has been retrieved from the database. During data pre-processing, filters have been applied to make the data more suitable for further processing. In the feature extraction stage, mean, variance, skewness, and kurtosis are extracted. Finally, Multi-Layer Perceptron has been used for prediction. For experimentation with Multi-Layer Perceptron with different training algorithms, a final model of the network was designed in which the scaled conjugate gradient (trainscg) was used as a network training function, tangent sigmoid (Tansig) as a hidden layer transfer function and linear function as an output layer transfer function. For hourly energy consumption prediction, a total of six weeks data of ten residential buildings has been used. To evaluate the performance of the proposed approach, Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), evaluation measurements were applied.

Biometrics is being commonly used nowadays for the identification and verification of humans everywhere in the world. In biometrics humans unique characteristics like palm, fingerprints, iris etc. are being used. Pattern Recognition and image processing are the major areas where research on signature verification is carried out. Hand written Signature of an individual is also unique and for identification of humans are being used and accepted specially in the banking and other financial transactions. The hand written signatures due to its importance are at target of fraudulence. In this paper we have surveyed different papers on techniques that are currently used for the identification and verification of Offline signatures.