PURPOSE To propose a more accurate method to predict the soft tissue changes after orthognathic surgery. PATIENTS AND METHODS The subjects included 69 patients who had undergone surgical correction of Class III mandibular prognathism by mandibular setback. Two multivariate methods of forming prediction equations were examined using 134 predictor and 36 soft tissue response variables: the ordi...

This paper describes a framework for a statistical anomaly prediction system using ensemble Quickprop neural network forecasting model, which predicts unauthorized invasions of user based on previous observations and takes further action before intrusion occurs. This paper focuses on detecting significant changes of transaction intensity for intrusion prevention. The experimental study is perfo...

This work aims at the modeling of bio-sorption of cadmium(II) onto physically and chemically activated Cajanus cajan (Pigeon pea) husks. Experimental data obtained were fitted to a number of isotherm and kinetic models, and the results interpreted. The monolayer Cd(II) bio-sorption capacities of the husk were found to considerably increase by 2.82 times due to chemical activation, for bio-s...

The objective of this paper is to develop a novel wind speed forecasting technique, which produces more accurate prediction. The Wavelet Transform (WT) along with the Auto Regressive Moving Average (ARMA) is chosen to form a hybrid whose combination is expected to give minimum Mean Absolute Prediction Error (MAPE). A simulation study has been conducted by comparing the forecasting results using...

Root-mean-square-deviation (RMSD) is an indicator in protein-structure-prediction-algorithms (PSPAs). Goal of PSP algorithms is to obtain 0 Å RMSD from native protein structures. Protein structure and RMSD prediction is very essential. In 2013, the estimated RMSD proteins based on nine features were obtained best results using D2N (Distance to the native). We presented in This paper proposed ap...

We report the development of a robust and general model for the prediction of melting points. It is based on a diverse data set of 4173 compounds and employs a large number of 2D and 3D descriptors to capture molecular physicochemical and other graph-based properties. Dimensionality reduction is performed by principal component analysis, while a fully connected feed-forward back-propagation art...

This paper proposes a novel approach to early determination of zero-quantized 8× 8 discrete cosine transform (DCT) coefficients for fast video encoding. First, with the dynamic range analysis of DCT coefficients at different frequency positions, several sufficient conditions are derived to early determine whether a prediction error block (8× 8) is an all-zero or a partial-zero block, i.e., the ...

Based on experimentally measured values by thermal probe method, the prediction model of thermal conductivities of apple fruit juice as a function of concentration and temperature was studied by neural network method. The optimal neural network was made of two hidden layers and every hidden layer had six neurons. The prediction result shows that the optimal model could predict thermal conductiv...