Three new data sets for intermolecular interactions, AHB21 for anion-neutral dimers, CHB6 for cation-neutral dimers, and IL16 for ion pairs, are assembled here, with complete-basis CCSD(T) results for each. These benchmarks are then used to evaluate the accuracy of the single-exchange approximation that is used for exchange energies in symmetry-adapted perturbation theory (SAPT), and the accura...

Spatial cross-validation and average-error statistics are examined with respect to their abilities to evaluate alternate spatial interpolation methods. A simple crossvalidation methodology is described, and the relative abilities of three, dimensioned error statistics—the root-mean-square error (RMSE), the mean absolute error (MAE), and the mean bias error (MBE)—to describe average interpolator...

The temporal prediction of the solar radiation is very important for the operation of any solar energy system technology and completing data set. Based on meteorological parameters, the artificial neural network (ANN) can bring a technical solution for the prediction problems. In this paper, we developed an ANN for the south Tunisian climate to predict global solar radiation. Five years of reco...

This paper concerns the use of feedforward neural networks (FNN) for predicting the nondimensional velocity of the gas that flows along a porous wall. The numerical solution of partial differential equations that govern the fluid flow is applied for training and testing the FNN. The equations were solved using finite differences method by writing a FORTRAN code. The Levenberg–Marquardt algorith...

In the paper a relatively simple yet powerful and versatile technique for forecasting time series data – simple exponential smoothing is described. The simple exponential smoothing (SES) is a short-range forecasting method that assumes a reasonably stable mean in the data with no trend (consistent growth or decline). It is one of the most popular forecasting methods that uses weighted moving av...

This paper gives the optimal stack filtering theory under the mean absolute error (MAE) criterion a completely new meaning in terms of the a posteriori Bayes minimum-cost decision. It is shown that under certain conditions this always leads to a rank-order filter (ROF) as the best filter in the minimum MAE sense. It is further shown that for a mostly practical case, the solution becomes the med...

We use a set of 477 lexicosyntactic, acoustic, and semantic features extracted from 393 speech samples in DementiaBank to predict clinical MMSE scores, an indicator of the severity of cognitive decline associated with dementia. We use a bivariate dynamic Bayes net to represent the longitudinal progression of observed linguistic features and MMSE scores over time, and obtain a mean absolute erro...

Judgemental forecasting of exchange rates is critical for ®nancial decision-making. Detailed investigations of the potential e€ects of time-series characteristics on judgemental currency forecasts demand the use of simulated series where the form of the signal and probability distribution of noise are known. The accuracy measures Mean Absolute Error (MAE) and Mean Squared Error (MSE) are freque...

With the change in global climate and environment, prevalence of extreme rainstorms flood disasters has increased, causing serious economic property losses. Therefore, accurate rapid prediction waterlogging become an urgent problem to be solved. In this study, Jianye District Nanjing City China is taken as study area. The time series data recorded by rainfall stations ponding monitoring from Ja...

In this paper we implement a collaborative filtering algorithm on the MovieLens dataset to predict movie ratings for the users. The original matrix, which contains the movie ratings on a 1-5 scale for the users, has many missing entries. Rank-K factorization is used to construct the filtering algorithm and alternating least squares is then performed on the two lower rank matrices in order to fi...