This article proposes a novel approach for the prediction of maximum dry density (MDD) and optimum moisture content (OMC) of soil-stabiliser mix by using radial basis function (RBF) neural networks. RBF neural network is utilised to construct comprehensive and accurate models to be able to relate the MDD and OMC of stabilised soil to the properties of natural soil such as particle size distribution, plasticity, linear shrinkage and the type and quantity of stabilising additives. Two separate sets of RBF prediction models, one for the MDD and the other for the OMC, have been developed. A parametric study was also conducted in this study using the results obtained from the proposed models to evaluate the sensitivity of MDD and OMC due to variation of the influencing parameters. A comprehensive set of data including a wide range of soil types obtained from previously published stabilisation test results was used for training, validation and testing the prediction models. The accuracy of the proposed models is satisfactory when compared with that of the experimental results. The results of proposed RBF models were further compared with those of the existing models found in literature and found to be more accurate.

A new model is derived to predict the peak ground acceleration (PGA) utilizing a hybrid method coupling artificial neural network (ANN) and simulated annealing (SA), called SA-ANN. The proposed model relates PGA to earthquake source to site distance, earthquake magnitude, average shear-wave velocity, faulting mechanisms, and focal depth. A database of strong ground-motion recordings of 36 earthquakes, which happened in Iran's tectonic regions, is used to establish the model. For more validity verification, the SA-ANN model is employed to predict the PGA of a part of the database beyond the training data domain. The proposed SA-ANN model is compared with the simple ANN in addition to 10 well-known models proposed in the literature. The proposed model performance is superior to the single ANN and other existing attenuation models. The SA-ANN model is highly correlated to the actual records (R = 0.835 and ρ = 0.0908) and it is subsequently converted into a tractable design equation

Real medical datasets usually consist of missing data with different patterns which decrease the performance of classifiers used in intelligent healthcare and disease diagnosis systems. Many methods have been proposed to impute missing data, however, they do not fulfill the need for data quality especially in real datasets with different missing data patterns. In this paper, a four-layer model is introduced, and then a hybrid imputation (HIMP) method using this model is proposed to impute multi-pattern missing data including non-random, random, and completely random patterns. In HIMP, first, non-random missing data patterns are imputed, and then the obtained dataset is decomposed into two datasets containing random and completely random missing data patterns. Then, concerning the missing data patterns in each dataset, different single or multiple imputation methods are used. Finally, the best-imputed datasets gained from random and completely random patterns are merged to form the final dataset. The experimental evaluation was conducted by a real dataset named IRDia including all three missing data patterns. The proposed method and comparative methods were compared using different classifiers in terms of accuracy, precision, recall, and F₁-score. The classifiers’ performances show that the HIMP can impute multi-pattern missing values more effectively than other comparative methods.