Multivariate models with multiple linear regression (MLR), artificial neural network (ANN), and k-nearest neighbors (KNN) were developed to predict the modulus of rupture Pinus sylvestris structural timber. The aim this study was develop compare these obtained from resonance ultrasound tests, static elasticity different measured wood feature. Resonance tests performed in three vibration modes (...

in this work, response surface methodology (rsm) and artificial neural network (ann) were used to predict the decolorization efficiency of reactive red 33 (rr 33) by o3/uv process in a bubble column reactor. the effects of four independent variables including time (20-60 min), superficial gas velocity (0.06-0.18 cm/s), initial concentration of dye (50-150 ppm) and ph (3-11) were investigated us...

Missing values in datasets and databases can be estimated via statistics, machine learning and artificial intelligence methods. This paper uses a novel hybrid neural network and weighted nearest neighbors to estimate missing values and provides good results with high performance. In this work, four different characteristic datasets were used and missing values were estimated. Error ratio, corre...

Background: Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnancy, which is associated with serious complications. In the event of early diagnosis of this disease, some of the maternal and fetal complications can be prevented. The aim of this study was to early predict gestational diabetes mellitus by two statistical models including artificial neural ne...

Diagnostic errors are the most frequent non-operative medical errors. Diagnosis should be more data-driven than trial-anderror. Machine Learning provides techniques for classification and regression purposes which can be used for solving diagnostic problems in different medical domains. Predictive analysis of fatal ailments like cancer using existing data can serve as a diagnosis tool for docto...

In this study, artificial neural network was used to predict the surface tension of 20 hydrocarbon mixtures. Experimental data was divided into two parts (70% for training and 30% for testing). Optimal configuration of the network was obtained with minimization of prediction error on testing data. The accuracy of our proposed model was compared with four well-known empirical equations. The arti...

The problem of k-nearest neighbors (kNN) search is to find nearest k neighbors from a given data set for a query point. To speed up the finding process of nearest k neighbors, many fast kNN search algorithms were proposed. The performance of fast kNN search algorithms is highly influenced by the number of dimensions, number of data points, and data distribution of a data set. In the extreme cas...