the quantitative structure-retention relationship (qsrr) of nanoparticles in roadside atmosphere against the comprehensive two-dimensional gas chromatography which was coupled to high-resolution time-of-flight mass spectrometry was studied. the genetic algorithm (ga) was employed to select the variables that resulted in the best-fitted models. after the variables were selected, the linear multi...

three artificial neural networks (ann) models; general regression neural network (grnn), redial basis function (rbf) and three layer multiple perceptron network were carried out to evaluate the prediction of the apparent metabolizable energy (ame) of wheat and corn from its chemical composition in broiler. input variables included: gross energy (ge), crude protein (cp), crude fiber (cf), ether ...

K-nearest neighbor (KNN) is an effective nonparametric classifier that determines the neighbors of a point based only on distance proximity. The classification performance KNN disadvantaged by presence outliers in small sample size datasets and its deteriorates with class imbalance. We propose local Bonferroni Mean Fuzzy K-Nearest Centroid Neighbor (BM-FKNCN) assigns label query dependent neare...

k is the most important parameter in a text categorization system based on k-Nearest Neighbor algorithm (kNN).In the classification process, k nearest documents to the test one in the training set are determined firstly. Then, the predication can be made according to the category distribution among these k nearest neighbors. Generally speaking, the class distribution in the training set is unev...

Given a set S of n d-dimensional points, the k-nearest neighbors (KNN) is the problem of quickly finding k points in S that are nearest to a query point q. The k-nearest neighbors problem has applications in machine learning for classifications and regression and and also in searching. The secure version of KNN where either q or S are encrypted, has applications such as providing services over ...

This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptivel...

background : in medical studies, when the joint prediction about occurrence of two events should be anticipated, a statistical bivariate model is used. due to the limitations of usual statistical models, other methods such as artificial neural network (ann) and hybrid models could be used. in this paper, we propose a hybrid artificial neural network-genetic algorithm (ann-ga) model to predictio...

This paper presents a decision support tool for educational and vocational guidance, based on the supervised classification method k-nearest neighbors (KNN). This method consists in determining, for each new observation to be classified, the list of nearest neighbors of the observations already classified. The use of the KNN method requires choosing a distance and the most classical one is the ...

The distribution of forest biomass in a river basin usually has obvious spatial heterogeneity in relation to the locations of the upper and lower reaches of the basin. In the subtropical region of China, a large amount of forest biomass, comprising diverse forest types, plays an important role in maintaining the balance of the regional carbon cycle. However, accurately estimating forest ecosyst...

A novel data-driven model for the prediction of bacteriological presence, in form total cell counts, treated water exiting drinking treatment plants is presented. The was developed and validated using a year hourly online flow cytometer data from an operational plant. Various machine learning methods are compared (random forest, support vector machines, k-Nearest Neighbors, Feed-forward Artific...