Data mining methods for prediction of air pollution

The paper discusses the methods of data mining for prediction of air pollution. Two problems in such prediction are important: the generation and selection of the prognostic features, and final prognosis of the pollution level for the next day on the basis of the data of the previous day. In this paper we analyze and compare two methods of feature selection. One applies the genetic algorithm, and the second the linear method of stepwise fit. On the basis of such analysis we are able to select the most important features influencing the prediction. As a mathematical tool for final prediction we apply the neural networks. Three different solutions will be compared: the multilayer perceptron (MLP), radial basis function (RBF) network and support vector machine (SVM)

Comparison of Methods of Feature Generation for Face Recognition

The paper is concerned with the recognition of faces at application of different methods of global feature generation. We check the selected choice of transformations of images, leading to the numerical representation of the face image. The investigated approaches include the linear and nonlinear methods of transformation: principal component analysis (PCA), Kernel PCA, Fisher linear discriminant analysis (FLD), Sammon transformation and stochastic neighbor embedding with t-distribution (tSNE). The representation of the image in the form of limited number of main components of transformation is put to the input of support vector machine classifier (SVM). The numerical results of experiments will be presented and discussed

Deep Classifiers and Wavelet Transformation for Fake Image Detection

The paper presents a computer system for detecting deep fake images in videos. The system is based on continuous wavelet transformation combined with a set of classifiers composed of a few convolutional neural networks of diversified architectures. Three different forms of forged images taken from the FaceForensics++ database are considered in numerical experiments. The results of experiments involving the proposed system have shown good performance in comparison to other current approaches to this particular problem

Fusion of feature selection methods in gene recognition

The paper presents the fusion approach of different feature selection methods in pattern recognition problems. The following methods are examined: nearest component analysis, Fisher discriminant criterion, refiefF method, stepwise fit, Kolmogorov-Smirnov criteria, T2-test, Kruskall-Wallis test, feature correlation with class, and SVM recursive feature elimination. The sensitivity to the noisy data as well as the repeatability of the most important features are studied. Based on this study, the best selection methods are chosen and applied in the process of selection of the most important genes and gene sequences in a dataset of gene expression microarray in prostate and ovarian cancers. The results of their fusion are presented and discussed. The small selected set of such genes can be treated as biomarkers of cancer

Application Of Sfg In Learning Algorithms Of Neural Networks

The paper presents application of signal flow graphs (SFG) and adjoint flow graphs (AFG) in determination of gradient vector for feedforward neural networks. The presented approach is universal and applicable in the same form irrespective of the particular structure of the network. The applicability of the method has been shown on the example of different types of neural networks: multilayer perceptron, sigma--pi network, generalized radial basis network and multilayer Volterra network. It finds application in any gradient based learning algorithms of neural networks. Some applications of this method, concerning the prediction and identification of the nonlinear dynamic plants are presented and discussed in the paper. 1: Introduction In this paper we present the simple uniform approach to gradient generation, based on the application of signal flow graph (SFG) description [2] and so called adjoint flow graph (AFG). Its advanteges will be illustrated on the examples of feedforward mult..