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The search of k-nearest neighbors (K-NN) is a very common task. The K-NN is utilized in many algorithms and scientific areas like clustering, classification, machine learning, N-body simulation, triangulation, image processing and/or video processing. However, the naive implementation of the K-NN is very slow. There are many novel algorithms for the K-NN search, but they are usually based on the hierarchical...
Random Forest (RF) classification algorithm is widely used in the area of information retrieval and became a basis for some extended branches of classification and/or regression algorithms. Cluster Forest (CF) represents a particular branch, and brings usually better results than individual clustering algorithms. This article describes a new ensemble clustering algorithm based on CF that internally...
The main goal of this paper is to detect faces from noisy images using three different classification methods and compare the results obtained from the classification methods. The faces are described by a set of images. Many other unsupervised statistical algorithms such as Principal Component Analysis (PCA) or Singular Value Decomposition (SVD) use only one image per person to extract features from...
We present an approach for emotion recognition using information of the pupil. In last years, the pupil variables have been used as an assessment of emotional arousal. In this article, we generate signals of pupil size and gaze position monitored during image viewing. The emotions are provoked by visual stimuli of colored images. Those images were taken from the International Affective Picture System...
The general classification is a machine learning task that tries to assign the best class to a given unknown input vector based on past observations (training data). Most of developed algorithms are very time consuming for large datasets (Support Vector Machine, Deep Neural Networks, etc.). Extreme Learning Machine (ELM) is a high quality classification algorithm that gains much popularity in recent...
Publication activity of researchers is studied inmany papers. Most of them are focused on topics, authors and their cooperation, finding experts or so on. In our paper we are interested in trends in the authors publication activity. We would like to know when and how often authors use their topics, how they combine topics or use new topics. To achieve our goal we use formal concept analysis and polynomial...
This paper proposes a human thermal face recognitionapproach with two variants based on Random linearOracle (RLO) ensembles. For the two approaches, the Segmentation-based Fractal Texture Analysis (SFTA) algorithmwas used for extracting features and the RLO ensembleclassifier was used for recognizing the face from its thermalimage. For the dimensionality reduction, one variant (SFTALDA-RLO) was used...
Multi-variable data relations can define a partial differential equation, which describes an unknown complex function on a basis of discrete observations, using the similarity model analysis methods. Time-series can form an ordinary differential equation, which is analogously possible to replace by partial derivatives of the same type time-dependent observations. Polynomial neural networks can compose...
Starting from the last century, animals identification became important for several purposes, e.g. tracking, controlling livestock transaction, and illness control. Invasive and traditional ways used to achieve such animal identification in farms or laboratories. To avoid such invasiveness and to get more accurate identification results, biometric identification methods have appeared. This paper presents...
Visualization of complex real-world data is an essential part of network processing. Complex high-dimensional or networked data ought to be presented in a form suitable for machine and human analysis. Therefore, advanced methods of dimension reduction or projection to low-dimensional spaces are investigated. In this work we use Differential Evolution as a real-parameter optimization metaheuristic...
Stochastic nature-inspired optimization and search methods depend on streams of integer and floating point numbers generated in course of their execution. The pseudo-random numbers are utilized for in-silico emulation of probability-driven natural processes such as modification of genetic information (mutation, crossover), partner selection, and survival of the fittest (selection, migration) and environmental...
Nature inspired algorithms implement successful optimization and adaptation strategies observed in the nature. Various bio-inspired algorithms mimic the behavioural patterns of plants, animals, their communities and their evolution. Surprisingly, the behavioural patterns and survival strategies of protozoa, one of the most prevalent and successful species on Earth, did not receive significant attention...
Graphs may be used to visualize relationships between objects. Relations are represented by edges and objects are called nodes. When graph is drawn, one can easily see and understand the basic structure of data. Many different applications can be found in social network analysis, computer networks, scientific literature analysis, etc. However drawing large graphs (thousands or a millions of nodes),...
Evolutionary methods and stochastic algorithms in general rely heavily on streams of (pseudo-)random numbers generated in course of their execution. The pseudo-random numbers are utilized for in-silico emulation of probability-driven natural processes such as modification of genetic information (mutation, crossover), partner selection, and survival of the fittest (selection, migration). Deterministic...
Simulations of molecular dynamics play an important role in computational chemistry and physics. Such simulations require accurate information about the state and properties of interacting systems. The computation of water cluster potential energy surface is a complex and computationally expensive operation. Therefore, machine learning methods such as Artificial Neural Networks have been recently...
Many natural networks including social networks are scale-free, i.e. the distribution of node degrees in the network is heavy-tailed and follows the power law. In this work we analyze the scale-free properties of selected subnets of a complex co-authorship network induced by significant nodes (authors) and the evolution of these properties in time. The subnets induced by significant authors are sampled...
Visualization is an important part of Network Analysis. It helps to find features of the network that are not easily identifiable. In this paper we present our approach to the visualization of weighted networks based on Sammon's projection. The network may be seen as a set of data points in the space induced by the incidence relation or as a symmetric matrix of vertex distances. We propose several...
Many real world data or process eave a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properts hies of network. We introduce a method for measuring the dependency between the nodes of a network, that is based on a structure in the local surroundings of the node. The approach extracts relations between the network's...
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