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1
Content available A Theoretical Review of Topological Organization for Wireless Sensor Network
Patil Sarang Dagajirao, Patil Pravin Sahebrao
International Journal of Electronics and Telecommunications
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2021
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Vol. 67, No. 3
507--515
EN
The recent decades have seen the growth in the fields of wireless communication technologies, which has made it possible to produce components with a rational cost of a few cubic millimeters of volume, called sensors. The collaboration of many of these wireless sensors with a basic base station gives birth to a network of wireless sensors. The latter faces numerous problems related to application requirements and the inadequate abilities of sensor nodes, particularly in terms of energy. In order to integrate the different models describing the characteristics of the nodes of a WSN, this paper presents the topological organization strategies to structure its communication. For large networks, partitioning into sub-networks (clusters) is a technique used to reduce consumption, improve network stability and facilitate scalability.
2
Content available remote Implementation of the game model of the Polish Ekstraklasa team using machine learning techniques
Pogodziński Jakub
Annals of Computer Science and Information Systems
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2021
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Vol. 26
231
EN
The aim of the thesis is to create a model defining the style of play of a team playing in the Polish Ekstraklasa. The limitation to the highest Polish league class is dictated by the differences in the style of play depending on the league. The model is to be created on the basis of data about the team's game. To build the model, supervised and unsupervised learning techniques will be used and compared to find the relationship between the team's statistics and the determination of its playing style.
3
Content available A new method for automatic determining of the DBSCAN parameters
Starczewski Artur, Goetzen Piotr, Er Meng Joo
Journal of Artificial Intelligence and Soft Computing Research
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2020
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Vol. 10, No. 3
209--221
EN
Clustering is an attractive technique used in many fields in order to deal with large scale data. Many clustering algorithms have been proposed so far. The most popular algorithms include density-based approaches. These kinds of algorithms can identify clusters of arbitrary shapes in datasets. The most common of them is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The original DBSCAN algorithm has been widely applied in various applications and has many different modifications. However, there is a fundamental issue of the right choice of its two input parameters, i.e the eps radius and the MinPts density threshold. The choice of these parameters is especially difficult when the density variation within clusters is significant. In this paper, a new method that determines the right values of the parameters for different kinds of clusters is proposed. This method uses detection of sharp distance increases generated by a function which computes a distance between each element of a dataset and its k-th nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.
4
Content available remote A Note on the Durda, Caron, and Buchanan Word Ambiguity Detection Algorithm
Urban Roman, Anisimowicz Hubert
Fundamenta Informaticae
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2019
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Vol. 168, nr 1
79--88
EN
In 2010 Durda, Caron, and Buchanan published a paper in INFOR: Information systems and Operational Research, entitled: An application of operational research to computational linguistics: Word ambiguity. In this article the authors developed “a new measure of word ambiguity (e.g., homonymy and polysemy) for use in psycholinguistic research”. In our work we propose some modification of their algorithm.
5
Content available Grupowanie trajektorii w analizie wyników badań klinicznych
Mazurek M.
Symulacja w Badaniach i Rozwoju
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2016
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Vol. 7, nr 1-2
15--24
PL
Wyniki badań klinicznych mogą tworzyć wielowymiarowe szeregi czasowe, które opisują zmiany w czasie istotnych parametrów opisujących stan zdrowia i kondycję pacjenta. Analiza tego typu danych polega na wyodrębnieniu typowych przebiegów - trajektorii w procesie analizy skupień. Klasteryzacja szeregów medycznych wiąże się z transformacją danych wejściowych: regularyzacją szeregu czasowego, uzupełnieniem brakujących danych, standaryzacją zmiennych. W dalszej kolejności należy dobrać liczbę skupień oraz wykonać grupowanie metodą k-średnich, DTW, PDC lub inną. Te algorytmy są dostępne w otwartych środowiskach obliczeń statystycznych, jednak aby ułatwić analitykom ich zastosowanie, został zbudowany pakiet medclust, który dostarcza wysokopoziomowych procedur, domyślnie sparametryzowanych do wyszukiwania skupień.
EN
Clinical researches often involves measuring time-varying parameters of body condition, which forms multidimensional time-series. Typical, representative trajectories can be extracted with clustering algorithms. In order to apply clustering algorithms, raw data has to be preprocessed and this includes regularization of time series, imputation of missing values, values standardization. Next, one of time-series clustering can be applied: Dynamic Time Warping or Permutation Distribution Clustering. These algorithms are already available in open environments for statistical computing like R. In order to facilitate application of the clustering algorithms to the clinical reasarch data, new R package medclust was implemented. It provides analysts with ready-to-use high-level procedures with predefined set of parameters values to analyze clinical trajectories data.
6
Content available Optical character recognition using artifical inelligence technologies
Musiał A., Szczepaniak P.
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
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2014
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nr 2
41--44
EN
The article represents results of the research of an Optical Character Recognition system. Proposed OCR system is able to convert a raster image into the text string, which represents the text shown on the input image. The main innovation is the fact that the system was created without following any strict rules. It was more an innovative research rather than simple programming using ready guidelines.
PL
Celem projektu opisywanego w artykule było przygotowanie działającego systemu do optycznego rozpoznawania znaków, tj. zdolnego przekształcić rastrowy obraz wejściowy w łańcuch znaków odpowiadający zapisanemu tekstowi na obrazie. Nowością jest m.in. fakt wykonania tego systemu bez podążania za z góry znaną architekturą aplikacji, a przygotowanie go w sposób bardziej doświadczalny, czyli wykorzystując podejście nowatorskie.
7
Content available remote Grupowanie obrazów według ich zawartości za pomocą metody Quasi-Color Histogram
Pisarek M.
Przegląd Elektrotechniczny
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2008
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R. 84, nr 6
95-100
PL
W artykule przedstawione zostały zagadnienia związane z dynamiką energetycznych filtrów aktywnych (EFA). Ścisły związek z reakcją układów EFA na zmiany obciążenia mają filtry cyfrowe stosowane w algorytmie sterowania służące do dekompozycji składowych mocy chwilowej. W pracy przedstawione zostały wyniki symulacji i badań laboratoryjnych z porównaniem dynamiki układu EFA dla wybranych filtrów cyfrowych.
EN
This paper considers problems concerning database image clustering by image contents. Several clustering validity techniques and indices have been presented. This paper presents results of series of experiments of clustering real image data set using Quasi-Color Histogram method.
8
Content available remote RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets
Maji P., Pal S.K.
Fundamenta Informaticae
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2007
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Vol. 80, nr 4
475-496
EN
A hybrid unsupervised learning algorithm, termed as rough-fuzzy c-means, is proposed in this paper. It comprises a judicious integration of the principles of rough sets and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in class definition, the membership function of fuzzy sets enables efficient handling of overlapping partitions. The concept of crisp lower bound and fuzzy boundary of a class, introduced in rough-fuzzy c-means, enables efficient selection of cluster prototypes. Several quantitative indices are introduced based on rough sets for evaluating the performance of the proposed c-means algorithm. The effectiveness of the algorithm, along with a comparison with other algorithms, has been demonstrated on a set of real life data sets.
9
Content available remote A New Density-Based Scheme for Clustering Based on Genetic Algorithm
Lin C.-Y., Chang C.-C., Lin C.-C.
Fundamenta Informaticae
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2005
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Vol. 68, nr 4
315--331
EN
Density-based clustering can identify arbitrary data shapes and noises. Achieving good clustering performance necessitates regulating the appropriate parameters in the density-based clustering. To select suitable parameters successfully, this study proposes an interactive idea called GADAC to choose suitable parameters and accept the diverse radii for clustering. Adopting the diverse radii is the original idea employed to the density-based clustering, where the radii can be adjusted by the genetic algorithm to cover the clusters more accurately. Experimental results demonstrate that the noise and all clusters in any data shapes can be identified precisely in the proposed scheme. Additionally, the shape covering in the proposed scheme is more accurate than that in DBSCAN.
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