Autor: Łęski, J. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A new approach for the clustering using pairs of prototypes
Autorzy:: Jezewski, M.
Czabanski, R.
Leski, J.
Horoba, K.
Powiązania:: https://bibliotekanauki.pl/articles/333693.pdf
Data publikacji:: 2015
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: fuzzy clustering
pairs of prototypes
fuzzy rule-based classification
grupowanie rozmyte
pary prototypów
rozmyta klasyfikacja oparta na regułach
Opis:: In the presented work two variants of the fuzzy clustering approach dedicated for determining the antecedents of the rules of the fuzzy rule-based classifier were presented. The main idea consists in adding additional prototypes (’prototypes in between’) to the ones previously obtained using the fuzzy c-means method (ordinary prototypes). The ’prototypes in between’ are determined using pairs of the ordinary prototypes, and the algorithm based on distances and densities finding such pairs was proposed. The classification accuracy obtained applying the presented clustering approaches was verified using six benchmark datasets and compared with two reference methods.
Źródło:: Journal of Medical Informatics & Technologies; 2015, 24; 113-121
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Improving the efficacy of automated fetal state assessment with fuzzy analysis of delivery outcome
Autorzy:: Czabanski, R.
Jezewski, M.
Horoba, K.
Jezewski, J.
Leski, J.
Powiązania:: https://bibliotekanauki.pl/articles/333655.pdf
Data publikacji:: 2015
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: fetal monitoring
fuzzy inference
support vector machines
supervised learning
monitorowanie płodu
wnioskowanie rozmyte
maszyna wektorów nośnych
uczenie nadzorowane
Opis:: A number of methods of the qualitative assessment of fetal heart rate (FHR) signals are based on supervised learning. The classification methods based on the supervised learning require a set of training recordings accompanied by the reference interpretation. In the real data collections the class of signals related to fetal distress is usually under-represented. Too small percentage of distress patterns adversely affects the effectiveness of the automated evaluation of the fetal state. The paper presents a method of equalizing the class sizes based on the reference assessment of the fetal state with the fuzzy analysis of the newborn attributes. The supervised learning with increased number of the FHR signals, which are characterized by the highest rate of the fuzzy inference leads to significant increase of the efficacy of the qualitative assessment of the fetal state using the Lagrangian support vector machine.
Źródło:: Journal of Medical Informatics & Technologies; 2015, 24; 223-230
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Influence of gestational age on neural networks interpretation of fetal monitoring signals
Autorzy:: Jeżewski, M.
Czabański, R.
Horoba, K.
Wróbel, J.
Łęski, J.
Jeżewski, J.
Powiązania:: https://bibliotekanauki.pl/articles/333505.pdf
Data publikacji:: 2008
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: monitoring płodu
kardiotokografia
klasyfikacja
sieci neuronowe
fetal monitoring
cardiotocography
classification
neural networks (NN)
Opis:: Cardiotocographic monitoring (CTG) is a primary biophysical monitoring method for assessment of the fetal state and is based on analysis of fetal heart rate, uterine contraction activity and fetal movement signals. Visual analysis of CTG traces is very difficult so computer-aided fetal monitoring systems have become a standard in clinical centres. We proposed the application of neural networks for the prediction of fetal outcome using the parameters of quantitative description of acquired signals as inputs. We focused on the influence of the gestational age (during trace recording) on the fetal outcome classification quality. We designed MLP and RBF neural networks with changing the number of neurons in the hidden layer to find the best structure. Networks were trained and tested fifty times, with random cases assignment to training, validating and testing subset. We obtained the value of sensitivity index above 0.7, what may be regarded as good result. However additional trace grouping within similar gestational age, increased classification quality in the case of MLP networks.
Źródło:: Journal of Medical Informatics & Technologies; 2008, 12; 137-142
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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