Wyniki wyszukiwania - BazTech

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Machine learning models for predicting patients survival after liver transplantation

Jarmulski W., Wieczorkowska A., Trzaska M., Ciszek M., Paczek L.

Computer Science

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2018

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Vol. 19 (2)

223--239

EN

In our work, we have built models predicting whether a patient will lose an organ after a liver transplant within a specified time horizon. We have used the observations of bilirubin and creatinine in the whole first year after the transplantation to derive predictors, capturing not only their static value but also their variability. Our models indeed have a predictive power that proves the value of incorporating variability of biochemical measurements, and it is the first contribution of our paper. As the second contribution we have identified that full-complexity models such as random forests and gradient boosting lack sufficient interpretability despite having the best predictive power, which is important in medicine. We have found that generalized additive models (GAM) provide the desired interpretability, and their predictive power is closer to the predictions of full-complexity models than to the predictions of simple linear models.

2

Analysis of Recognition of a Musical Instrument in Sound Mixes Using Support Vector Machines

Wieczorkowska A., Kubera E., Kubik-Komar A.

Fundamenta Informaticae

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2011

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Vol. 107, nr 1

85-104

EN

Experiments with recognition of the dominating musical instrument in sound mixes are interesting from the point of view of music information retrieval, but this task can be very difficult if the mixed sounds are of the same pitch. In this paper, we analyse experiments on recognition of the dominating instrument in mixes of same-pitch sounds of definite pitch. Sound from one octave (no. 4 in MIDI notation) have been chosen, and instruments of various types, including percussive instruments were investigated. Support vector machines were used in our experiments, and statistical analysis of the results was also carefully performed. After discussing the outcomes of these experiments and analyses, we conclude our paper with suggestions regarding directions of possible future research on this subject.

3

Standard IEEE 802.21 - mechanizmy wspierania mobilności

Machań P., Jordan A., Wieczorkowska A.

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2007

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nr 7

211-216

PL

Rosnąca liczba dostępnych technik bezprzewodowych o zróżnicowanych funkcjonalnościach tworzy naturalną potrzebę opracowania metody koegzystencji i współpracy tych rozwiązań. Przedmiotem artykułu są mechanizmy wspierania mobilności w szerokopasmowych sieciach heterogenicznych serii IEEE 8O2.x, w szczególności IEEE 802.11 (Wi-Fi) i IEEE 802.16 (WiMAX), a także UMTS, oferowane przez standard IEEE 802.21. Zawarte w pracy opisy 802.21 zostały zaczerpnięte ze wstępnej wersji 4.00 standardu IEEE 802.21.

EN

The growing number of wireless technologies raises the demand for interoperability mechanisms between networks. The article discuss the mobility support algorithms for IEEE 802.x standard family {mai- nly concentrated on IEEE 802.11 and IEEE 802.16) and UMTS that are offered by IEEE 802.21 standard. The paper is based on IEEE 802.21 draft 4.0 published in February 2007.

4

Musical Sound Classification based on Wavelet Analysis

Wieczorkowska A.

Fundamenta Informaticae

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2001

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Vol. 47, nr 1,2

175-188

EN

Contents-based searching through audio data is basically restricted to metadata, which are attached manually to the file. Otherwise, users have to look for the specific musical information alone. Nevertheles, when classifiers based on descriptors extracted from sounds analytically are used, automatic classification can be in some cases possible. For instance, wavelet analysis can be used as a basis for automatic classification of audio data. In this paper, classification of musical instrument sounds based on wavelet parameterization is described. Decision trees and rough set based algorithms are used as classification tools. The parameterization is very simple, but the efficiency of classification proves that automatic classification of these sounds is possible.