Autor: Froelich, W. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Medical diagnosis using fuzzy cognitive map classifier
Autorzy:: Froelich, W.
Wrobel, K.
Powiązania:: https://bibliotekanauki.pl/articles/333970.pdf
Data publikacji:: 2015
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: fuzzy cognitive map
medical diagnosis
classification
rozmyta mapa poznawcza
diagnostyka medyczna
klasyfikacja
Opis:: In this study, we address the problem of medical diagnosis by applying Fuzzy Cognitive Map (FCM). A distinctive feature of the FCM is its ability to simulate the development of the disease in time. By this simulation, it is possible to predict the severity of the disease by having future knowledge on current medical investigations. For the first time in this paper, we construct an FCM-based classifier dedicated solely to perform medical diagnosis. To learn the FCM, we use an evolutionary algorithm explicitly specifying the newly designed fitness function. Real, publicly available medical data are applied for the validation and evaluation of the proposed approach.
Źródło:: Journal of Medical Informatics & Technologies; 2015, 24; 247-254
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 2.

Tytuł:: Recognition of lip prints using Fuzzy c-Means clustering
Autorzy:: Wrobel, K.
Froelich, W.
Powiązania:: https://bibliotekanauki.pl/articles/333981.pdf
Data publikacji:: 2015
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: lip print
image processing
clustering techniques
data classification
grafika wargowa
przetwarzanie obrazu
metody grupowania
klasyfikacja danych
Opis:: In this paper a new method for lip print recognition is proposed. The proposed approach is based on Fuzzy c-Means clustering of the characteristics features of lip prints. First, the Hough transform is applied for the recognition of the characteristic features within lip prints, then Fuzzy c-Means clustering is performed to cluster those features. The proposed algorithm applies the results of clustering to find an unknown image withing the collected repository of lip prints. Instead of comparing all pairs of individual characteristic features, the proposed algorithm uses the representatives of clusters for the comparison of images. The advantage of using the proposed method is its increased tolerance to the noise in data and thus the increased efficiency of the recognition. The effectiveness of presented method has been verified experimentally using real-world images. The results are satisfactory and suggest the possibility of using the method in forensic identification systems
Źródło:: Journal of Medical Informatics & Technologies; 2015, 24; 67-73
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 3.

Tytuł:: Medical diagnosis support by the application of associational cognitive maps
Autorzy:: Froelich, W.
Wakulicz-Deja, A.
Powiązania:: https://bibliotekanauki.pl/articles/969801.pdf
Data publikacji:: 2010
Wydawca:: Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:: medical diagnosis
decision support
cognitive maps
Opis:: The objective of the presented research is to construct a model of a patient's health that is based on the idea of cognitive map, a graphical knowledge-representation tool. The application of the proposed model for medical diagnosis is the practical goal of the research. Initially, we provide a brief review of the related works on medical decision support systems and cognitive maps. Afterwards, we sketch the general idea of the conceptual approach to the representation of medical knowledge and provide a new formulation of the medical diagnosis problem. Then, we define our model based on associational cognitive maps and show how it can be applied to diagnosis support. Due to the relative ease of understanding of cognitive map, the model can be easily interpreted and used, thereby making medical knowledge widely available through computer consultation systems. The application example presented is based on a relatively simple, real medical case.
Źródło:: Control and Cybernetics; 2010, 39, 2; 439-456
0324-8569
Pojawia się w:: Control and Cybernetics
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 4.

Tytuł:: Diagnosing Parkinson’s disease using the classification of speech signals
Autorzy:: Froelich, W.
Wróbel, K.
Porwik, P.
Powiązania:: https://bibliotekanauki.pl/articles/333984.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: Parkinson's disease
medical diagnosis
data classification
choroba Parkinsona
diagnostyka medyczna
klasyfikacja danych
Opis:: This paper addressees the problem of an early diagnosis of Parkinson’s disease by the classification of characteristic features of person’s voice. A new, two-step classification approach is proposed. In the first step, the voice samples are classified using standard state-of-the-art classifiers. In the second step, the classified samples are assigned to patients and the final classification process based on majority criterion is performed. The advantage of using our new approach is the resulting, reliable patientoriented medical diagnose. The proposed two-step method of classification allows also to deal with the variable number of voice samples gathered for every patient. Preliminary experiments revealed quite satisfactory classification accuracy obtained during the performed leave-one-out cross validation.
Źródło:: Journal of Medical Informatics & Technologies; 2014, 23; 187-193
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Informacja

Wyszukujesz frazę "Froelich, W." wg kryterium: Autor

Źródło danych

Dostawca treści

Kolekcja

Rok wydania

Wydawca

Temat

Autor

Typ dokumentu

Język