Wyniki wyszukiwania - BazTech

1

Speech nonfluency detection and classification based on linear prediction coefficients and neural networks

Kobus A., Kuniszyk-Jóźkowiak W., Smołka E., Codello I.

Journal of Medical Informatics & Technologies

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2010

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Vol. 15

135--143

EN

The goal of the paper is to present a speech nonfluency detection method based on linear prediction coefficients obtained by using the covariance method. The application “Dabar” was created for research. It implements three different methods of LP with the ability to send coefficients computed by them into the input of Kohonen networks. Neural networks were used to classify utterances in categories of fluent and nonfluent. The first one was Kohonen network (SOM), used to reduce LP coefficients representation of each window, which were used as input data to SOM input layer, to a vector of winning neurons of SOM output layer. Radial Basis Function (RBF) networks, linear networks and Multi-Layer Perceptrons were used as classifiers. The research was based on 55 fluent samples and 54 samples with blockades on plosives (p, b, d, t, k, g). The examination was finished with the outcome of 76% classifying.

2

The diseases classification method on gait abnormalities characteristic contributions

Chandzlik S., Piecha J.

Journal of Medical Informatics & Technologies

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2005

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Vol. 9

187--194

EN

Present medicine uses computers in various applications, especially in a field of a diseases level classification and diagnosis. In many cases an automatic conclusion making units are the main goal of the computer systems usage. The software units are developed for the diseases classification or for monitoring of the disease medical treatment. An example application was described in this paper. It concerns a gait abnormalities level analysis that is described by a data records gathered by insoles of Parotec System for Windows (PSW) [17,18]. The PSW software package is used for visualisation of the gait characteristic static and dynamic characteristic features. In the authors' works many additional data components were distinguished. The field of the applications is located within the neurological gait characteristics also the source applications concern orthopaedics [16,18]. Careful analysis of the data provided the developers with new areas the PSW applications [4,11,13]. For conclusion making units the artificial networks theory was implemented [2,4,11,13]. For more effective training of the neural networks specific characteristic measures were introduced [4,5]. They allow controlling the training process more precisely, avoiding mistakes in current records classification.

3

Modelling of hemodialysis: regression versus neural model

Szmajda R., Szczepaniak P. S.

Journal of Medical Informatics & Technologies

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2004

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Vol. 7

KB41--46

EN

In the paper, evaluation of two approaches to modelling of hemodialysis is performed. Results obtained by regression are compared to those generated by neural models. Differences in the modelling quality are small. Both models shown the same qualitative dependencies between analyzed parameters.

4

Classification of breast thermal images using artificial neural networks

Jakubowska T., Wiecek B., Wysocki M., Drews-Peszynski C., Strzelecki M.

Journal of Medical Informatics & Technologies

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2004

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Vol. 7

MIP41--50

EN

In this paper we present classification of the thermal images in order to discriminate healthy and pathological cases during breast cancer screening. Different image features and approaches for data reduction and classification have been used to distinguish healthy breast one with malignant tumour. We use image histogram and co-occurrence matrix to get thermal signatures and analyze symmetry between left and right side. The most promised method was based on wavelet transformation and nonlinear neural network classifier. The proposed approach was used in the pilot investigations in the medical centre which is permanently using thermograph for breast cancer screening, as an adjacent method for other classical diagnostic method, such as mammography.

5

Application of neural networks in optimization of the recruitment process for sport swimming

Ryguła I., Roczniok R.

Journal of Medical Informatics & Technologies

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2004

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Vol. 7

KB75--82

EN

The essence of the recruitment and selection in sports depends on determining an aptitude vector of the candidate to sport training. For this reason, the recruitment process may be optimized by determining possibly large amount of information on the sport level of the candidate with as small as possible number of tested characteristics, using a mathematical model based on neural networks. The main aim of this work was verification of the usefulness of neural models in optimization of the process of recruitment process, both at sprint distance of 50 m and typically endurance distance of 800 m. The material for the investigation was a group of 80 young swimmers in the youngster and junior category from the Silesian macro-region. For the purpose of verification of the usefulness of neural models, statistical analysis were made of the measurement results of young swimmers and two neural models were developed - for sprint distance (50 m) and endurance distance (800 m). The developed models, based on the architecture of perception networks, has shown capability of generalization and prediction, which has enabled to reach conclusions on practical possibility of using neural networks in optimization of the recruitment and selection process for sport swimming.

6

Evolutionary algorithms and neural networks applied to the computer - aided medical diagnosis

Zaganczyk A.

Journal of Medical Informatics & Technologies

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2002

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Vol. 4

SN21--24

EN

The purpose of presented work is to create a project and computer implementation of complex decision support system used in an important medical field, which is cardiology. This system is applied to support physical diagnosis concern different kinds of myocardial infraction. The system - called NEUROGEN v.01, is a kind of hybrid system, which is a combination of Genetic Algorithm (GA) and Neural Network (NN). The idea of this specific combination is that GA is used as a evolutionary method of learning of NN. In accordance with this special task, the NN is a three-layer feedforward network with eight numbers of input neurons, six numbers of hidden and five number of output neurons. The number of neurons in each layer was appointed on the base of data of the task. In this work, the purpose was to look for the optimal values of the parameters of algorithm, which are: crossover probability, mutation probability, the number of individuals in population, the number of generations of the algorithm and λ - parameter of function of activation which characterize neurons in NN. An extra task is to check if the beginning population has any influence on effectiveness of the system. In this paper there will be presented the way of rising of NEUROGEN v.01 and achieved results.