In this study, a hepatitis disease diagnosis study was realized using neural network structure. For this purpose, a multilayer neural network structure was used. Levenberg-Marquardt algorithm was used as training algorithm for the weights update of the neural network. The results of the study were compared with the results of the previous studies reported focusing on hepatitis disease diagnosis and using same UCI machine learning database. We obtained a classification accuracy of 91.87% via tenfold cross validation.
The tongue is one of the few organs with high mobility in the case of severe spinal cord injuries. However, most tongue-machine interfaces (TMIs) require the patient to wear obtrusive and unhygienic devices in and around the mouth. This paper aims to develop a TMI based on the glossokinetic potentials (GKPs), i.e. the electrical signals generated by the tongue when it touches the buccal walls. Ten participants were recruited for this research. The GKP patterns were classified by convolutional neural network (CNN) and support vector machine (SVM). It was observed that the CNN outperformed the SVM in individual and average scores for both raw and preprocessed datasets, reaching an accuracy of 97~99%. The CNN-based GKP processing method makes it easy to build a natural, appealing and robust TMI for the paralyzed. Being the first attempt to process GKPs with the CNN, our research offers an alternative to the traditional brain-computer interfaces (BCIs), which suffers from the instability and low signalto-noise ratio (SNR) of electroencephalography (EEG).
Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha-beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.
Hepatitis is a major public health problem all around the world. Hepatitis disease diagnosis via proper interpretation of the hepatitis data is an important classification problem. In this study, a comparative hepatitis disease diagnosis study was realized. For this purpose, a probabilistic neural network structure was used. The results of the study were compared with the results of the previous studies reported focusing on hepatitis disease diagnosis and using same UCI machine learning database.
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