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Effective working of brain computer interface largely depends upon mental state and vigilance level of human brain. Electroencephalogram signal undergoes for unpredictable changes when vigilance state of human brain alters widely and sometimes cause wrong interpretation by brain computer interface during operation. Hence, brain computer interface needs to investigate subject's brain alertness level...
The realization of robotic systems that understands human intentions and produces accordingly complex behaviors is needed particularly for disabled persons, and would consequently benefit the aged. For this purpose, a control technique that recognizes human intentions from neural responses called brain machine interface (BMI) have been suggested. The unique ability to communicate with machines by...
In this study, the assessment of three different feature selection methods including Information Gain (IG), Gini Index (GI), and CHI square (CHI2) is made by utilizing two popular pattern classifiers, namely Artificial Neural Network (ANN) and Decision Tree (DT), on the classification of Turkish e-mails. The feature vectors are constructed by the bag-of-words feature extraction method. This paper...
In this study, the effect of dimension for a feature vector on the classification of Turkish e-mails as spam or legitimate is investigated. Although hundreds of experimental studies are achieved especially for English, which is a non-agglutinative language, the number of efforts for Turkish, which is one of the most popular agglutinative languages in the world, is counted something on the fingers...
Detection of cardiac abnormalities through annotation and modeling based on 3-Lead ECG data has been reported in literature very extensively. However, we realize that analysis based on 12-Lead, dimension resolved, ECG data is vital for accurate detection of critical cardiac events such as Myocardial Infarction (MI), Ischemia, Bundle Branch Blocks, Pericarditis etc. and understanding of underlying...
The detection of concealed weapons is one of the biggest challenges facing homeland security. It has been shown that each weapon can have a unique fingerprint, which is an electromagnetic signal determined by its size, shape, and physical composition. Extracting the signature of each weapon is one of the major tasks of any detection system. In this paper, feature extraction of a new metal detector...
This paper presents a system for detecting breast cancer based on moments. Instead of trying to improve the applied classifier we focused on improving the input attributes. We extracted new features from database samples using the first four moments namely, mean, variance, skewness and kurtosis. Through simulations, 10-fold cross validation method was applied to the Wisconsin breast cancer database...
In this paper, we present a comparison Support Vector Machine (SVM) and Artificial Neural Network (ANN) for classification of electrooculogram (EOG) signals acquired under specific eye movements. These methods that are required for an eye controlled system are compared by means of their accuracy and response time. Acquired EOG signals consist of 5 different eye movements - being horizontal (right...
In this present work, a technique for differentiation of normal and cirrhotic liver segmented regions of interest (SROIs) based on Laws' masks analysis is reported. Thirty four B-mode ultrasound images taken from 22 normal volunteers and 12 patients suffering from liver cirrhosis were collected from Department of Radiodiagnosis and Imaging, PGIMER, Chandigarh, India. The filtered texture images are...
This paper presents a support vector machine (SVM), The color images and text to effectively classify the large image database. In order to evaluate the performance of this method, it will be this way for the artificial neural network, this method proved to be superior to other methods.
This paper presents comparison of different classification algorithms which are Linear Discriminant Analysis, Support Vector Machines and Neural networks for EEG signals recorded during mental and motor tasks from a subject. The purpose was to determine an optimum classification scheme that could be efficiently used in a brain-computer interface application. Each EEG data set were first excluded from...
Electromyography (EMG) signal is a measure of muscles' electrical activity and usually represented as a function of time, defined in terms of amplitude, frequency and phase. This biosignal can be employed in various applications including diagnoses of neuromuscular diseases, controlling assistive devices like prosthetic/orthotic devices, controlling machines, robots, computer etc. EMG signal based...
Today's advanced muscular sensing and processing technologies have made the acquisition of electromyography (EMG) signal which is valuable. EMG signal is the measurement of electrical potentials generated by muscle cells which is an indicator of muscle activity. Other than rehabilitation engineering and clinical applications, EMG signals can also be employed in the field of human computer interaction...
A web text classification method using a neural network is presented here. The proposed method can classify a set of English text documents into a number of given classes depending on their contents where the number of such classes is not known a priori. Text documents, internet edition of news paper, from various faculties of games and sports are considered for experimentation. The method is found...
The paper deals with an application of spectral analyses to recognition and classification of sound signals. Spectral analysis is a possible method to obtain information for classification of signals. The short time spectral analyses of segmented sound signals of a car engine are presented. The application of classification with neural network is shown. The real signal of sound car engines was used.
Using the Mallat fast algorithm with sym5 wavelet, the pulse waves of 20 heroin druggers and 20 healthy normal subjects are decomposed into two levels. The squared distances from the third and tenth scale coefficients in the second-level decomposition of every pulse wave to the global mean value are used to form a feature vector. The extracted feature vectors have good separable characteristics in...
In this paper, a new method for face localization in color images, which is based on co-evolutionary systems, is introduced. The proposed method uses a co-evolutionary system to locate the eyes in a face image. The used coevolutionary system involves two genetic algorithm models. The first GA model searches for a solution in the given environment, and the second GA model searches for useful genetic...
We propose a classification model for the cognitive level of question items in examinations based on Bloom's taxonomy. The model implements the artificial neural network approach, which is trained using the scaled conjugate gradient learning algorithm. Several data preprocessing techniques such as word extraction, stop word removal, stemming, and vector representation are applied to a feature set...
Adaptive Resonance Theory Network I (ART1) is a neural network concerning unsupervised learning. It is the first member of the ART family. ART1 can learn and recognize binary patterns. The basic idea in ART1 is that the input vector is compared to the prototype vectors in order of decreasing similarity until a prototype vector close enough to the input vector is found. In this paper, we are going...
There are some problems to be resolved for speech emotion recognition, such as the dimension of feature sets is usually too high and the redundancy among various features is relatively stronger. Considering these problems, the factor analysis and majority voting based speech emotion recognition was proposed. How to extract emotional factors from global statistical features and GMM super vectors was...
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