Başlık için Bilgisayar Mühendisliği Bölümü listeleme
Toplam kayıt 12, listelenen: 1-12
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A Hybrid artificial intelligence method to classify trichotillomania and obsessive compulsive disorder
(2015-02)Classification of psychiatric disorders is becoming one of the major focuses of research using artificial intelligence approach. The combination of feature selection and classification methods generates satisfactory ... -
A Hybrid PSO-PID Approach For Trajectory Tracking Application of A Liquid Level Control Process
(2015-06)Water level control is a crucial step for steam generators (SG) which are widely used to control the temperature of nuclear power plants. The control process is therefore a challenging task to improve the performance ... -
A Wrapper-Based Approach For Feature Selection And Disorder Classification
(2015-06-22)Feature selection (FS) and classification are consecutive artificial intelligence (AI) methods used in data analysis, pattern classification, data mining and medical informatics. Beside promising studies in the application ... -
Analysis of Brain Functional Changes in High-Frequency Repetitive Transcranial Magnetic Stimulation in Treatment-Resistant Depression
(Clinical EEG & Neuroscience, 2014)Repetitive transcranial magnetic stimulation (rTMS) is a treatment procedure that uses magnetic fields to stimulate nerve cells in the brain, and is associated with significant improvements in clinical symptoms of major ... -
Ant Colony Optimization Based Feature Selection Method for QEEG Data Classification
(Psychiatry Investigation, 2014)Objective:Many applications such as biomedical signals require selecting a subset of the input features in order to represent the whole set of features. A feature selection algorithm has recently been proposed as a new ... -
ARTIFICIAL INTELLIGENCE APPROACH TO CLASSIFY UNIPOLAR and BIPOLAR DEPRESSIVE DISORDERS
(2015)Machine learning (ML) approaches for medical decision making processes are valuable when both high classification accuracy and less feature requirements are satisfied. Artificial neural networks ... -
COMPARISON OF WAVELET FAMILIES FOR MENTAL TASK CLASSIFICATION
(2016-02-05)Wavelet theory is a widely used feature extraction method for raw electroencephalogram (EEG) signal processing. The nature of the EEG signal is non-stationary, therefore applying wavelet transform on EEG signals is a ... -
Discriminating schizophrenia and schizo-obsessive disorder: a structural MRI study combining VBM and machine learning methods
(2016-07-12)Schizo-obsessive disorder is characterized by the clinical syndrome in which comorbid obsessive–compulsive disorder accompanies schizophrenia. A substantial number of studies have investigated the neuropsychological and ... -
Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach
(Clinical EEG and Neuroscience, 2014)Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major ... -
Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance
(2015-01-12)ObjectiveaaThe combination of repetitive transcranial magnetic stimulation (rTMS), a non-pharmacological form of therapy for treating major depressive disorder (MDD), and electroencephalogram (EEG) is a valuable tool for ... -
Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program
(2014)This study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies ... -
Process Control Using Genetic Algorithm And Ant Colony Optimization Algorithm
(Journal of Intelligent Fuzzy Systems, 2014)Artificial life uses biological knowledge and techniques to solve different engineering, management, control and computational problems. Natural systems teach us that very simple individual organisms can form systems ...