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This study investigates the discrimination between calm, exciting positive and exciting negative emotional states using EEG signals. Towards this direction, a publicly available dataset from eNTERFACE Workshop 2006 was used having as stimuli emotionally evocative images. At first, EEG features were extracted based on literature review. Then, a computational framework is proposed using machine learning...
In this paper, the single-channel EEG based classification systems using simple extracted features are investigated. Each classification system contains the following stages: data acquisition, signal decomposition, feature extraction, and classification. In addition to using the filter bank and empirical mode decomposition (EMD) methods for signal decomposition, a sparse discrete wavelet packet transform...
Unknown awareness is very important for many applications such as face recognition. In a typical unknown aware classifier, an “unknown” label is assigned to strange test instances. This study proposes an unknown aware classifier known as UAkNN by extending the well-known kNN classifier. In UAkNN, unknown awareness is achieved by exploiting distances between instances of individual classes. These distances...
The breast cancer is one of the most popular cause of death among women. It is also one of the diseases that can be cured and has high healing chances when it is detected in the early stages [1]. Detecting the cancer and differentiating between the diagnosis that affirm whether a patient has breast cancer or not has been considered as a big challenge. In order to have an accurate diagnosis, Support...
Liver fibrosis is the natural wound healing response to parenchymal injury in chronic liver diseases and may eventually result in liver cirrhosis. Noninvasive imaging methods widely used for the diagnosis of liver fibrosis are Magnetic Resonance Imaging (MRI), Computed tomography (CT), Ultrasound and Elastography. This work aims to extract texture features from ultrasonic liver images. The Artificial...
We defined a set of quantifiable features for authorship categorization. We performed our experiments on public domain literature — all books analyzed were obtained in plain text format through Project Gutenberg's online repository of classic books. We tested three machine learning algorithms: Artificial Neural Network, Naïve Bayes Classifier, and Support Vector Machine with our features. We found...
Due to the huge increase in the size of the data it becomes troublesome to perform efficient analysis using the current traditional techniques. Big data put forward a lot of challenges due to its several characteristics like volume, velocity, variety, variability, value and complexity. Today there is not only a necessity for efficient data mining techniques to process large volume of data but in addition...
Extreme Learning Machine (ELM) is a fast and efficient classifier with single hidden layer feed-forward neural networks. In this paper, the ELM is employed to classify the EEG signals in BCI system, the BCI competition datasets are used to test, the mutual information and classify accuracy are considered as evaluation criteria. Compare with the LDA and SVM, the ELM method could obtain more mutual...
In this study, we deemed further to evaluate the performance of Neural Network (NN) and Support Vector Machine (SVM) in classifying the gait patterns between autism and normal children. Firstly, temporal spatial, kinetic and kinematic gait parameters of forty four subjects namely thirty two normal subjects and twelve autism children are acquired. Next, these three category gait parameters acted as...
A single MRI scan generates a large number of images of various cross sections of the body. This large set of accumulated data makes manual analysis time consuming thus a smart tool for screening is vital. This paper presents a novel classification and segmentation method which has the ability to identify white matter in MRI mages. Based on those findings, a supporting web based tool for the MRI image...
In this paper an effective and most reliable method for appropriate classification of cardiac arrhythmia using automated based Artificial Neural Network (ANN) has been proposed. The results are encouraging and are found to have produced a very confident and efficient arrhythmia classification, which is easily applicable in diagnostic decision support system. In this paper the authors have employed...
Cardiac arrhythmia detection at the initial stage saves the patient from sudden death caused due to cardiac arrest. Arrhythmia can be predicted by detecting Ventricular Tachycardia and Ventricular Fibrillation. There are many techniques and methods for the detection of arrhythmia. The system proposes a highly efficient VF detector. It uses 18 parameters extracted from the ECG as input. These parameters...
Human hand functions range from precise-minute handling to heavy and robust movements. Remarkably, 50 percent of all hand functions are made possible by the thumb. Therefore, developing an artificial thumb which can mimic the actions of a real thumb precisely is a major achievement. Despite many efforts dedicated to this area of research, control of artificial thumb movements in resemblance to our...
Improvement of classification accuracy is importance in data analysis problems. Enhancement of techniques have been proposed previously to address the problems as regard to classification performance, however, the issues of misclassification and noise elimination in the early stage of processing have been ignored by many researchers. If these problems were addressed, the performance of the classification...
This paper proposes an intellectual classification system to recognize normal and abnormal MRI brain images. Nowadays, decision and treatment of brain tumors is based on symptoms and radiological appearance. Magnetic resonance imaging (MRI) is a most important controlled tool for the anatomical judgment of tumors in brain. In the present investigation, various techniques were used for the classification...
Epilepsy is a global problem, and with seizures eluding even the smartest of diagnosis, a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Contemporary researchers went ahead and devised a multitude of methods for automatic epilepsy detection, becoming a reason why one should find the best method out, based on accuracy,...
The fundamental problem in applying geophysical mapping to locate unexploded ordnance (UXO) is distinguishing true UXO from non-UXO. Enhancing the accuracy of UXO detection has multiple benefits, especially in the areas of cost savings and safety. We investigated discrimination approaches using both magnetic field data and numerically modeled data. Libraries of total field magnetic (TFM) responses...
Machine learning is the adaptive process that makes computers improve from experience, by example, and by analogy. So It is a discipline of methodologies that provides, in one form or another, intelligent information processing capabilities for handling real life. Bioinformatics is one of the application of Machine Learning. Bioinformatics is the interdisciplinary science of interpreting biological...
Diagnosis is an important task in medical science because of its criticality, efficiency and accuracy in determining whether or not a patient has a particular disease. This shall further decide the most suitable line of treatment. There has been a large increase in the number of thyroid cases over the past few years. Since thyroid has a complex relation with metabolism and body weight, it is extremely...
Common stream mining tasks include classification, clustering and frequent pattern mining among them, data stream classification has drawn particular attention due to its vast real-time application. Through these applications, the main goal is to efficiently build classification models from data streams for accurate prediction. The development of such model has shown the need for machine learning...
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