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Epileptic seizure is one of the most common neurological diseases around the world. It is clinical symptoms and/or signs due to abnormal excessive or synchronous neuronal activity in the human brain. Electroencephalogram (EEG) that measures the electrical activity of the brain generated by the cerebral cortex nerve cells, is the most utilized test to detect the seizure activities by visual scanning...
With the appearance and development of the technology of malicious codes and other unknown threats, information security has drawn people's attention. In this paper, we investigate on behavior-based detection which is different from traditional static detection technology. Firstly, we discuss the procedure in detail, especially feature extraction and classification. Several machine learning methods...
The most common cause of blindness in the world by far is known to be the Glaucoma condition. The increase in the ratio of cup to the disc area and the thinning of retinal layers are the most common symptoms of Glaucoma. Functional and structural features of the eye should be examined in order to distinguish an eye with Glaucoma from a healthy eye. In this study, the texture information in Optical...
Human brain is considered as complex system having different mental states e.g., rest, active or cognitive states. It is well understood fact that brain activity increases with the cognitive load. This paper describes the cognitive and resting state classification based on EEG features. Previously, most of the studies used linear features. EEG signals are non-stationary in nature and have complex...
Electroencephalography is most common noninvasive neuroimaging modality and it is widely used for measuring brain electrical signals. Measurement of electrical signals from the scalp requires high density electrodes and low noise amplifier. It is well known fact that neural activity increased with increasing the mental work e.g., IQ task in our case. In this paper, non-linear features have been used...
Student performance classification is a challenging task for teacher and stakeholder for better academic planning and management. Data mining can be used to find knowledge from student data to improve the performance of classifying model. Before applying a classification model, feature selection method is proposed in data preprocessing process to find out the most significant and intrinsic features...
Electroencephalographic (EEG) patterns are electrical signals generated in association with neural activities. Most anomalies in brain functioning manifest with their signature characteristics in EEG pattern. Epileptic seizure, which is a brain abnormality well-studied through EEG analysis, is an abnormal harmonious neural activity in the brain characterized by the presence of spikes in EEG. An automated...
Electroencephalography signals, which are a measure of brain functioning, can be used to distinguish between alcoholic and non-alcoholic due to adverse effect of alcohol on the brain. In the paper, proposed is three test scenarios carried out with different classifiers. The first case involves use of raw data as the feature set and in the second case, derived features after wavelet decomposition like...
Researches indicate that electroencephalography (EEG) can be used to classify data of imagined speech. It can be further utilized to develop speech prosthesis and synthetic telepathy systems. The objective of this paper is to improve the classification performance in imagined speech by selecting the features that extract maximum discriminatory information from the data. The features extracted are...
Epilepsy is a common neurological disorder which is difficult to treat because of its unpredictable and recurrent nature. The electroencephalogram (EEG) is a valuable tool for detecting epileptic seizures. With the aim of reducing the input feature dimensionality, a single median based feature called interquartile range (IQR) was used in this paper for the classification of normal and seizure EEG...
Currently, sleep disorders are considered as one of the major human life issues. There are several stable physiological stages that the human brain goes through during sleep. Nowadays, many biomedical signals such as EEG, ECG, EMG, and EOG offer useful details for clinical setups that are used in identifying sleep disorders. In this work, we propose an efficient technique that could be implemented...
Sarcasm is considered one of the most difficult problem in sentiment analysis. In our observation on Indonesian social media, for certain topics, people tend to criticize something using sarcasm. Here, we proposed two additional features to detect sarcasm after a common sentiment analysis is conducted. The features are the negativity information and the number of interjection words. We also employed...
Epilepsy is a crucial neurological disorder in which patients experience epileptic seizure caused by abnormal electrical discharges from the brain. It is highly common in children and adults at the age of 65–70. Around 1 % of the world's population is affected by this disease. The mechanism of epilepsy is still incomprehensible to researchers; however, 80% of the seizure activity can be treated effectively...
Discovering rare categories and classifying new instances of them are important data mining issues in many fields, but fully supervised learning of a rare class classifier is prohibitively costly in labeling effort. There has therefore been increasing interest both in active discovery: to identify new classes quickly, and active learning: to train classifiers with minimal supervision. These goals...
Genes are encoding regions that form essential building block within the cell and lead to proteins which are achieving various functions. However, some genes may be mutated due to internal or external factors and this is a main cause for various diseases. The latter case could be discovered by closely examining samples taken from patients to identify faulty genes. In other words, it is important to...
Medicine is one of the major fields where the application of artificial intelligence primarily deals with construction of programs that perform diagnosis and make therapy recommendations. In digital mammography, data mining techniques are used to detect and characterize abnormalities in images and clinical reports. In the existing approaches, the mammogram image classification is done in either clinical...
This paper proposes a non-intrusive method to predict/estimate the intracranial pressure (ICP) level based on features extracted from multiple sources. Specifically, these features include midline shift measurement and texture features extracted from CT slices, as well as patient's demographic information, such as age. Injury Severity Score is also considered. After aggregating features from slices,...
Next generation sequencing (NGS) technology has increasingly become the backbone of transcriptomics analysis, but sequencer error causes biases in the read counts. In this paper we establish a framework for predicting true sequences from NGS data. We formulate this task as a classification problem. We define several features, such as log likelihood ratio of estimated true counts, error probability...
This paper presents a wavelet-based texture analysis method for classification of melanoma. The method applies tree-structured wavelet transform on different color channels of red, green, blue and luminance of dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients. Feature extraction and a two-stage feature selection method, based on entropy and correlation,...
In Pattern recognition, ensembles of classifiers are used to increase the performance and accuracy of classification systems. The creation of ensembles, selection of base classifiers and combining the decisions of the classifiers is an active research area. In this paper we propose a method of ensemble creation that is based on fuzzy clustering (Fuzzy C Mean) and fuzzy entropy; and named as Fuzzy...
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