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In this study, we developed an automatic algorithm for sleep-wake detection based on Electrooculography (EOG) in healthy and non-healthy patients. Several features were extracted in time and frequency domains from the EOG signal. The artificial neural network (ANN) was used as a classifier. This pilot study consisted of three aims; the first aim was to utilise only the EOG signal for automatic sleep-wake...
A weighted least squares scheme based on an empirical survival error potential function is proposed in this paper. The empirical survival error potential function provides an error compensation scheme for noise distributions far from being Gaussian. This error compensation procedure is efficiently implemented via a weighted least squares formulation where an analytical solution form is obtained. The...
This paper presents novel time-frequency (t-f) features based on t-f image descriptors for the automatic detection and classification of epileptic seizure activities in EEG data. Most previous methods were based only on signal-related features derived from the instantaneous frequency and the energies of EEG signals generated from different spectral sub-bands. The proposed features are extracted from...
Understanding cognitive responses of human brain is one of the significant research fields where electroencephalography plays vital role in analyzing brain functionality with respect to brain signals. Electromyography is another modality to understand cognitive responses with respect to muscle activation. In this research work, a data set consists of healthy and myopathy has been considered from physionet...
To the trained-eye, experts can often identify a team based on their unique style of play due to their movement, passing and interactions. In this paper, we present a method which can accurately determine the identity of a team from spatiotemporal player tracking data. We do this by utilizing a formation descriptor which is found by minimizing the entropy of role-specific occupancy maps. We show how...
Wireless sensor networks (WSNs) are gaining popularity in practical monitoring and surveillance applications. Because of the limited energy of sensor nodes, many WSNs work in a low duty cycle mode to effectively extend their network lifetime. However, low duty cycling also decreases transmission efficiency and makes data gathering more challenging. By exploiting the redundancy of in real sensing data,...
Aiming at properties of remote sensing image data such as high-dimension, nonlinearity and massive unlabeled samples, a kind of probability least squares support vector machine (PLSSVM) classification method based on hybrid entropy and L1 norm was proposed. Firstly, hybrid entropy was designed by combining quasi-entropy with entropy difference, which was used to select the most "valuable"...
In this article, the probable sub cellular location of a protein is predicted by applying multiobjective particle swarm optimization (MOPSO) based feature selection technique. The feature set is created from the different amino acid compositions of the protein. Thus, the sample of protein versus amino acid compositions (features) constitutes the dataset. The proposed algorithm is designed to find...
This paper proposes a novel method of detecting packed executable files using steganalysis, primarily targeting the detection of obfuscated malware through packing. Considering that over 80% of malware in the wild is packed, detection accuracy and low false negative rates are important properties of malware detection methods. Experimental results outlined in this paper reveal that the proposed approach...
Information extraction from signals has been a long time research topic and numerous signal transforms have been defined for the same purpose. In this paper, a comparative study of various signal transforms on basis of time-frequency (TF) resolution, cross-terms suppression and maximum information content has been presented. The transforms considered for the analysis are Frequency domain transforms,...
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...
Many real-world applications involve multi-label data streams, so effective concept drift detection methods should be able to consider the unique properties of multi-label stream data, such as label dependence. To deal with these challenges, we proposed an efficient and effective method to detect concept drift based on label grouping and entropy for multi-label data. Two methods are proposed to group...
In Twitter, there have been various kinds of Tweets and also the total volume is extremely huge, so some kind of effectively filterable technique in response to the contents of Tweets or each user's purpose is considered to be essential in order for Twitter to be effective at the time of disaster. A framework for study aiming to effectively utilize social media at the time of disaster was proposed...
This research aims at investigating the relationship between Electroencephalogram (EEG) signals and human emotional states. A subject-independent emotion recognition system is proposed using EEG signals collected during emotional audio-visual inductions to classify different classes of continuous valence-arousal model. First, four feature extraction methods based on Approximate Entropy, Spectral entropy,...
This work focuses on non-linear characterization of 61-channel electroencephalogram (EEG) signal for detecting alcoholics using ranked Approximate Entropy (ApEn) parameters. Significant channels that contribute to the detection of alcoholism are selected by ranking the ApEn features based on ANOVA test. In order to classify alcoholics from control, the ranked feature set is applied to two non-linear...
Traditional file recovery methods rely on file system information, which are ineffective when file system information isn't available. File carving is a file recovery method that recovers files according to their structure and content without file system information, which is widely used in digital forensics. As the important carriers of digital information, multimedia files are important digital...
Cross-situational learning, the ability to learn word meanings across multiple scenes consisting of multiple words and referents, is thought to be an important tool for language acquisition. The ability has been studied in infants, children, and adults, and yet there is much debate about the basic storage and retrieval mechanisms that operate during cross-situational word learning. It has been difficult...
Recurrence plot is a useful analysis method for nonlinear time series, and has been widely used in studies of heart rate variability in recent years. In this paper, recurrence plot and corresponding quantification analysis were utilized to analyze the heart rate variability data from healthy people and congestive heart failure sufferers. It was found that the measures for standard recurrence quantification...
Stable local feature detection and representation are the fundamental components of target recognition and image retrieval. The traditional SIFT algorithm's descriptor of the feature points is a 128-element vector, and a lot of redundant information is presence. So the brief and effective expression of the image feature information is the key to improve the performance of the algorithm. This paper...
This paper presents an empirical study on machine learning based sentiment analysis for Vietnamese, in which we focus on the task of sentiment classification. We investigate the task regarding both learning model and linguistics feature aspects. We also introduce an annotated corpus for sentiment classification extracted from hotel reviews in Vietnamese and conduct a series of experiments and analyses...
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