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Author profiling, that is, determining the demographic attributes -such as gender, age, nationality, language, religion, and others- of an author for a given document, has been approached from different areas, especially from linguistics and natural language processing, by extracting different types of features from training documents, usually content- and style-based features.This work addresses...
Machine learning algorithms known for their performance in using historical data and examples to predict and classify unknown instances. Decision tree is an efficient machine learning approach that can use data only without the involvement of decision maker to improve the decision making process. Multi-Criteria Decision Analysis (MCDA)is another paradigm used for data classification. In this paper,...
Acoustic Echo Cancellation is very important in modern day communication owing to the ubiquitous spread of hands free telephony and voice over internet protocol systems. In this paper, a multi resolution non negative matrix factorisation (NMF) based acoustic echo cancellation method is proposed. This acoustic echo cancellation addresses two important research issues. Unlike conventional filter based...
In this paper, a methodology for a texture-based classification between sand and rock images will be proposed, and an object recognition algorithm is added for the sand images. Eighteen types of sand and rock textures have been used to extract different type of features which are hypothetically should help in the classification process. The features discrimination ability were tested and ranked using...
Brain Computer Interface (BCI) connects the human brain and a computer to help physically disabled and impaired people. Electrocorticography (ECoG) proved an effective modality as a BCI platform. ECoG is a new BCI system signal platform. BCI processes brain signals to extract features which are classified through classification algorithms. Feature sets from signals are large and hence, to optimize...
responds from academic questionnaire generally contains many comments, advice and suggestions. This responds is not processed systematically due to lack of method to process. whereas such information might be very useful as additional source in decision making. Opinion mining is well suited to address the issue. The objective of this study is to develop opinion classification system using Maximum...
This paper presents a study of spectral entropy analysis on speech for the possible prediction of depression in speakers who are at risk of committing suicide, when the symptom of depression strikes, unless admitted and having a proper treatment in time. Prediction is primarily necessary task to prevention of that life-threatening risk. In this study the full-band and further sub-band entropies of...
In this paper we analyze the texturedness decision time Td needed to evaluate the texturedness level of a given audio signal. We correlate the subjective values of Td with the objective time parameter Tobs of a recently proposed audio texturedness indicator [1]. As Tobs varies, speech texturedness measures are highly variant but got low texturedness values, whereas classic audio textures and academic...
Users newly enter a recommender system can not get personalized recommendation due to the lack of personal profiles. An interview process that asks new users to rate a set of items (the seed set) will help user modeling and improve user experience. Traditional seed set generation approaches often concentrate on item-wise properties instead of aiming at finding the optimal seed set. We propose a simple...
Regret is a negative social affect arising from upward counterfactual thinking for adverse outcomes. The neurophysiologic markers of identifying regret based on feature extraction from EEG signal has yet to be investigated. The EEG signals were recorded while 25 subjects performed a gambling task involving feedback for the subjects' chosen selection, the alternative selection, or for both. After the...
In this paper, a novel technique Is proposed for detecting cardiac arrhythmias using signals obtained from a multi-lead electrocardiogram (ECG). The method employs the use of two non-linear features namely detrended fluctuation analysis and sample entropy. The features are calculated on signals obtained after performing discrete wavelet transform on the incoming raw ECG data and selecting the diagnostically...
Classifying motor imagery brain signals where the signals are obtained based on imagined movement of the limbs is a major, yet very challenging, step in developing Brain Computer Interfaces (BCIs). Of primary importance is to use less data and computationally efficient algorithms to support real-time BCI. To this end, an algorithm that exploits the sparse characteristics of EEGs is proposed to classify...
This paper adopts the maximum model for English part of speech tagging. It makes pre-tagging for the word that has the only part of speech during the pretreatment of corpus, which adds many context features that can be utilized. We also improve the tagging algorithm, and take into account the whole optimization of POS series without extra computation, and the accuracy of tagging is also improved....
Tracking objects like a basketball from a monocular view is challenging due to its small size, potential to move at high velocities as well as the high frequency of occlusion. However, humans with a deep knowledge of a game like basketball can predict with high accuracy the location of the ball even without seeing it due to the location and motion of nearby objects, as well as information of where...
The paper presents an approach for feature selection in human activity recognition. Features are extracted based on spatiotemporal orientation energy and activity template, while feature reduction has been studied thoroughly using various techniques. Due to high dimensional data from extraction phase, a model with less features which are important and significant can build attractive, interpretative...
Distributed Denial of Service (DDoS) has already been one of the most serious threats to network security, and entropy-based approaches for DDoS attack detection are appealing since they provide more detailed insights than traditional traffic volume-based methods. In this paper, we propose a novel entropy-based DDoS attack detection approach by constructing entropy vectors of different features from...
Texture classification is essential part in automated industry and medical diagnosis. Traditional approaches for texture classification consider the gray scale image with intensity transition and variations in the texture image. Modern approaches use color information to add extra features to the classifier for stronger classification. Compared to Neural Networks, Support Vector Machines are more...
This manuscript present a gender classification technique uses the (2D)2PCA, Gabor filter, and SVM for classifying the gender from occluded and non occluded face images. Present work explores two approaches, i.e., Fusion at feature level, and fusion at classifier level. Experimental result shows that, both the proposed approaches give an acceptable result on non-occluded face image database. For occlude...
To make the traditional support vector data description (SVDD) achieve better generalization performance and more robust against noise, a selective ensemble method based on correntropy and Renyi entropy is proposed. In this proposed ensemble method, the correntropy between the radii of the basis classifiers and the radius of the ensemble is utilized to substitute the sum-squared-error (SSE) criterion...
Recommender system has become one of the most promising techniques in the era of big data. It aims to help users to quickly find the valuable information from the massive data. Many recommendation approaches have been proposed in recent years. Currently, a majority of researchers still pay attention on designing more effective and efficient methods, and they usually put all the user data into model...
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