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While systematic reviews (SRs) are positioned as an essential element of modern evidence-based medical practice, the creation and update of these reviews is resource intensive. In this research, we propose to leverage advanced analytics techniques for automatically classifying articles for inclusion and exclusion for systematic review update. Specifically, we used the soft-margin Support Vector Machine...
In this paper, a method is proposed to predict the putt outcomes of golfers based on their electroencephalogram (EEG) signals recorded before the impact between the putter and the ball. This method can be used into a brain-computer interface system that encourages golfers for putting when their EEG patterns show that they are ready. In the proposed method, multi-channel EEG trials of a golfer are...
The expandable and dynamic web which is a huge repository for information is growing at lightning speed and hence it is hard to find the relevant information from the web. Efficient algorithms reduce the burden of search engines up to a great extent. Query classification is one such aspect and thus a valuable asset for a search engine. Everyday millions of web queries are posted on the web. The main...
Automatic facial expression recognition has been drawn many attentions in both computer vision and artificial intelligence (AI) for the past decades. Although much progress has been made, facial expression recognition (FER) is still a challenging and interesting problem. In this paper, we propose a new FER system, which uses the active shape mode (ASM) algorithm to align the faces, then extracts local...
In this paper we extend our previous work on strategies for automatically constructing noise resilient SVM detectors from click through data for large scale concept-based image retrieval. First, search log data is used in conjunction with Information Retrieval (IR) models to score images with respect to each concept. The IR models evaluated in this work include Vector Space Models (VSM), BM25 and...
This paper presents an asynchronously intracortical brain-computer interface (BCI) which allows the subject to continuously drive a mobile robot. This system has a great implication for disabled patients to move around. By carefully designing a multiclass support vector machine (SVM), the subject's self-paced instantaneous movement intents are continuously decoded to control the mobile robot. In particular,...
Malware is widely used to disrupt computer operation, gain access to users' computer systems or gather sensitive information. Nowadays, malware is a serious threat of the Internet. Extensive analysis of data on the Web can significantly improve the results of malware detection. However malware analysis has to be supported by methods capable of events correlation and cross-layer correlation detection,...
The relational approach to dependency estimation entails the selection of a sufficiently compact 'relevance' subset of training-set objects with which any newly occurring object may be compared in order to estimate its hidden target characteristics. If several comparison modalities are available, a 'relevance' subset of these may additionally have to be chosen via an appropriate selection criterion...
Although empirical studies have demonstrated the usefulness of statistical fault localizations based on code coverage, the effectiveness of these techniques may be deteriorated due to the presence of some undesired circumstances such as the existence of coincidental correctness where one or more passing test cases exercise a faulty statement and thus causing some confusion to decide whether the underlying...
Factor analysis is mainly by extracting the compact representations of speakers' utterances, which are referred to as i-vectors. A low new space called total variability space, which is speaker and channel dependent is trained in the modeling. During the experiments, channel compensation approaches are used to remove the interference included by i-vectors. They are respectively are Nuisance Attribute...
This paper presents a new relevance feedback scheme, which incorporates Extreme Learning Machine (ELM) to content-based image retrieval (CBIR) with relevance feedback. Relevance feedback schemes based on Support Vector Machine (SVM) have been proposed in previous paper. However, the performance of the schemes are often poor which is caused by the low speed of SVM algorithm in high dimension data....
In this paper, we proposed to combine the transform based approach with dimensionality reduction technique for off-line signature verification. The proposed approach has four major phases: Preprocessing, Feature extraction, Feature reduction and Classification. In the feature extraction phase, Discrete Cosine Transform (DCT) is employed on the signature image to obtain the upper-left corner block...
In this paper, we propose an automated method to classify normal/abnormal wall motion in Left Ventricle (LV) function in cardiac cine-Magnetic Resonance Imaging (MRI). Without the need of pre-processing and by exploiting all the images of a cardiac cycle, spatio-temporal profiles are extracted from a subset of diametrical lines crossing opposites segments of the ventricular cavity. Two machine learning...
Classification of structural magnetic resonance imaging (sMRI) brain scans is helpful to detect Alzheimer's disease (AD) at its early stage. In this paper we present a classification scheme that combines the uncorrelated multilinear principal component analysis (UMPCA) and Laplacian Score (LS) methods, which are known to be effective to the structural correlation preserving and redundancy reduction...
The basic goal of this work is to develop a Consonant-Vowel Recognition System (CVRS) for determining a sequence of Consonant-Vowel (CV) units present in a given speech utterance. In this work, we are focusing on developing CVRSs for Indian languages namely Bengali and Odia. This framework of developing CVRSs can be extended to any Indian languages. We have developed two separate CVRSs for Bengali...
Learning algorithms of the support vector machine is to map the input vector to a high dimensional space through certain kernel function and separate the image of the original linear input vector with the maximum of interval under consideration. This paper is about the limb motion recognition problem of stroke patients, mapping the input vector to the reproducing kernel RKHS (reproducing Kernel Hilbert...
The Local Concentration based feature extraction approach is take into consideration to be able to very effectively extract position related information from messages by transforming every area of a message to a corresponding LC feature. To include the LC approach into the entire process of spam filtering, a LC model is designed, where two kinds of detector sets are initially generated by using term...
Support vector machine (SVM) is a popular method for classification in data mining. The canonical duality theory provides a unified analytic solution to a wide range of discrete and continuous problems in global optimization. This paper presents a canonical duality approach for solving support vector machine problem. It is shown that by the canonical duality, these nonconvex and integer optimization...
The class label of each feature vector in the dataset is respectively added in the corresponding feature vector as a feature value, which build a new vector called altered feature vector, all of which compose the altered dataset. It is demostrated that an SVM based on the altered dataset has advantages such as high generilization performance and little structure risk, compared with an SVM based on...
In this work we propose Inclusive vector to keep the key words available in natural language database. The inclusive vectors are generated by the process of extraction of words given in the source and the cited items of records published in the ISI Thompson Citation Indexes. The proposed inclusive vector exhibits related words and the degree of their relationships. In this work we present the results...
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