Set Of Novel Features Research Articles

Improved multi-stage neonatal seizure detection using a heuristic classifier and a data-driven post-processor

After identifying the most seizure-relevant characteristics by a previously developed heuristic classifier, a data-driven post-processor using a novel set of features is applied to improve the performance. The main characteristics of the outputs of the heuristic algorithm are extracted by five sets of features including synchronization, evolution, retention, segment, and signal features. Then, a support vector machine and a decision making layer remove the falsely detected segments. Four datasets including 71 neonates (1023h, 3493 seizures) recorded in two different university hospitals, are used to train and test the algorithm without removing the dubious seizures. The heuristic method resulted in a false alarm rate of 3.81 per hour and good detection rate of 88% on the entire test databases. The post-processor, effectively reduces the false alarm rate by 34% while the good detection rate decreases by 2%. This post-processing technique improves the performance of the heuristic algorithm. The structure of this post-processor is generic, improves our understanding of the core visually determined EEG features of neonatal seizures and is applicable for other neonatal seizure detectors. The post-processor significantly decreases the false alarm rate at the expense of a small reduction of the good detection rate.

Improving Shift‐Reduce Phrase‐Structure Parsing with Constituent Boundary Information

Shift‐reduce parsing enjoys the property of efficiency because of the use of efficient parsing algorithms like greedy/deterministic search and beam search. In addition, shift‐reduce parsing is much simpler and easy to implement compared with other parsing algorithms. In this article, we explore constituent boundary information to improve the performance of shift‐reduce phrase‐structure parsing. In previous work, constituent boundary information has been used to speed up chart parsers successfully. However, whether it is useful for improving parsing accuracy has not been investigated. We propose two different models to capture constituent boundary information, based on which two sets of novel features are designed for a shift‐reduce parser. The first model is a boundary prediction model that uses a classifier to predict the boundaries of constituents. We use automatically parsed data to train the classifier. The second one is a Tree Likelihood Model that measures the validity of a constituent by its likelihood which is calculated on automatically parsed data. Experimental results show that our proposed method outperforms a strong baseline by 0.8% and 1.6% in F‐score on English and Chinese data, respectively, achieving the competitive parsing accuracies on Chinese (84.8%) and English (90.8%). To our knowledge, this is the first time for shift‐reduce phrase‐structure parsing to advance the state‐of‐the‐art with constituent boundary information.

Improving the Performance Against Force Variation of EMG Controlled Multifunctional Upper-Limb Prostheses for Transradial Amputees.

We investigate the problem of achieving robust control of hand prostheses by the electromyogram (EMG) of transradial amputees in the presence of variable force levels, as these variations can have a substantial impact on the robustness of the control of the prostheses. We also propose a novel set of features that aim at reducing the impact of force level variations on the prosthesis controlled by amputees. These features characterize the EMG activity by means of the orientation between a set of spectral moments descriptors extracted from the EMG signal and a nonlinearly mapped version of it. At the same time, our feature extraction method processes the EMG signals directly from the time-domain to reduce computational cost. The performance of the proposed features is tested on EMG data collected from nine transradial amputees performing six classes of movements each with three force levels. Our results indicate that the proposed features can achieve significant reductions in classification error rates in comparison to other well-known feature extraction methods, achieving improvements of ≈ 6% to 8% in the average classification performance across all subjects and force levels, when training with all forces.

A New Feature Vector Based on Gene Ontology Terms for Protein-Protein Interaction Prediction.

Protein-protein interaction (PPI) plays a key role in understanding cellular mechanisms in different organisms. Many supervised classifiers like Random Forest (RF) and Support Vector Machine (SVM) have been used for intra or inter-species interaction prediction. For improving the prediction performance, in this paper we propose a novel set of features to represent a protein pair using their annotated Gene Ontology (GO) terms, including their ancestors. In our approach, a protein pair is treated as a document (bag of words), where the terms annotating the two proteins represent the words. Feature value of each word is calculated using information content of the corresponding term multiplied by a coefficient, which represents the weight of that term inside a document (i.e., a protein pair). We have tested the performance of the classifier using the proposed feature on different well known data sets of different species like S. cerevisiae, H. Sapiens, E. Coli, and D. melanogaster. We compare it with the other GO based feature representation technique, and demonstrate its competitive performance.

Cognitive state recognition using wavelet singular entropy and ARMA entropy with AFPA optimized GP classification

Cognitive state, which is the inner mental state of a person while interacting with an artificial system through man-machine interface, can be affected by various factors, such as fatigue, stress, mental workload, attention deficit, and executive function, among others, which can lead to errors, accidents, or even disasters. One practical solution to this problem is to monitor and recognize the cognitive state of subjects via physiological signals. In this study, a hybrid adaptive flower pollination algorithm-Gaussian process model is proposed to recognize the cognitive state of in-flight pilots. Instead of using the traditional conjugate gradient technique to find optimal hyperparameters, an improved flower pollination algorithm is proposed. The adaptive Lévy strategy is then used to increase the robustness of this algorithm, as well as to enhance the global optimization and generalization capability of the Gaussian process model. In addition to conventional features in the time-frequency domain, a novel set of features involving wavelet singular entropy and autoregressive-moving average entropy is proposed to improve classification accuracy. Experiments are performed through flight simulations in a full flight simulator with six degrees of freedom. Comparable experimental results validate the feasibility of the proposed method for recognizing cognitive state and provide a wide range of conclusions on the feature selection and feature patterns of cognitive state.

Self co‐articulation detection and trajectory guided recognition for dynamic hand gestures

Hand gestures are a natural way of communication among humans in everyday life. Presence of spatiotemporal variations and unwanted movements within a gesture called self co‐articulation makes the segmentation a challenging task. The study reveals that the self co‐articulation may be used as one of the feature to enhance the performance of hand gesture recognition system. It was detected from the gesture trajectory by addition of speed information along with the pause in the gesture spotting phase. Moreover, a new set of novel features in the feature extraction stage was used such as position of the hand, self co‐articulated features, ratio and distance features. The ANN and SVM were used to develop two independent models using new set of features as input. The models based on CRF and HCRF was used to develop the baseline system for the present study. The experimental results suggest that the proposed new set of features provides improvement in terms of accuracy using ANN (7.48%) and SVM (9.38%) based models as compared with baseline CRF based model. There are also significant improvements in the performances of both ANN (2.08%) and SVM (3.98%) based models as compared with HCRF based model.

A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation

Advanced hemodynamic monitoring is a critical component of treatment in clinical situations where aggressive yet guided hemodynamic interventions are required in order to stabilize the patient and optimize outcomes. While there are many tools at a physician’s disposal to monitor patients in a hospital setting, the reality is that none of these tools allow hi-fidelity assessment or continuous monitoring towards early detection of hemodynamic instability. We present an advanced automated analytical system which would act as a continuous monitoring and early warning mechanism that can indicate pending decompensation before traditional metrics can identify any clinical abnormality. This system computes novel features or bio-markers from both heart rate variability (HRV) as well as the morphology of the electrocardiogram (ECG). To compare their effectiveness, these features are compared with the standard HRV based bio-markers which are commonly used for hemodynamic assessment. This study utilized a unique database containing ECG waveforms from healthy volunteer subjects who underwent simulated hypovolemia under controlled experimental settings. A support vector machine was utilized to develop a model which predicts the stability or instability of the subjects. Results showed that the proposed novel set of features outperforms the traditional HRV features in predicting hemodynamic instability.

Texture Classification Using Dense Micro-Block Difference.

This paper is devoted to the problem of texture classification. Motivated by recent advancements in the field of compressive sensing and keypoints descriptors, a set of novel features called dense micro-block difference (DMD) is proposed. These features provide highly descriptive representation of image patches by densely capturing the granularities at multiple scales and orientations. Unlike most of the earlier work on local features, the DMD does not involve any quantization, thus retaining the complete information. We demonstrate that the DMD have dimensionality much lower than Scale Invariant Feature Transform (SIFT) and can be computed using integral image much faster than SIFT. The proposed features are encoded using the Fisher vector method to obtain an image descriptor, which considers high-order statistics. The proposed image representation is combined with the linear support vector machine classifier. Extensive experiments are conducted on five texture data sets (KTH-TIPS, UMD, KTH-TIPS-2a, Brodatz, and Curet) using standard protocols. The results demonstrate that our approach outperforms the state-of-the-art in texture classification.

ANN-Based Hand Gesture Recognition Using Self co-articulated Set of Features

ABSTRACTHand gesture recognition system can be a particularly challenging task because of the presence of several spatio-temporal variations like hand trembling, self co-articulation within a gesture, etc. during the segmentation stage. In this paper, we focus our attention on self co-articulation problem which has not been addressed before. With the addition of a new feature, i.e. removing the hand movement during pause state along with the velocity features within the gestures, we were able to perform the gesture spotting more smoothly for the isolated gestures. Moreover, a new set of novel features was added in the feature extraction stage: (1) number of self co-articulated stroke; (2) orientation of the self co-articulated strokes; (3) position of the hand; (4) distance between the start and end points of a gesture; and (5) ratio between the longest and shortest distances from centre. These features were fed to neural network classifier. The results of experimentation were carried out with 40 gestures and it showed an average true positive rate of 91.25%.

Resting-state whole-brain functional connectivity networks for MCI classification using L2-regularized logistic regression.

Mild cognitive impairment (MCI) has been considered as a transition phase to Alzheimer's disease (AD), and the diagnosis of MCI may help patients to carry out appropriate treatments to delay or even prevent AD. Recent advanced network analysis techniques utilizing resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been widely used to get more comprehensive understanding of neurological disorders at a whole-brain connectivity level. However, how to explore effective brain functional connectivity from fMRI data is still a challenge especially when the ultimate goal is to train classifiers for discriminating patients effectively. In our research, we studied the functional connectivity of the whole brain by calculating Pearson's correlation coefficients based on rs-fMRI data, and proposed a set of novel features by applying Two Sample T-Test on the correlation coefficients matrix to identify the most discriminative correlation coefficients. We trained a L2-regularized Logistic Regression classifier based on the five novel features for the first time and evaluated the classification performance via leave-one-out cross validation. We also iterated 10-fold cross validation ten times in order to evaluate the statistical significance of our method. The experiment result demonstrates that classification accuracy and the area under receiver operating characteristic (ROC) curve in our method are 87.5% and 0.929 respectively, and the statistical results prove that our method is statistically significant better than other three algorithms, which means our method could be meaningful to assist physicians efficiently in "real-world" diagnostic situations.

Using the Natural Scenes’ Edges for Assessing Image Quality Blindly and Efficiently

Two real blind/no-reference (NR) image quality assessment (IQA) algorithms in the spatial domain are developed. To measure image quality, the introduced approach uses an unprecedented concept for gathering a set of novel features based on edges of natural scenes. The enhanced sensitivity of the human eye to the information carried by edge and contour of an image supports this claim. The effectiveness of the proposed technique in quantifying image quality has been studied. The gathered features are formed using both Weibull distribution statistics and two sharpness functions to devise two separate NR IQA algorithms. The presented algorithms do not need training on databases of human judgments or even prior knowledge about expected distortions, so they are real NR IQA algorithms. In contrast to the most general no-reference IQA, the model used for this study is generic and has been created in such a way that it is not specified to any particular distortion type. When testing the proposed algorithms on LIVE database, experiments show that they correlate well with subjective opinion scores. They also show that the introduced methods significantly outperform the popular full-reference peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) methods. Besides they outperform the recently developed NR natural image quality evaluator (NIQE) model.

Analysis and Design of Efficient generalizedForensic framework for Detecting TwitterSpammers

Asocial networking web site could be a platform to make social networks or social relations among those who share interests, activities, backgrounds or real-life connections. Users pay a good deal of your time on known social networks(e.g.Facebook,Twitter, SinaWeibo, etc.), reading news, discussing events and posting their message. Unfortunately, this quality conjointly attracts a big quantity of spammers whoincessantly expose malicious behaviours (e.g. post messages containing commercial topics or URLs, following a bigger quantity of users, etc.), resulting in nice inconvenience on traditional users’ social activities. The propose system will workAIER (Artificial intelligence for emergency response) approach for detecting twitter spammer .We first collected and labelled a large dataset with 34 K trending topics and 20 million tweets then, construct a labelled dataset of users and manually classify users into spammers and non-spammers; after that, abstract a set of novel features from message content and users’ social behavior. Our experiments show that true positive rate of spammers and non-spammers could reach 99.1% and 99.9%.

A novel hybrid energy fraction and entropy-based approach for systolic heart murmurs identification

This paper presents a set of novel features of heart sound for the detection of the abnormality of heart sounds and classification of heart murmurs. The features include energy fraction of the first and the second heart sounds (S1–S2EF), energy fraction of heart murmur (HMEF), the maximum energy fraction of heart sound frequency sub-band (HSEFmax), sample entropy of the first and the second heart sounds component (S1–S2sampen) and sample entropy of heart murmur component (HMsampen). Firstly, the heart sound signals were de-noised and normalized, then decomposed by wavelet packet. The features, such as energy fraction and sample entropy were calculated from the reconstructed selective frequency components of heart sound signals. The support vector machine (SVM) was employed as a classifier to detect the abnormality of heart sound and discriminate heart murmurs. A dataset consisting of 80 normal heart sounds and 167 systolic heart murmurs samples, segmented from 40 healthy volunteers and 67 patients, were used to test and validate the proposed method. The performance of our proposed method was assessed in terms of sensitivity, specificity and accuracy. The result showed that our proposed method exhibited a satisfactory performance with a high accuracy of 97.17%, a specificity of over 98.55% and a sensitivity of over 93.48%. This suggests that the presented method can be used as an effective assistance for cardiac auscultation.

A novel feature-based approach to extract drug-drug interactions from biomedical text.

Knowledge of drug-drug interactions (DDIs) is crucial for health-care professionals to avoid adverse effects when co-administering drugs to patients. As most newly discovered DDIs are made available through scientific publications, automatic DDI extraction is highly relevant. We propose a novel feature-based approach to extract DDIs from text. Our approach consists of three steps. First, we apply text preprocessing to convert input sentences from a given dataset into structured representations. Second, we map each candidate DDI pair from that dataset into a suitable syntactic structure. Based on that, a novel set of features is used to generate feature vectors for these candidate DDI pairs. Third, the obtained feature vectors are used to train a support vector machine (SVM) classifier. When evaluated on two DDI extraction challenge test datasets from 2011 and 2013, our system achieves F-scores of 71.1% and 83.5%, respectively, outperforming any state-of-the-art DDI extraction system. The source code is available for academic use at http://www.biosemantics.org/uploads/DDI.zip.

A novel set of features for continuous hand gesture recognition

Applications requiring the natural use of the human hand as a human–computer interface motivate research on continuous hand gesture recognition. Gesture recognition depends on gesture segmentation to locate the starting and end points of meaningful gestures while ignoring unintentional movements. Unfortunately, gesture segmentation remains a formidable challenge because of unconstrained spatiotemporal variations in gestures and the coarticulation and movement epenthesis of successive gestures. Furthermore, errors in hand image segmentation cause the estimated hand motion trajectory to deviate from the actual one. This research moves toward addressing these problems. Our approach entails using gesture spotting to distinguish meaningful gestures from unintentional movements. To avoid the effects of variations in a gesture’s motion chain code (MCC), we propose instead to use a novel set of features: the (a) orientation and (b) length of an ellipse least-squares fitted to motion-trajectory points and (c) the position of the hand. The features are designed to support classification using conditional random fields. To evaluate the performance of the system, 10 participants signed 10 gestures several times each, providing a total of 75 instances per gesture. To train the system, 50 instances of each gesture served as training data and 25 as testing data. For isolated gestures, the recognition rate using the MCC as a feature vector was only 69.6 % but rose to 96.0 % using the proposed features, a 26.1 % improvement. For continuous gestures, the recognition rate for the proposed features was 88.9 %. These results show the efficacy of the proposed method.

Support vector regression for rate prediction in distributed video coding

A recent trend in video coding is toward the low-complexity distributed techniques which provide an adaptive way to distribute the computational complexity among the encoders and the decoder. One of the well-known architectures for Distributed Video Coding DVC is the Stanford architecture. This structure imposes the presence of a feedback channel from the decoder to the encoder, causing the codec to be impractical in some applications. However, the feedback channel can be eliminated if the encoder estimates the desirable bitrate during the encoding process. In this paper, we introduce a new method for bitrate estimation using ν-SVM regression with the aid of a novel set of features. We also present a Hybrid coding mode which reduces the computational complexity in a conventional Stanford codec. The presented methods are evaluated using three different video sequences. The simulation results for the feedback-free method show that the average decrease in PSNR of the decoded frames is 0.7 dB for low-motion and up to 3 dB for high motion videos. While preserving the same PSNR quality as the conventional Stanford codec, the Hybrid method reduces the computational complexity by a factor 3, thereby speeding up the decoding process by imposing an overhead bitrate of 3 kb/s.

No‐reference image quality assessment algorithm based on Weibull statistics of log‐derivatives of natural scenes

A blind/no-reference (NR) image quality assessment (IQA) algorithm based on natural scenes is developed. The proposed algorithm does not need training on databases of human judgments of distorted images or even prior knowledge about expected distortions (as is the case in most general NR IQA algorithms). To measure the image quality, the introduced approach uses a set of novel features in a spatial domain. The devised features are formed using the Weibull statistics of log-derivatives. When testing the proposed algorithm on the LIVE database, experiments showed that it correlates well with subjective opinion scores. In addition, they indicated that the new method has a good performance when compared with the state-of-the-art methods.

Entirely Blind Image Quality Assessment Estimator

Prior knowledge about anticipated distortions and their corresponding human opinion scores is needed in the most general purpose no-reference image quality assessment algorithms. When creating the model, all distortion types may not be existed. Predicting the quality of distorted images in practical no-reference image quality assessment algorithms is devised without prior knowledge about images or their distortions. In this study, a blind/no-reference opinion and distortion unaware image quality assessment algorithm based on natural scenes is developed. The proposed approach uses a set of novel features to measure image quality in a spatial domain. The extracted features which are from the scenes gist are formed using Weibull distribution statistics. When testing the proposed algorithm on LIVE database, experiments show that it correlates well with subjective opinion scores. They also show that the proposed algorithm significantly outperforms the popular full-reference peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) methods. Not only do the results reasonably well compete with the recently developed natural image quality evaluator (NIQE) model, but also outperform it.

Gaze-Based Relevance Feedback for Realizing Region-Based Image Retrieval

In this paper, a gaze-based Relevance Feedback (RF) approach to region-based image retrieval is presented. Fundamental idea of the proposed method comprises the iterative estimation of the real-world objects (or their constituent parts) that are of interest to the user and the subsequent exploitation of this information for refining the image retrieval results. Primary novelties of this work are: a) the introduction of a new set of gaze features for realizing user's relevance assessment prediction at region-level, and b) the design of a time-efficient and effective object-based RF framework for image retrieval. Regarding the interpretation of the gaze signal, a novel set of features is introduced by formalizing the problem under a mathematical perspective, contrary to the exclusive use of explicitly defined features that are in principle derived from the psychology domain. Apart from the temporal attributes, the proposed features also represent the spatial characteristics of the gaze signal, which have not been extensively studied in the literature so far. On the other hand, the developed object-based RF mechanism aims at overcoming the main limitation of region-based RF approaches, i.e., the frequently inaccurate estimation of the regions of interest in the retrieved images. Moreover, the incorporation of a single-camera image processing-based gaze tracker makes the overall system cost efficient and portable. As it is shown by the experimental evaluation, the proposed method outperforms representative global- and region-based explicit RF approaches, using a challenging general-purpose image dataset.

Models of Performance of Evolutionary Program Induction Algorithms Based on Indicators of Problem Difficulty

Modeling the behavior of algorithms is the realm of evolutionary algorithm theory. From a practitioner's point of view, theory must provide some guidelines regarding which algorithm/parameters to use in order to solve a particular problem. Unfortunately, most theoretical models of evolutionary algorithms are difficult to apply to realistic situations. However, in recent work (Graff and Poli, 2008, 2010), where we developed a method to practically estimate the performance of evolutionary program-induction algorithms (EPAs), we started addressing this issue. The method was quite general; however, it suffered from some limitations: it required the identification of a set of reference problems, it required hand picking a distance measure in each particular domain, and the resulting models were opaque, typically being linear combinations of 100 features or more. In this paper, we propose a significant improvement of this technique that overcomes the three limitations of our previous method. We achieve this through the use of a novel set of features for assessing problem difficulty for EPAs which are very general, essentially based on the notion of finite difference. To show the capabilities or our technique and to compare it with our previous performance models, we create models for the same two important classes of problems-symbolic regression on rational functions and Boolean function induction-used in our previous work. We model a variety of EPAs. The comparison showed that for the majority of the algorithms and problem classes, the new method produced much simpler and more accurate models than before. To further illustrate the practicality of the technique and its generality (beyond EPAs), we have also used it to predict the performance of both autoregressive models and EPAs on the problem of wind speed forecasting, obtaining simpler and more accurate models that outperform in all cases our previous performance models.