Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76

Nonindigenous Aquatic Species

Online resource center, maintained by U.S.G.S., provides information, data, links about exotic plants, invertebrates, vertebrates, diseases and parasites. Central repository contains accurate and spatially referenced biogeographic accounts of alien aquatic species. Search for species by state, drainage area, citation in texts; find fact sheets, maps showing occurrence in the U.S. Or, for each taxon, review list of exotic species, find scientific, common name, photo, status; link to facts and distribution map. Educational levels: General public, High school

Multi-Way Factorization Machine For Sentiment Analysis

Sentiment analysis is a process of learning the relationship between sentiment label and text. The research value of sentiment analysis is two-fold: first, it has a wide range of applications in many sectors and industries, e.g., the industry has flourished due to the proliferation of commercial applications such as using sentiment analysis as an integrated part of customer experience strategy. Second, it offers an array of new challenging problems for research community such as word feature embedding and machine learning. Albeit earlier methods such as Naïve Bayes (NB), Random Forest (RF), k-Nearest-Neighbours (kNN), Support Vector Machine (SVM) and more recent methods such as Deep Learning (DL) methods are effective, they are primarily designed for shorter or longer textual data thus are not able to maintain a robust performance across a variety of text with diverse lengths. In reality, some text is as abbreviated as one single word while others are so pleonastic that are over thousands of words. Moreover, ad hoc combination of feature embedding and learning methods makes it more difficult to choose the right approach for different types of textual data. Undoubtedly an integrated feature embedding and sentiment analysis method is desirable. In this thesis, we introduce multi-way FM as a new method for sentiment analysis accounting for higher-order feature interaction. We demonstrate the performance and flexibility of the FM method to other competing methods by tuning a single parameter to accommodate both shorter Twitter and longer movie review documents

A Study of Spam E-mail classification using Feature Selection package

Feature selection (FS) is the technique of selecting a subset of relevant features for building learning models. FS algorithms typically fall into two categories: feature ranking and subset selection. Feature ranking ranks the features by a metric and eliminates all features that do not achieve an adequate score. Subset selection searches the set of possible features for the optimal subset. Many FS algorithm have been proposed. This paper presents a new FS technique which is guided by Fselector Package. The package Fselector implements a novel FS algorithm which is devoted to the feature ranking and feature subset selection of high dimensional data. This package provides functions for selecting attributes from a given dataset. Attribute subset selection is the process of identifying and removing as much of the irrelevant and redundant information as possible. The R package provides a convenient interface to the algorithm. This paper investigates the effectiveness of twelve commonly used FS methods on spam data set. One of the basic popular methods involves filter which select the subset of feature as preprocessing step independent of chosen classifier, Support vector machine classifier. The algorithm is designed as a wrapper around five classification algorithms. The short description of the algorithm and performance measure of its classification is presented with the spam data set

English Bards and Unknown Reviewers: a Stylometric Analysis of Thomas Moore and the Christabel Review

Fraught relations between authors and critics are a commonplace of literary history. The particular case that we discuss in this article, a negative review of Samuel Taylor Coleridge's Christabel (1816), has an additional point of interest beyond the usual mixture of amusement and resentment that surrounds a critical rebuke: the authorship of the review remains, to this day, uncertain. The purpose of this article is to investigate the possible candidacy of Thomas Moore as the author of the provocative review. It seeks to solve a puzzle of almost two hundred years, and in the process clear a valuable scholarly path in Irish Studies, Romanticism, and in our understanding of Moore's role in a prominent literary controversy of the age

Adversarial Detection of Flash Malware: Limitations and Open Issues

During the past four years, Flash malware has become one of the most insidious threats to detect, with almost 600 critical vulnerabilities targeting Adobe Flash disclosed in the wild. Research has shown that machine learning can be successfully used to detect Flash malware by leveraging static analysis to extract information from the structure of the file or its bytecode. However, the robustness of Flash malware detectors against well-crafted evasion attempts - also known as adversarial examples - has never been investigated. In this paper, we propose a security evaluation of a novel, representative Flash detector that embeds a combination of the prominent, static features employed by state-of-the-art tools. In particular, we discuss how to craft adversarial Flash malware examples, showing that it suffices to manipulate the corresponding source malware samples slightly to evade detection. We then empirically demonstrate that popular defense techniques proposed to mitigate evasion attempts, including re-training on adversarial examples, may not always be sufficient to ensure robustness. We argue that this occurs when the feature vectors extracted from adversarial examples become indistinguishable from those of benign data, meaning that the given feature representation is intrinsically vulnerable. In this respect, we are the first to formally define and quantitatively characterize this vulnerability, highlighting when an attack can be countered by solely improving the security of the learning algorithm, or when it requires also considering additional features. We conclude the paper by suggesting alternative research directions to improve the security of learning-based Flash malware detectors

A Multiple-Objects Recognition Method Based on Region Similarity Measures: Application to Roof Extraction from Orthophotoplans

In this paper, an efficient method for automatic and accurate detection of multiple objects from images using a region similarity measure is presented. This method involves the construction of two knowledge databases: The first one contains several distinctive textures of objects to be extracted. The second one is composed with textures representing background. Both databases are provided by some examples (training set) of images from which one wants to recognize objects. The proposed procedure starts by an initialization step during which the studied image is segmented into homogeneous regions. In order to separate the objects of interest from the image background, an evaluation of the similarity between the regions of the segmented image and those of the constructed knowledge databases is then performed. The proposed approach presents several advantages in terms of applicability, suitability and simplicity. Experimental results obtained from the method applied to extract building roofs from orthophotoplans prove its robustness and performance over popular methods like K Nearest Neighbours (KNN) and Support Vector Machine (SVM)