Sign Languages to Speech Conversion Prototype using the SVM Classifier

2019 ◽  
Author(s):  
Malli Mahesh Chandra ◽  
S Rajkumar ◽  
Lakshmi Sutha Kumar
Keyword(s):  
Svm Classifier ◽  
Sign Languages

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

Identification of Chronic Hypersensitivity Pneumonitis Biomarkers with Machine Learning and Differential Co-expression Analysis

2020 ◽  
Vol 20 ◽  
Author(s):  
Hongwei Zhang ◽  
Steven Wang ◽  
Tao Huang
Keyword(s):  
Feature Selection ◽  
Expression Analysis ◽  
Hypersensitivity Pneumonitis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Great Promise ◽  
Support Vector ◽  
Svm Classifier ◽  
Clinical Tool ◽  
Chronic Hypersensitivity Pneumonitis

Aims: We would like to identify the biomarkers for chronic hypersensitivity pneumonitis (CHP) and facilitate the precise gene therapy of CHP. Background: Chronic hypersensitivity pneumonitis (CHP) is an interstitial lung disease caused by hypersensitive reactions to inhaled antigens. Clinically, the tasks of differentiating between CHP and other interstitial lungs diseases, especially idiopathic pulmonary fibrosis (IPF), were challenging. Objective: In this study, we analyzed the public available gene expression profile of 82 CHP patients, 103 IPF patients, and 103 control samples to identify the CHP biomarkers. Method: The CHP biomarkers were selected with advanced feature selection methods: Monte Carlo Feature Selection (MCFS) and Incremental Feature Selection (IFS). A Support Vector Machine (SVM) classifier was built. Then, we analyzed these CHP biomarkers through functional enrichment analysis and differential co-expression analysis. Result: There were 674 identified CHP biomarkers. The co-expression network of these biomarkers in CHP included more negative regulations and the network structure of CHP was quite different from the network of IPF and control. Conclusion: The SVM classifier may serve as an important clinical tool to address the challenging task of differentiating between CHP and IPF. Many of the biomarker genes on the differential co-expression network showed great promise in revealing the underlying mechanisms of CHP.

Crosslinguistic similarity and variation in the simultaneous morphology of sign languages

2020 ◽  
Vol 37 (4) ◽  
pp. 571-608
Author(s):  
Diane Brentari ◽  
Laura Horton ◽  
Susan Goldin-Meadow
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
Optimality Theory ◽  
Sign Languages ◽  
British Sign Language ◽  
Verb Phrases ◽  
Classifier Predicates ◽  
Crosslinguistic Variation ◽  
Hong Kong Sign Language ◽  
British Sign

Abstract Two differences between signed and spoken languages that have been widely discussed in the literature are: the degree to which morphology is expressed simultaneously (rather than sequentially), and the degree to which iconicity is used, particularly in predicates of motion and location, often referred to as classifier predicates. In this paper we analyze a set of properties marking agency and number in four sign languages for their crosslinguistic similarities and differences regarding simultaneity and iconicity. Data from American Sign Language (ASL), Italian Sign Language (LIS), British Sign Language (BSL), and Hong Kong Sign Language (HKSL) are analyzed. We find that iconic, cognitive, phonological, and morphological factors contribute to the distribution of these properties. We conduct two analyses—one of verbs and one of verb phrases. The analysis of classifier verbs shows that, as expected, all four languages exhibit many common formal and iconic properties in the expression of agency and number. The analysis of classifier verb phrases (VPs)—particularly, multiple-verb predicates—reveals (a) that it is grammatical in all four languages to express agency and number within a single verb, but also (b) that there is crosslinguistic variation in expressing agency and number across the four languages. We argue that this variation is motivated by how each language prioritizes, or ranks, several constraints. The rankings can be captured in Optimality Theory. Some constraints in this account, such as a constraint to be redundant, are found in all information systems and might be considered non-linguistic; however, the variation in constraint ranking in verb phrases reveals the grammatical and arbitrary nature of linguistic systems.

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