Search results for "SVM"
showing 10 items of 39 documents
Feature Ranking of Large, Robust, and Weighted Clustering Result
2017
A clustering result needs to be interpreted and evaluated for knowledge discovery. When clustered data represents a sample from a population with known sample-to-population alignment weights, both the clustering and the evaluation techniques need to take this into account. The purpose of this article is to advance the automatic knowledge discovery from a robust clustering result on the population level. For this purpose, we derive a novel ranking method by generalizing the computation of the Kruskal-Wallis H test statistic from sample to population level with two different approaches. Application of these enlargements to both the input variables used in clustering and to metadata provides a…
Recognition of emotional states by visual facial analysis and machine learning
2018
In face-to-face settings, an act of communication includes verbal and emotional expressions. From observation, diagnosis and identification of the individual's emotional state, the interlocutor will undertake actions that would influence the quality of the communication. In this regard, we suggest to improve the way that the individuals perceive their exchanges by proposing to enrich the textual computer-mediated communication by emotions felt by the collaborators. To do this, we propose to integrate a real time emotions recognition system in a platform “Moodle”, to extract them from the analysis of facial expressions of the distant learner in collaborative activities. There are three steps…
Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project.
2016
International audience; Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean of Indirect ImmunoFluorescence (IIF) method, and performed by analyzing patterns and fluorescence intensity. This paper introduces the AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-border cooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of images and double reporting and a Gold Standard database, containing around 1000 double reported images, has been settled. The Gold Standard database is used for optimization of …
[Regular Paper] Detection of H. pylori Induced Gastric Inflammation by Diffuse Reflectance Analysis
2018
Spectral acquisitions contain rich information and thus, are promising modalities for early detection of gastric diseases. In this study, we analyze the diffuse reflectance of the gastric inflammatory lesions induced by the bacterium H. pylori in the mouse stomach. A pipeline has been designed to characterize and classify spectra acquired on mice. The pipeline is based on a band clustering algorithm followed by the computation of meaningful division and subtraction features and by classification with a linear SVM classifier. Currently, the pipeline is able to recognize inflamed stomach's spectra with an accuracy of 98%. These results are promising and the same pipeline could be adapted for …
Klasifikācijas metožu lietojums vilciena vagonu tipa noteikšanai no attēla
2015
Datorredzes un tēlu atpazīšanas jomu attīstība paver iespēju veikt dažādu ikdienā nozīmīgu procesu automatizāciju. Lai aizvietotu cilvēka veiktu vilcienu vagonu vizuālo pārbaudi un automātiski konstatētu dažāda veida novirzes no normas, ir nepieciešams veikt vagonu klasifikāciju pēc tipa. Darbā tiek aplūkotas dažas attēlu apstrādes tehnikas, ar kuru palīdzību tiek iegūtas vagona attēla krāsas, struktūras un izmēra iezīmes. Tālāk tiek apskatītas un izvērtētas vairākas klasifikācijas metodes un divas piemērotākās no tām, atbalsta vektoru metode jeb SVM un nozīmīgo vektoru metode jeb RVM, tiek aprakstītas sīkāk. Rezultātā tiek izstrādāts sistēmas prototips, kas var atšķirt dažāda tipa vagonus …
A Novel Time Series Kernel for Sequences Generated by LTI Systems
2017
The recent introduction of Hankelets to describe time series relies on the assumption that the time series has been generated by a vector autoregressive model (VAR) of order p. The success of Hankelet-based time series representations prevalently in nearest neighbor classifiers poses questions about if and how this representation can be used in kernel machines without the usual adoption of mid-level representations (such as codebook-based representations). It is also of interest to investigate how this representation relates to probabilistic approaches for time series modeling, and which characteristics of the VAR model a Hankelet can capture. This paper aims at filling these gaps by: deriv…
An Automatic System for the Analysis and Classification of Human Atrial Fibrillation Patterns from Intracardiac Electrograms
2008
This paper presents an automatic system for the analysis and classification of atrial fibrillation (AF) patterns from bipolar intracardiac signals. The system is made up of: 1) a feature- extraction module that defines and extracts a set of measures potentially useful for characterizing AF types on the basis of their degree of organization; 2) a feature-selection module (based on the Jeffries-Matusita distance and a branch and bound search algorithm) identifying the best subset of features for discriminating different AF types; and 3) a support vector machine technique-based classification module that automatically discriminates the AF types according to the Wells' criteria. The automatic s…
Improvement of multimodal images classification based on DSMT using visual saliency model fusion with SVM
2019
Multimodal images carry available information that can be complementary, redundant information, and overcomes the various problems attached to the unimodal classification task, by modeling and combining these information together. Although, this classification gives acceptable classification results, it still does not reach the level of the visual perception model that has a great ability to classify easily observed scene thanks to the powerful mechanism of the human brain.
 In order to improve the classification task in multimodal image area, we propose a methodology based on Dezert-Smarandache formalism (DSmT), allowing fusing the combined spectral and dense SURF features extracted …
Classification of spectra and search for biomarkers in prostate tumours from proton nuclear magnetic resonance spectroscopy
2010
Prostate cancer is the most common cancer in men over 50 years. Current detection methods either lack sensitivity or specificity or are unpleasant for the patient. Magnetic resonance spectroscopy allows the study of metabolism in vivo. The use of a high field machine (≥3T) has allowed us to dispense with the use of an endorectal coil, which is particularly uncomfortable for the patient. The objective of this thesis is to create an automatic method to detect cancer by processing data obtained through magnetic resonance spectroscopy MRS is a complex phenomenon, very sensitive to acquisition conditions. Firstly, we have studied how to improve and optimise signal acquisition. However, even with…
Non-invasive localization of atrial ectopic beats by using simulated body surface P-wave integral maps
2017
Non-invasive localization of continuous atrial ectopic beats remains a cornerstone for the treatment of atrial arrhythmias. The lack of accurate tools to guide electrophysiologists leads to an increase in the recurrence rate of ablation procedures. Existing approaches are based on the analysis of the P-waves main characteristics and the forward body surface potential maps (BSPMs) or on the inverse estimation of the electric activity of the heart from those BSPMs. These methods have not provided an efficient and systematic tool to localize ectopic triggers. In this work, we propose the use of machine learning techniques to spatially cluster and classify ectopic atrial foci into clearly diffe…