machine learning

Big Data

Time series features extraction using Fourier and Wavelet transforms on ECG data

ABSTRACT This article focuses on the features extraction from time series and signals using Fourier and Wavelet transforms. This task will be carried out on an electrocardiogram (ECG) dataset in order to classify three groups of people: those with cardiac arrhythmia (ARR), congestive heart failure (CHF) and normal sinus rhythm (NSR). Our approach consists of using scaleogram (i.e. 2D representation of 1D extracted features) as an input to train a Neural Network (NN). We conducted the different tasks using python as a programming language. The…

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Archi & Techno

Industrial document classification with Deep Learning

Knowledge is a goldmine for companies. It comes in different shapes and forms: mainly documents (presentation slides and documentation) that allow businesses to share information with their customers and staff. The way companies harness this knowledge is central to their ability to develop their business successfully. One of the common ways to ease the access to this document base is to use search engines based on textual data. At OCTO, we have decided to use optical character recognition (OCR) solutions to extract this data, since…

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Big Data

A quick summary and some thoughts on the Scikit-learn workshop

On december 2nd was given at Telecom ParisTech the workshop : “Using Scikit-learn and Scientific Python at Scale” with top contributors from the project as speakers. This workshop was divided into four talks :    Scikit-learn for industrial applications, basic research and mind reading - Alexandre Gramfort    Distributed computing for predictive modeling in Python - Olivier Grisel    Scikit-learn at scale : out-of-core methods - Thierry Guillemot    An Industrial application at Airbus Group - Vincent Feuillard Scikit-learn is currently the most widely used open source library…

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Archi & Techno

The basics of face recognition

Face Recognition is definitely one of the most popular computer vision problems. Thanks to its popularity it has been well studied over the last 50 years. The first intents to explore face recognition were made in the 60's however it was until the 90's when Turk and Pentland implemented the "Eigenfaces" algorithm, that this field showed some really exciting and useful results. Bright future Face recognition is recently getting more and more attention and we can anticipate bright future of this field. Security was historically…

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