DATA CLASSIFICATION of SPECTRUM ANALYSIS USING NEURAL NETWORK © Мatviykiv О.М., Faitas О.І., 2013 This Article Provides the Comparison of Libraries Neural Networks
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Identification of Activities of Daily Living Through Data Fusion on Motion and Magnetic Sensors Embedded on Mobile Devices
Identification of Activities of Daily Living through Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices Ivan Miguel Pires1,2,3, Nuno M. Garcia1, Nuno Pombo1, Francisco Flórez-Revuelta4, Susanna Spinsante5 and Maria Canavarro Teixeira6,7 1Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal 2Altranportugal, Lisbon, Portugal 3ALLab - Assisted Living Computing and Telecommunications Laboratory, Computer Science Department, Universidade da Beira Interior, Covilhã, Portugal 4Department of Computer Technology, Universidad de Alicante, Spain 5Università Politecnica delle Marche, Ancona, Italy 6UTC de Recursos Naturais e Desenvolvimento Sustentável, Polytechnique Institute of Castelo Branco, Castelo Branco, Portugal 7CERNAS - Research Centre for Natural Resources, Environment and Society, Polytechnique Institute of Castelo Branco, Castelo Branco, Portugal [email protected], [email protected], [email protected], [email protected], [email protected], [email protected] Abstract Several types of sensors have been available in off-the-shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired from motion and magnetic sensors, i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of Activities of Daily Living (ADL). Based on pattern recognition techniques, the system developed in this study includes data acquisition, data processing, data fusion, and classification methods like Artificial Neural Networks (ANN). Multiple settings of the ANN were implemented and evaluated in which the best accuracy obtained, with Deep Neural Networks (DNN), was 89.51%. This novel approach applies L2 regularization and normalization techniques on the sensors’ data proved it suitability and reliability for the ADL recognition. Keywords: Mobile devices sensors; sensor data fusion; artificial neural networks; identification of activities of daily living. -
Artificial Neural Networks
ARTIFICIAL NEURAL NETWORKS: A REVIEW OF TRAINING TOOLS Darío Baptista, Fernando Morgado-Dias Madeira Interactive Technologies Institute and Centro de Competências de Ciências Exactas e da Engenharia, Universidade da Madeira Campus da Penteada, 9000-039 Funchal, Madeira, Portugal. Tel:+351 291-705150/1, Fax: +351 291-705199 Abstract: Artificial Neural Networks became a common solution for a wide variety of problems in many fields. The most frequent solution for its implementation consists of building and training the Artificial Neural Network within a computer. For implementing a network in an efficient way, the user can access a large choice of software solutions either commercial or prototypes. Choosing the most convenient solution for the application according to the network architecture, training algorithm, operating system and price can be a complex task. This paper helps the Artificial Neural Network user by providing a large list of solution available and explaining their characteristics and terms of use. The paper is confined to reporting the software products that have been developed for Artificial Neural Networks. The features considered important for this kind of software to have in order to accommodate its users effectively are specified. The development of software that implements Artificial Neural is a rapidly growing field driven by strong research interests as well as urgent practical, economical and social needs. Copyright CONTROLO2012 Keywords: Artificial Neural Networks, Training Tools, Training Algorithms, Software. 1. INTRODUCTION commercialization of new ANN tools. With the purpose to inform which tools are available at present Nowadays, in different areas, it is important to and make the choice of which tool to use, this paper analyse nonlinear data to do prediction, classification contains the description of software that has been or to build models. -
Programming Neural Networks with Encog3 in Java
Programming Neural Networks with Encog3 in Java Programming Neural Networks with Encog3 in Java Jeff Heaton Heaton Research, Inc. St. Louis, MO, USA v Publisher: Heaton Research, Inc Programming Neural Networks with Encog 3 in Java First Printing October, 2011 Author: Jeff Heaton Editor: WordsRU.com Cover Art: Carrie Spear ISBN’s for all Editions: 978-1-60439-021-6, Softcover 978-1-60439-022-3, PDF 978-1-60439-023-0, LIT 978-1-60439-024-7, Nook 978-1-60439-025-4, Kindle Copyright ©2011 by Heaton Research Inc., 1734 Clarkson Rd. #107, Chester- field, MO 63017-4976. World rights reserved. The author(s) created reusable code in this publication expressly for reuse by readers. Heaton Research, Inc. grants readers permission to reuse the code found in this publication or down- loaded from our website so long as (author(s)) are attributed in any application containing the reusable code and the source code itself is never redistributed, posted online by electronic transmission, sold or commercially exploited as a stand-alone product. Aside from this specific exception concerning reusable code, no part of this publication may be stored in a retrieval system, trans- mitted, or reproduced in any way, including, but not limited to photo copy, photograph, magnetic, or other record, without prior agreement and written permission of the publisher. Heaton Research, Encog, the Encog Logo and the Heaton Research logo are all trademarks of Heaton Research, Inc., in the United States and/or other countries. TRADEMARKS: Heaton Research has attempted throughout this book to distinguish proprietary trademarks from descriptive terms by following the capitalization style used by the manufacturer. -
A Thesis Entitled a Supervised Machine Learning Approach Using
A Thesis entitled A Supervised Machine Learning Approach Using Object-Oriented Programming Principles by Merl J. Creps Jr Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Masters of Science Degree in Computer Science and Engineering Dr. Jared Oluoch, Committee Chair Dr. Weiqing Sun, Committee Member Dr. Henry Ledgard, Committee Member Dr. Amanda C. Bryant-Friedrich, Dean College of Graduate Studies The University of Toledo May 2018 Copyright 2018, Merl J. Creps Jr This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of A Supervised Machine Learning Approach Using Object-Oriented Programming Principles by Merl J. Creps Jr Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Masters of Science Degree in Computer Science and Engineering The University of Toledo May 2018 Artificial Neural Networks (ANNs) can be defined as a collection of interconnected layers, neurons and weighted connections. Each layer is connected to the previous layer by weighted connections that couple each neuron in a layer to the next neuron in the adjacent layer. An ANN resembles a human brain with multiple interconnected synapses which allow for the signal to easily flow from one neuron to the next. There are two distinct constructs which an ANN can assume; they can be thought of as Supervised and Unsupervised networks. Supervised neural networks have known outcomes while Unsupervised neural networks have unknown outcomes. This thesis will primarily focus on Supervised neural networks. The contributions of this thesis are two-fold. -
Optimization of Neural Network Architecture for Biomechanic Classification Tasks with Electromyogram Inputs
International Journal of Artificial Intelligence and Applications (IJAIA), Vol. 7, No. 5, September 2016 OPTIMIZATION OF NEURAL NETWORK ARCHITECTURE FOR BIOMECHANIC CLASSIFICATION TASKS WITH ELECTROMYOGRAM INPUTS Alayna Kennedy 1 and Rory Lewis 2 1 The Pennsylvania State University, University Park, PA, USA 2 Department of Computer Science, University of Colorado at Colorado Springs, USA ABSTRACT Electromyogram signals (EMGs) contain valuable information that can be used in man-machine interfacing between human users and myoelectric prosthetic devices. However, EMG signals are complicated and prove difficult to analyze due to physiological noise and other issues. Computational intelligence and machine learning techniques, such as artificial neural networks (ANNs), serve as powerful tools for analyzing EMG signals and creating optimal myoelectric control schemes for prostheses. This research examines the performance of four different neural network architectures (feedforward, recurrent, counter propagation, and self organizing map) that were tasked with classifying walking speed when given EMG inputs from 14 different leg muscles. Experiments conducted on the data set suggest that self organizing map neural networks are capable of classifying walking speed with greater than 99% accuracy. KEYWORDS Electromyogram, Prostheses, Neural Networks, Biomechanical Analysis, Machine Learning, Myoelectric Control Schemes, Self Organizing Maps, Pattern Recognition Algorithms 1. INTRODUCTION Assistive prosthetic devices commonly use a myoelectric control scheme, which adjusts the function of a prosthetic device given electromyogram (EMG) inputs, and which has been proven an effective method of man-machine interfacing between user and prosthetic [1]-[4]. However, despite the significant development of the prosthetic industry over the past decade, high-accuracy commercial prostheses remain too expensive for the average middle-class amputee to afford [5], [6]. -
Reverse Image Search for Scientific Data Within and Beyond the Visible
Journal, Vol. XXI, No. 1, 1-5, 2013 Additional note Reverse Image Search for Scientific Data within and beyond the Visible Spectrum Flavio H. D. Araujo1,2,3,4,a, Romuere R. V. Silva1,2,3,4,a, Fatima N. S. Medeiros3,Dilworth D. Parkinson2, Alexander Hexemer2, Claudia M. Carneiro5, Daniela M. Ushizima1,2,* Abstract The explosion in the rate, quality and diversity of image acquisition instruments has propelled the development of expert systems to organize and query image collections more efficiently. Recommendation systems that handle scientific images are rare, particularly if records lack metadata. This paper introduces new strategies to enable fast searches and image ranking from large pictorial datasets with or without labels. The main contribution is the development of pyCBIR, a deep neural network software to search scientific images by content. This tool exploits convolutional layers with locality sensitivity hashing for querying images across domains through a user-friendly interface. Our results report image searches over databases ranging from thousands to millions of samples. We test pyCBIR search capabilities using three convNets against four scientific datasets, including samples from cell microscopy, microtomography, atomic diffraction patterns, and materials photographs to demonstrate 95% accurate recommendations in most cases. Furthermore, all scientific data collections are released. Keywords Reverse image search — Content-based image retrieval — Scientific image recommendation — Convolutional neural network 1University of California, Berkeley, CA, USA 2Lawrence Berkeley National Laboratory, Berkeley, CA, USA 3Federal University of Ceara,´ Fortaleza, CE, Brazil 4Federal University of Piau´ı, Picos, PI, Brazil 5Federal University of Ouro Preto, Ouro Preto, MG, Brazil *Corresponding author: [email protected] aContributed equally to this work. -
Detecting Simulated Attacks in Computer Networks Using Resilient Propagation Artificial Neural Networks
ISSN 2395-8618 Detecting Simulated Attacks in Computer Networks Using Resilient Propagation Artificial Neural Networks Mario A. Garcia and Tung Trinh Abstract—In a large network, it is extremely difficult for an (Unsupervised Neural Network can detect attacks without administrator or security personnel to detect which computers training.) There are several training algorithms to train neural are being attacked and from where intrusions come. Intrusion networks such as back propagation, the Manhattan update detection systems using neural networks have been deemed a rule, Quick propagation, or Resilient propagation. promising solution to detect such attacks. The reason is that This research describes a solution of applying resilient neural networks have some advantages such as learning from training and being able to categorize data. Many studies have propagation artificial neural networks to detect simulated been done on applying neural networks in intrusion detection attacks in computer networks. The resilient propagation is a systems. This work presents a study of applying resilient supervised training algorithm. The term “supervised” propagation neural networks to detect simulated attacks. The indicates that the neural networks are trained with expected approach includes two main components: the Data Pre- output. The resilient propagation algorithm is considered an processing module and the Neural Network. The Data Pre- efficient training algorithm because it does not require any processing module performs normalizing data function while the parameter setting before being used [14]. In other words, Neural Network processes and categorizes each connection to learning rates or update constants do not need to be computed. find out attacks. The results produced by this approach are The approach is tested on eight neural network configurations compared with present approaches. -
Comparative Analysis of Simulators for Neural Networks Joone and Neuroph
COMPUTER MODELLING & NEW TECHNOLOGIES 2016 20(1) 16-20 Zdravkova E, Nenkov N Comparative analysis of simulators for neural networks Joone and NeuroPh E Zdravkova, N Nenkov* Faculty of Mathematics and Informatics, University of Shumen “Episkop Konstantin Preslavsky” 115, Universitetska St., Shumen 9712, Bulgaria *Corresponding author’s e-mail: [email protected], [email protected] Received 1 March 2016, www.cmnt.lv Abstract Keywords: This paper describes a comparative analysis of two simulator neural networks - Joone and NeuroPh. Both neural network simulators are object-oriented and java - based. The analysis seeks to show how much these two simulators neural network simulator are similar and how different in their characteristics, what neural networks is suitable to be made through logical function exclusive OR them, what are their advantages and disadvantages, how they can be used interchangeably to give certain neural network architecture desired result. For the purpose of comparative analysis of both the simulator will be realized logic function, which is not among the standard, and relatively complex and is selected as a combination of several standard logical operations. 1 Introduction 2 Methodology Both the simulator selected for the study are Java - based To be tested and analyzed both the simulator will realize and object - oriented simulators. The used simulators are logical function, and which is relatively complex and is not Joone 4.5.2 and NeuroPh 2.92. Joone is object - oriented among the standard. The generated neural network calculate frameworks allows to build different types of neural the result of the following logical function: networks. It is built on combining elements which can also (((( A XOR B) AND C) OR D) ((((E XOR F) AND G) OR be expanded to build new training algorithms and archi- H)) I)) AND ((( J XOR K)(L XOR M)) OR (N O)) tecttures for neural networks. -
Neural Network FAQ, Part 1 of 7
Neural Network FAQ, part 1 of 7: Introduction Archive-name: ai-faq/neural-nets/part1 Last-modified: 2002-05-17 URL: ftp://ftp.sas.com/pub/neural/FAQ.html Maintainer: [email protected] (Warren S. Sarle) Copyright 1997, 1998, 1999, 2000, 2001, 2002 by Warren S. Sarle, Cary, NC, USA. --------------------------------------------------------------- Additions, corrections, or improvements are always welcome. Anybody who is willing to contribute any information, please email me; if it is relevant, I will incorporate it. The monthly posting departs around the 28th of every month. --------------------------------------------------------------- This is the first of seven parts of a monthly posting to the Usenet newsgroup comp.ai.neural-nets (as well as comp.answers and news.answers, where it should be findable at any time). Its purpose is to provide basic information for individuals who are new to the field of neural networks or who are just beginning to read this group. It will help to avoid lengthy discussion of questions that often arise for beginners. SO, PLEASE, SEARCH THIS POSTING FIRST IF YOU HAVE A QUESTION and DON'T POST ANSWERS TO FAQs: POINT THE ASKER TO THIS POSTING The latest version of the FAQ is available as a hypertext document, readable by any WWW (World Wide Web) browser such as Netscape, under the URL: ftp://ftp.sas.com/pub/neural/FAQ.html. If you are reading the version of the FAQ posted in comp.ai.neural-nets, be sure to view it with a monospace font such as Courier. If you view it with a proportional font, tables and formulas will be mangled. -
From Digital Computing to Brain-Like Neuromorphic Accelerator
Mahyar SHAHSAVARI Lille University of Science and Technology Doctoral Thesis Unconventional Computing Using Memristive Nanodevices: From Digital Computing to Brain-like Neuromorphic Accelerator Supervisor: Pierre BOULET Thèse en vue de l’obtention du titre de Docteur en INFORMATIQUE DE L’UNIVERSITÉ DE LILLE École Doctorale des Sciences Pour l’Ingénieur de Lille - Nord De France 14-Dec-2016 Jury: Hélène Paugam-Moisy Professor of University of the French West Indies (Université des Antilles), France (Rapporteur) Michel Paindavoine Professor of University of Burgundy (Université de Bourgogne), France (Rapporteur) Virginie Hoel Professor of Lille University of Science and Technology, France(Président) Said Hamdioui Professor of Delft University of Technology, The Netherlands Pierre Boulet Professor of Lille University of Science and Technology, France Acknowledgment Firstly, I would like to express my sincere gratitude to my advisor Prof. Pierre Boulet for all kind of supports during my PhD. I never forget his supports specially during the last step of my PhD that I was very busy due to teaching duties. Pierre was really punctual and during three years of different meetings and discussions, he was always available on time and as a record he never canceled any meeting. I learned a lot form you Pierre, specially the way of writing, communicating and presenting. you were an ideal supervisor for me. Secondly, I am grateful of my co-supervisor Dr Philippe Devienne. As I did not knew any French at the beginning of my study, it was not that easy to settle down and with Philippe supports the life was more comfortable and enjoyable for me and my family here in Lille. -
Data Fusion on Motion and Magnetic Sensors Embedded on Mobile Devices for the Identification of Activities of Daily Living
Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living Ivan Miguel Pires1,2,3, Nuno M. Garcia1,3,4, Nuno Pombo1,3,4 , Francisco Flórez-Revuelta5 and Susanna Spinsante6 1Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal 2Altranportugal, Lisbon, Portugal 3ALLab - Assisted Living Computing and Telecommunications Laboratory, Computer Science Department, Universidade da Beira Interior, Covilhã, Portugal 4Universidade Lusófona de Humanidades e Tecnologias, Lisbon, Portugal 5Department of Computer Technology, Universidad de Alicante, Spain 6Università Politecnica delle Marche, Ancona, Italy i mpires @it.ubi.pt, ngarcia @di. ubi.pt, ngpombo@di. ubi.pt, francisco.florez @ua.es, s.spinsante @univpm.it Abstract Several types of sensors have been available in off-the-shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired from motion and magnetic sensors, i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of Activities of Daily Living (ADL) using pattern recognition techniques. The system developed in this study includes data acquisition, data processing, data fusion, and artificial intelligence methods. Artificial Neural Networks (ANN) are included in artificial intelligence methods, which are used in this study for the recognition of ADL. The purpose of this study is the creation of a new method using ANN for the identification of ADL, comparing three types of ANN, in order to achieve results with a reliable accuracy. The best accuracy was obtained with Deep Learning, which, after the application of the L2 regularization and normalization techniques on the sensors’ data, reports an accuracy of 89.51%. -
Unconventional Computing Using Memristive Nanodevices: from Digital Computing to Brain-Like Neuromorphic Accelerator
Mahyar SHAHSAVARI Lille University of Science and Technology Doctoral Thesis Unconventional Computing Using Memristive Nanodevices: From Digital Computing to Brain-like Neuromorphic Accelerator Supervisor: Pierre BOULET Thèse en vue de l’obtention du titre de Docteur en INFORMATIQUE DE L’UNIVERSITÉ DE LILLE École Doctorale des Sciences Pour l’Ingénieur de Lille - Nord De France 14-Dec-2016 Jury: Hélène Paugam-Moisy Professor of University of the French West Indies (Université des Antilles), France (Rapporteur) Michel Paindavoine Professor of University of Burgundy (Université de Bourgogne), France (Rapporteur) Virginie Hoel Professor of Lille University of Science and Technology, France(Président) Said Hamdioui Professor of Delft University of Technology, The Netherlands Pierre Boulet Professor of Lille University of Science and Technology, France Acknowledgment Firstly, I would like to express my sincere gratitude to my advisor Prof. Pierre Boulet for all kind of supports during my PhD. I never forget his supports specially during the last step of my PhD that I was very busy due to teaching duties. Pierre was really punctual and during three years of different meetings and discussions, he was always available on time and as a record he never canceled any meeting. I learned a lot form you Pierre, specially the way of writing, communicating and presenting. you were an ideal supervisor for me. Secondly, I am grateful of my co-supervisor Dr Philippe Devienne. As I did not knew any French at the beginning of my study, it was not that easy to settle down and with Philippe supports the life was more comfortable and enjoyable for me and my family here in Lille.