...Joseph is a 59-year-old construction worker who recently suffered a stroke. A small blood clot became lodged in one of the vessels serving the right side of his brain and restricted blood flow to a portion of his right precentral gyrus. Many neurons in that area were damaged or destroyed and, as a result, Joseph is partially paralyzed on the left side of his body. He is able to move his left leg and walk, and can also move his left arm, but his left hand and the left side of his face are paralyzed. •Answer the following questions about the case study: ◦Why were Joseph's left face and hand paralyzed, but not the rest of his arm or his leg? ◦What is preventing Joseph from moving his left hand? In your answer, include the following: ◾First, describe normal function of a motor neuron that forms a synapse with a muscle cell. How is the signal that initiates movement transmitted from the neuron to the muscle? Describe this process in detail. ◾Next, keeping in mind that the motor neuron itself is not damaged, explain how damage or destruction of a neuron that communicates with the motor neuron prevents Joseph from moving his hand. ◦With time, and perhaps some physical therapy, Joseph may recover some of the movement in his left hand and face. What factors might contribute to this recovery? In your answer, include the following: ◾Why can't Joseph's brain simply replace those neurons that were damaged and destroyed by the stroke? ◾How might compensation allow Joseph to regain...
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...www.elsevier.com/locate/atoures Annals of Tourism Research, Vol. 32, No. 1, pp. 93–111, 2005 Ó 2005 Elsevier Ltd. All rights reserved. Printed in Great Britain 0160-7383/$30.00 doi:10.1016/j.annals.2004.05.001 MARKET SEGMENTATION A Neural Network Application Jonathan Z. Bloom University of Stellenbosch, South Africa Abstract: The objective of the research is to consider a self-organizing neural network for segmenting the international tourist market to Cape Town, South Africa. A backpropagation neural network is used to complement the segmentation by generating additional knowledge based on input–output relationship and sensitivity analyses. The findings of the self-organizing neural network indicate three clusters, which are visually confirmed by developing a comparative model based on the test data set. The research also demonstrated that Cape Metropolitan Tourism could deploy the neural network models and track the changing behavior of tourists within and between segments. Marketing implications for the Cape are also highlighted. Keywords: segmentation, SOM neural network, input–output analysis, sensitivity analysis, deployment. Ó 2005 Elsevier Ltd. All rights reserved. ´ ´ Resume: Segmentation du marche: une application du reseau neuronal. Le but de la ´ ´ recherche est de considerer un reseau neuronal auto-organisateur pour segmenter le marche ´ ´ ´ touristique international a Cape Town, en Afrique du Sud. On utilise un reseau neuronal de ` ´ retropropogation pour...
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...boisestate.edu/update/files/2013/08/Memritor620x320.jpg) Today’s computing chips are incredibly complex and contain billions of nano-scale transistors, allowing for fast, high-performance computers, pocket-sized smartphones that far outpace early desktop computers, and an explosion in handheld tablets. Despite their ability to perform thousands of tasks in the blink of an eye, none of these devices even come close to rivaling the computing capabilities of the human brain. At least not yet. But a Boise State University research team could soon change that. Electrical and computer engineering faculty Elisa Barney Smith, Kris Campbell and Vishal Saxena are joining forces on a project titled “CIF: Small: Realizing Chip-scale Bio-inspired Spiking Neural Networks with Monolithically Integrated Nano-scale Memristors.” (http://news.boisestate.edu/update/files 1 of 3 3/15/2014 12:37 PM Researchers Building a Computer Chip Based on the Human Brain - U... http://news.boisestate.edu/update/2013/08/14/research-team-building-a-... /2013/08/PCB_image.png) Team members are experts in machine learning (artificial intelligence), integrated circuit design and memristor devices. Funded by a three-year, $500,000 National Science Foundation grant, they have taken on the challenge of developing a new kind of computing architecture that works more like a brain than a traditional digital computer. “By mimicking the brain’s billions of interconnections and pattern recognition capabilities, we may ultimately...
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...2 CHAPTER 2.1 2.2 2.3 Decision Making and Business Processes Why Do I Need To Know This LEARNING OUTCOMES Explain the difference between transactional data and analytical information, and between OLTP and OLAP. Define TPS, DSS, and EIS, and explain how organizations use these types of information systems to make decisions. Understand what AI is and the four types of artificial intelligence systems used by organizations today. Describe how AI differs from TPS, DSS, and EIS. Describe the importance of business process improvement, business process reengineering, business process modelling, and business process management to an organization and how information systems can help in these areas. This chapter describes various types of business information systems found across the enterprise used to run basic business processes and used to facilitate sound and proper decision making. Using information systems to improve decision making and re-engineer business processes can significantly help organizations become more efficient and effective. ? 2.4 2.5 As a business student, you can gain valuable insight into an organization by understanding the types of information systems that exist in and across enterprises. When you understand how to use these systems to improve business processes and decision making, you can vastly improve organizational performance. After reading this chapter, you should have gained an appreciation of the various kinds of information systems employed...
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...CSE- 401 DISTRIBUTED SYSTEMS [3 1 0 4] 1. Distributed System Models: Introduction , Examples , Architecture models , Fundamental models (1.1,1.2,1.4, 2.1-2.3 of Text1 ) ..2hrs 2. Interprocess Communication, Distributed Objects and Remote Invocation: Introduction , External data representation and marshalling, Communication models, Communication between distributed objects , Remote procedure call Case study: Interprocess communication in UNIX, Java RMI . (4.1-4.6, 5.1-5.5 of Text1) ..6hrs 3. Operating System Introduction , Operating system layer, Processes and threads, Communication and invocation, Architecture (6.1-6.6 of Text1) ..4hrs. 4. Distributed File Systems and Name Services: Introduction , File service architecture, Name services, Domain Name System, Directory and directory services. Case study: Sun network file system, Global name service. (8.1-8.3, 9.1-9.4 of Text1) …6hrs 5. Synchronization: Clock Synchronization, Physical clocks, Logical clocks, Global state (5.1-5.3 of Text2) ..5hrs 6. Transactions&...
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...NEURAL NETWORKS by Christos Stergiou and Dimitrios Siganos | Abstract This report is an introduction to Artificial Neural Networks. The various types of neural networks are explained and demonstrated, applications of neural networks like ANNs in medicine are described, and a detailed historical background is provided. The connection between the artificial and the real thing is also investigated and explained. Finally, the mathematical models involved are presented and demonstrated. Contents: 1. Introduction to Neural Networks 1.1 What is a neural network? 1.2 Historical background 1.3 Why use neural networks? 1.4 Neural networks versus conventional computers - a comparison 2. Human and Artificial Neurones - investigating the similarities 2.1 How the Human Brain Learns? 2.2 From Human Neurones to Artificial Neurones 3. An Engineering approach 3.1 A simple neuron - description of a simple neuron 3.2 Firing rules - How neurones make decisions 3.3 Pattern recognition - an example 3.4 A more complicated neuron 4. Architecture of neural networks 4.1 Feed-forward (associative) networks 4.2 Feedback (autoassociative) networks 4.3 Network layers 4.4 Perceptrons 5. The Learning Process 5.1 Transfer Function 5.2 An Example to illustrate the above teaching procedure 5.3 The Back-Propagation Algorithm 6. Applications of neural networks 6.1 Neural networks in practice 6.2 Neural networks in medicine 6.2.1 Modelling and Diagnosing the Cardiovascular...
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...Final Project Resume DESIGN AND IMPLEMENTATION OF INDONESIAN SIGN LANGUAGE RECOGNITION SYSTEM BASED ON FLEX SENSOR WITH ARTIFICIAL NEURAL NETWORK Azizah Izzatur Rahim Program Studi D4Teknik Elektronika Departemen Teknik Elektro Politeknik Elektronika Negeri Surabaya Kampus PENS-ITS, Jalan Raya ITS Sukolilo, Surabaya 60111 Tel: (031) 594 7280; Fax: (031) 594 6114 Email : azizahirahim@gmail.com Abstract As a social creature, humans are very need of communication as a media to make some interaction with others. There are verbal and non verbal communication. Yet, not all of humans are able to communicate well because they don’t understand each other meaning. This kind of problem are often happened in normal people who hard to understand mute people meanings who uses sign language. Meanwhile, translator devices are very expensive and not everyone can buy or even hired a translator also needed a high cost. So, from this union of flex sensor and accelerometer with artificial neural network backpropagation method resulted a sign language translator device which text is as the output on computer that more economic with succeed percentage by 99.2% and failure percentage less by 1%. Moreover, this device can be use as learning and introduction media to normal people to knowing sign language system. Keyword : Sensor Flex, Accelerometer, Backpropagation, Isyarat 1. Background So many ways that human done to communicated each others like conversations, sign language or sentences...
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...BIO INSPIRED NEURAL NETWORKING AMONG MULTI-ROBOTS CHAPTER 1 INTRODUCTION Transportation is one of the most important economic activities of any country. Among the various forms of transport, road transport is one of the most popular means of transportation. Transportation has an element of danger attached to it in the form of vehicle crashes. Road crashes not only cause death and injury, but they also bring along an immeasurable amount of agony to the people involved. Efforts to improve traffic safety to date have concentrated on the occupant protection, which had improved the vehicle crash worthiness. The other important area where research is currently being done is collision avoidance. Technological innovations have given the traffic engineer an option of improving traffic safety by utilizing the available communication tools and sophisticated instruments. Using sensors and digital maps for increasing traffic safety is in its infancy. Systems are being developed to utilize the available state of the art facilities to reduce or possibly prevent the occurrence of crashes. Total prevention of crashes might not be possible for now, but the reduction of crashes could easily be achieved by using the collision avoidance systems. 1.1 NEED FOR COLLISION AVOIDANCE The development of collision avoidance systems is motivated by their potential for increased vehicle safety. Half of the more than 1.5 million rear-end crashes that occurred in 1994 could have been prevented by...
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...Knowledge Management System 4 2.3 Customer Relationship Management 4 2.4 Supply Chain Management System 5 3.0 Cloud Computing 5 3.1 Characteristics of Cloud Computing 6 3.1.1 Elasticity & Scalability 6 3.1.2 Provisioning 6 3.1.3 Standardisation 6 3.1.4 Billing and Service Usage 7 3.2 Issues with Cloud Computing before Implementation 7 4.0 Technology Review 7 5.0 Operating Systems in Personal Computers 8 5.1 Features of Microsoft Windows 8 9 6.0 Enterprise Systems 10 6.1 Benefits of implementing Enterprise Systems 10 6.2 Challenges caused by implementing Enterprise Systems 11 7.0 Intelligent Systems 11 7.1 Types of Intelligent Systems 12 7.1.1 Expert Systems 12 7.1.2 Artificial Neural Networks 12 7.1.3 Motion Controls 13 7.1.4 Genetic Algorithms 13 8.0 Web Services 13 9.0 Educational Institutions 14 10.0 Technological Safeguards 15 10.1 Encryption 15 Reference List 16 1.0 Technical, Business and System Competencies Information system is best defined as the an amalgamation of hardware, software, technical infrastructure and skilled employees which are prearranged to smooth the progress of planning, controlling, coordinating and decision making within an organisation....
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...ARTIFICIAL NEURAL NETWORKS METHODOLOGICAL ADVANCES AND BIOMEDICAL APPLICATIONS Edited by Kenji Suzuki Artificial Neural Networks - Methodological Advances and Biomedical Applications Edited by Kenji Suzuki Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Ivana Lorkovic Technical Editor Teodora Smiljanic Cover Designer Martina Sirotic Image Copyright Bruce Rolff, 2010. Used under license from Shutterstock.com First published March, 2011 Printed in...
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...Temperature and Illuminosity Management to monitor greenhouse conditions using SunSPOTs Suthikshn Kumar Dept of Information Science, PESIT Bangalore suthikshn.kumar@pes.edu Ishaan Raghunandan Dept of Information Science, PESIT Bangalore ishaan.raghunandan@gmail.com Abstract: In modern greenhouses, several measurement points are required to trace down the local climate parameters in different parts of the enclosure efficiently.A Wireless Sensor Network (WSN) consisting of small-size wireless sensor nodes an attractive and cost-efficient option to build the required measurement system. The most important climatic conditions are the temperature and the light intensity within the greenhouse.This paper describes the implementation and configuration of the wireless sensor network using the Sun SPOT platform.It also uses a Time Series Algorithm to predict the temperature variation. Keywords: WSN,SunSPOT,TimeSeries,Java,Zigbee,Back Propagation 1.Introduction The most important factors for the quality and productivity of plant growth are temperature and light. Continuous monitoring of these environmental variables gives information to the agriculturalist to better understand, how each factor affects growth and how to manage maximal crop productiveness. The optimal greenhouse climate adjustment can enable us to improve productivity and to achieve remarkable energy savings - especially during the winter in northern countries. Increased number of measurement points should not dramatically...
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...Business – MGT 501 Neural Network Technique Outline * Overview ………………………………………………………….……… 4 * Definition …………………………………………………4 * The Basics of Neural Networks……………………………………………5 * Major Components of an Artificial Neuron………………………………..5 * Applications of Neural Networks ……………….9 * Advantages and Disadvantages of Neural Networks……………………...12 * Example……………………………………………………………………14 * Conclusion …………………………………………………………………14 Overview One of the most crucial and dominant subjects in management studies is finding more effective tools for complicated managerial problems, and due to the advancement of computer and communication technology, tools used in management decisions have undergone a massive change. Artificial Neural Networks (ANNs) is an example, knowing that it has become a critical component of business intelligence. The below article describes the basics of neural networks as well as some work done on the application of ANNs in management sciences. Definition of a Neural Network? The simplest definition of a neural network, particularly referred to as an 'artificial' neural network (ANN), is provided by the inventor of one of the first neurocomputers, Dr. Robert Hecht-Nielsen who defines a neural network as follows: "...a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs."Neural Network Primer:...
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...http://ml.memect.com Contents 1 Artificial neural network 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2.1 Improvements since 2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3.1 Network function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3.2 Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.3 Learning paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.4 Learning algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Employing artificial neural networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.5 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5.1 Real-life applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5.2 Neural networks and neuroscience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.6 Neural network software . . . . . . . . . . . . . . ...
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...TURBMW06_013234761X.QXD 3/7/07 8:07 PM ONLINE CHAPTER Page 1 Neural Networks 6 for Data Mining Learning Objectives ◆ Understand the concept and different types of artificial neural networks (ANN) ◆ Learn the advantages and limitations of ANN ◆ Understand how backpropagation neural networks learn ◆ Understand the complete process of using neural networks ◆ Appreciate the wide variety of applications of neural networks N eural networks have emerged as advanced data mining tools in cases where other techniques may not produce satisfactory predictive models. As the term implies, neural networks have a biologically inspired modeling capability, but are essentially statistical modeling tools. In this chapter, we study the basics of neural network modeling, some specific applications, and the process of implementing a neural network project. 6.1 Opening Vignette: Using Neural Networks to Predict Beer Flavors with Chemical Analysis 6.2 Basic Concepts of Neural Networks 6.3 Learning in Artificial Neural Networks (ANN) 6.4 Developing Neural Network–Based Systems 6.5 A Sample Neural Network Project 6.6 Other Neural Network Paradigms 6.7 Applications of Artificial Neural Networks 6.8 A Neural Network Software Demonstration 6.1 OPENING VIGNETTE: USING NEURAL NETWORKS TO PREDICT BEER FLAVORS WITH CHEMICAL ANALYSIS Coors Brewers Ltd., based in Burton-upon-Trent, Britain’s brewing capital, is proud of having the United Kingdom’s top beer brands...
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...Exam #3 Pediatric Review Questions 1. Spastic cerebral palsy is characterized by what? 2. Are there any drugs that can decrease spasticity in a child? How would you respond to a parent asking this question? 3. What is a myelomeningocele? 4. Which problem is often associated with a myelomeningocele? 5. What is the most common problem of a child born with a myelomeningocele? 6. What is a recommendation to prevent neural tube defect? 7. How much folic acid is recommended for women of childbearing age? 8. What position do you place a neonate in to feed that has had a myelomeningocele repair? 9. What advice about the diet would you give a parent who has a child with a latex allergy? 10. What are appropriate nursing interventions for a child with latex allergies? 11. What are the clinical manifestations of a child with spinal muscular atrophy (Werdnig-Hoffman disease)? 12. What is the management plan for a child diagnosed with pseudohypertrophic (Duchenne) muscular dystrophy? 13. Therapeutic management of a child with tetanus includes the administration of what medication(s)? 14. Select all that apply: care of a child after a spinal cord injury would include what nursing interventions? 15. How does immobilization affect the metabolism? Increase or decrease the metabolism? 16. How does immobilization affect the cardiovascular system? It causes…. 17. What can result from the bone demineralization associated with immobility? 18. What would you do for a child who has...
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