...Two different neurological disorders that can occur in development and how they affect development. The National Institute of Neurological Disorders states that spina bifida, which means cleft spine, is characterized by the incomplete development of the brain, spinal cord, and/or meninges (the protective covering around the brain and spinal cord. There are four types of spinal bifida: occulta, closed neural defects, meningocele, and myelomeningocele. Occulta is the mildest and most common form in which one or more vertebrae are malformed. Closed neural tube defects make up the second type of spina bifida. This form consists of a diverse group of defects in which the spinal cord is marked by malformations of fat, bone, or meninges. The third type is, memingocele, spinal fluid and meninges protrude through an abnormal vertebral opening. And the most severe and occurs when the spinal cord/neural elements are exposed through the opening in the spine is called myelomeningocele. The effect it has on development is that it can range from minor physical problems with little functional impairment to severe physical and mental disabilities. Spina bifida’s impact is determined by the size and location of the malformation, whether it covered, and which spinal nerves are involved. Also, most people with spina bifida are of normal intelligence. The national Institute of Neurological Disorders states that cerebral palsy refers to any one of a number of neurological disorders that appear...
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...SEGMENTATION WITH NEURAL NETWORK B.Prasanna Rahul Radhakrishnan Valliammai Engineering College Valliammai Engineering College prakrish_2001@yahoo.com krish_rahul_1812@yahoo.com Abstract: Our paper work is on Segmentation by Neural networks. Neural networks computation offers a wide range of different algorithms for both unsupervised clustering (UC) and supervised classification (SC). In this paper we approached an algorithmic method that aims to combine UC and SC, where the information obtained during UC is not discarded, but is used as an initial step toward subsequent SC. Thus, the power of both image analysis strategies can be combined in an integrative computational procedure. This is achieved by applying “Hyper-BF network”. Here we worked a different procedures for the training, preprocessing and vector quantization in the application to medical image segmentation and also present the segmentation results for multispectral 3D MRI data sets of the human brain with respect to the tissue classes “ Gray matter”, “ White matter” and “ Cerebrospinal fluid”. We correlate manual and semi automatic methods with the results. Keywords: Image analysis, Hebbian learning rule, Euclidean metric, multi spectral image segmentation, contour tracing. Introduction: Segmentation can be defined as the identification of meaningful image components. It is a fundamental task in image processing providing the basis for any kind of...
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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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...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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...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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...Delen, 2011, p. 282, para. 2) One important issue with beer is flavor. Typically, the flavor is determined by test panels. These tests are usually time-consuming. Coors wants to understand the chemical composition of flavors and if they knew that, it would open doors that have not been opened yet. “The relationship between chemical analysis and beer flavor is not clearly understood yet” (Turban, Sharda, & Delen, 2011, p. 282, para. 3). There is data on sensory analysis and chemical composition and Coors needs a way to link them together. The answer was Neural networks. Neural Networks The simplist defination of Neural Networks, more referred to as an ‘Artificial neural network’ (ANN) is defined by Dr. Robert Hecht-Nielsen, as a “computer system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs” (A Basic Introduction To Neural Networks, n.d.). With different interconnected layers such as many input layers, one output layers; one operation called the training operation, one could add many inputs (variables) into the system to reach the desired outcome. However at first it must be said that in order to reach the desired outcome, many inputs have to be entered over and over again and changing...
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...Anencephaly is a serious birth defect in which a baby is born without parts of the brain and skull. It is a type of neural tube defect; these are birth defects that happen during the first month of pregnancy, and it’s usually before a woman knows she is pregnant. Keywords: N/A Birth Defects: Anencephaly Birth defects are a structural or/and a functional of abnormalities that are present at birth that cause physical or mental disability. They’re the leading cause of death for infants and a fetus during the first year of life, and they can be fatal. Anencephaly is an example of a neural tube defect, a condition that results from an error in the first weeks of embryonic development. The term embryonic development refers to the changes that take...
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...MIDTERM: CS 6375 INSTRUCTOR: VIBHAV GOGATE October, 23 2013 The exam is closed book. You are allowed a one-page cheat sheet. Answer the questions in the spaces provided on the question sheets. If you run out of room for an answer, use an additional sheet (available from the instructor) and staple it to your exam. • NAME • UTD-ID if known • SECTION 1: • SECTION 2: • SECTION 3: • SECTION 4: • SECTION 5: • Out of 90: 1 CS 6375 FALL 2013 Midterm, Page 2 of 13 October 23, 2013 CS 6375 FALL 2013 Midterm, Page 3 of 13 October 23, 2013 SECTION 1: SHORT QUESTIONS (15 points) 1. (3 points) The Naive Bayes classifier uses the maximum a posteriori or the MAP decision rule for classification. True or False. Explain. Solution: True. The decision rule for the Naive Bayes classifier is: P (Xi |Y = y) arg; max P (Y = y) y i One can think of P (Y = y) as the prior distribution and P (Xi |Y = y) as the data likelihood. Note that when we do the learning, we are using the MLE approach. The decision rule is using MAP inference but the learning algorithm is using the MLE approach. Make sure you understand what this distinction means. 2. (6 points) Let θ be the probability that “Thumbtack 1” (we will abbreviate it as T1) shows heads and 2θ be the probability that “Thumbtack 2” (we will abbreviate it as T2) shows heads. You are given the following Dataset (6 examples). T1 Tails T2 Heads T1 Tails T1 Tails T2 Heads T2 ...
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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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...EXECUTIVE SUMMARY INTRODUCTION/BACKGROUND The objective of the thesis is to predict and optimize the mechanical properties of Aircraft fuselage aluminium (AA5083). Firstly, data-driven modelling techniques such as Artificial Neural – Fuzzy networks and regressive analysis are used and by making the effective use of experimental data, FIS membership function parameters are trained. At the core, mathematical model that functionally relates tool rotational speed and forward movement per revolution to that of Yield strength, Ultimate strength and Weld quality are obtained. Also, simulations are performed, and the actual values are compared with the predicted values. Finally, multi-objective optimization of mechanical properties fuselage aluminium was undertaken using Genetic Algorithm to improve the performance of the tools industrially. AIMS AND OBJECTIVES Objectives of the dissertation include Understanding the basic principles of operation of Friction Stir Welding (FSW). Gaining experience in modelling and regressive analysis. Gaining expertise in MATLAB programming. Identifying the best strategy to achieve the yield strength, Ultimate Tensile strength and Weld quality of Friction Stir Welding. Performing optimization of mechanical properties of FSW using Genetic Algorithm. I To draw conclusions on prediction of mechanical properties of FSW optimization of aircraft fuselage aluminium. ACHIEVEMENTS The basic principles of friction welding of the welding...
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...\subsection{KNN classifier} Cover and Hart \cite{cover1967nearest} introduced the idea of nearest neighbor pattern classification.In pattern recognition field, KNN is one of the most non-parametric classifiers that is used for a supervised learning algorithm \cite{murphy2012machine,kaghyan2012activity}. % ,murthy2015ann,suguna2010improved}. KNN searches the K points in the training set that are closest to the test input x which is judged to the highest class probability \cite{murphy2012machine}. Based on the predefined(classified) class labels which is a set of N labeled instances $\{x_i, y_i\}^1_N$, the classifier task is to predict the label of class that has the unknown(unclassified) query vector $x_0$ \cite{song2007iknn}. Using the K nearest neighbors of $x_0$, the KNN could make a majority vote to decide the class label of $x_0$. In order to classify a new object, KNN don't use any model to fit, but it depends on memory(the attributes and training samples). The criteria of classification are yielded by the training samples themselves without any additional data. KNN compute a prediction value from the neighborhood classes to classify the new query instance. %the decision rule is to assign an unclassified sample %point to the classification of the nearest of a collection of predetermined classified %points. Various ways are existing to compute the distance between two points in multidimensional space. Suppose we have x, y two n-dimensional vectors, such that $x=\{x_1...
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...Stereoscopic Building Reconstruction Using High-Resolution Satellite Image Data Anonymous submission Abstract—This paper presents a novel approach for the generation of 3D building model from satellite image data. The main idea of 3D modeling is based on the grouping of 3D line segments. The divergence-based centroid neural network is employed in the grouping process. Prior to the grouping process, 3D line segments are extracted with the aid of the elevation information obtained by using area-based stereo matching of satellite image data. High-resolution IKONOS stereo images are utilized for the experiments. The experimental result proved the applicability and efficiency of the approach in dealing with 3D building modeling from high-resolution satellite imagery. Index Terms—building model, satellite image, 3D modeling, line segment, stereo I. I NTRODUCTION Extraction of 3D building model is one of the important problems in the generation of an urban model. The process aims to detect and describe the 3D rooftop model from complex scene of satellite imagery. The automated extraction of the 3D rooftop model can be considered as an essential process in dealing with 3D modeling in the urban area. There has been a significant body of research in 3D reconstruction from high-resolution satellite imagery. Even though a natural terrain can be successfully reconstructed in a precise manner by using correlation-based stereoscopic processing of satellite images [1], 3D building reconstruction...
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...the stronger trend. In this paper we investigate the use of the Hurst exponent to classify series of financial data representing different periods of time. Experiments with backpropagation Neural Networks show that series with large Hurst exponent can be predicted more accurately than those series with H value close to 0.50. Thus Hurst exponent provides a measure for predictability. KEY WORDS Hurst exponent, time series analysis, neural networks, Monte Carlo simulation, forecasting In time series forecasting, the first question we want to answer is whether the time series under study is predictable. If the time series is random, all methods are expected to fail. We want to identify and study those time series having at least some degree of predictability. We know that a time series with a large Hurst exponent has strong trend, thus it’s natural to believe that such time series are more predictable than those having a Hurst exponent close to 0.5. In this paper we use neural networks to test this hypothesis. Neural networks are nonparametric universal function approximators [9] that can learn from data without assumptions. Neural network forecasting models have been widely used in financial time series analysis during the last decade [10],[11],[12]. As universal function approximators, neural networks can be used for surrogate predictability. Under the same conditions, a time series with a smaller forecasting error than another is said to be more predictable. We study the Dow-Jones...
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...Psychology p.1 Psychology Experiments Psychology p.2 Abstract I have just been asked to teach the concept of adaptation to a psychology class. I will conduct three experiments for this. Then I will write a paper that describes adaption and how I experienced it in my experiments. The paper will cover the following. I will fully describe process and results of the experiments that I chose. I will also answer what is sensory adaptation. Then I will explain the concept of sensory adaptation, and refer to my text for the definition of sensory adaptation. Next I will explain how adaptation is evident in each of my experimental results. I will provide a comprehensive description of the sensory systems that are involved in the experiments that I performed. This description will include what happens from the receptors to the brain. My discussion will illustrate what was experienced in each experiment. Finally I will discuss how adaptation is important from an evolutionary perspective. Psychology p.3 There are three experiments I will conduct and they are as fallows. Experiment 1: Rub your index fingers gently over a piece of very coarse sandpaper a few times and rate its coarseness on a scale from 1 (very soft) to 7 (very coarse). After a minute or two, rub the same finger over the paper and again rate its coarseness. Did your perception of the coarseness change? How? Record your reaction. In experiment 2 we will Prepare one cup with sugar water and one with fresh...
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...International Review of Business Research Papers Vol.2. No.4. December 2006, Pp. 39-50 eBusiness-Process-Personalization using Neuro-Fuzzy Adaptive Control for Interactive Systems Zunaira Munir1 , Nie Gui Hua2 , Adeel Talib3 and Mudassir Ilyas4 ‘Personalization’, which was earlier recognized as the 5th ‘P’ of e-marketing , is now becoming a strategic success factor in the present customer-centric e-business environment. This paper proposes two changes in the current structure of personalization efforts in ebusinesses. Firstly, a move towards business-process personalization instead of only website-content personalization and secondly use of an interactive adaptive scheme instead of the commonly employed algorithmic filtering approaches. These can be achieved by applying a neuro-intelligence model to web based real time interactive systems and by integrating it with converging internal and external e-business processes. This paper presents a framework, showing how it is possible to personalize e-business processes by adapting the interactive system to customer preferences. The proposed model applies Neuro-Fuzzy Adaptive Control for Interactive Systems (NFACIS) model to converging business processes to get the desired results. Field of Research: Marketing, e-business 1. Introduction: As Kasanoff (2001) mentioned, the ability to treat different people differently is the most fundamental form of human intelligence. "You talk differently to your boss than to...
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