What is the relationship in the distribution of high order and low order stores in Road 9‚ an urban CBD district‚ in Maadi‚ Cairo‚ Egypt? Table of Contents Hypothesis | Page 2 | Background | Page 2 | Method of data collection | Page 3 | Data analysis and presentation | Page 4 | Results | Page 6 | Interpretation of results | Page 6 | Data misrepresentation | Page 6 | Conclusion | Page 7 | Evaluation | Page 8 | List of Illustrations Figure 1: Location of Egypt | Page 2 |
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Non-Technical Project MODERN POWER SYSTEM STABILITY ASSESSMENT TOOLS – A REVIEW Student: Nhut Tien Nguyen Matrikel-Nr: 201975 Supervisor: Dr.-Ing. C. Nguyen Mau Magdeburg‚ November 2013 MODERN POWER SYSTEM STABILITY ASSESSMENT TOOLS – A REVIEW i Keywords Power system stability‚ frequency stability‚ oscillatory stability‚ transient stability‚ voltage stability‚ direct method‚ time domain simulation‚ dynamic security assessment‚ Prony Analysis‚ Eigenanalysis‚ reactive power
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can automatically find a traffic accident‚ search for the spot and then send the basic information to first aid center within two seconds covering geographical coordinates‚ the time and circumstances in which a traffic accident takes place. GPS software is fitted in the vehicle will now start communicate with the satellite and get the latitude and longitude values and send the information to the centralized server. Then the server will search the nearest hospital and send the accident information
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1. INTRODUCTION Obtaining a high-resolution (HR) image from single or multiple low-resolution (LR) images‚ known as “super-resolution” has been a problem. High resolution means high pixel density‚ also referred to as high-definition (HD). An HR image brings out details that would be blocked out in an LR image. More pixels in the same area imply a higher sampling frequency thereby offering more details. Due to the tremendous progress in sensor and chip manufacturing techniques‚ we can now obtain
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International ACM SIGIR Conference on Research and Development in Information Retrieval. Han‚ Jiawei and Kamber‚ Micheline. Data Mining: Concepts and Techniques. Lifshits‚ Yury. Algorithms for Nearest Neighbor. Steklov Insitute of Mathematics at St. Petersburg. April 2007 Cherni‚ Sofiya. Nearest Neighbor Method. South Dakota School of Mines and Technology. 26 Acknowledgements I would like to thank Mrs. Shubhangi Gawali for being an excellent mentor and a patient guide throughout this whole learning
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Machine Learning Journal (2003) 53:23-69 Theoretical and Empirical Analysis of ReliefF and RReliefF ˇ Marko Robnik-Sikonja (marko.robnik@fri.uni-lj.si) Igor Kononenko (igor.kononenko@fri.uni-lj.si) University of Ljubljana‚ Faculty of Computer and Information Science‚ Trˇ aˇka 25‚ z s 1001 Ljubljana‚ Slovenia tel.: + 386 1 4768386 fax: + 386 1 4264647 Abstract. Relief algorithms are general and successful attribute estimators. They are able to detect conditional dependencies between attributes
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the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. In both cases‚ the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: • In k-NN classification‚ the output is a class membership. An object is classified by a majority vote of its neighbors‚ with the object being assigned to the class most common among its k nearest neighbors (k is
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I. Introduction Opinions are important to all humans as they influence ones behaviour. In today’s competitive world‚ businesses and organizations always want to find consumer or public opinions about their products and services. Consumers also want to know the opinions of existing users of a product before purchasing it. In the past‚ when an individual needed opinions‚ he/she asked friends and family. When an organization or a business needed public or consumer opinions‚ it conducted surveys‚ opinion
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well? Best performing model My best performing model was the using the multilayer perception classifier with a learning rate of 0.2 . I filtered down the attributes to the most useful 120. I chose 120 because it when I ran the ‘filterattributeeval’ search with the ‘ranker’ function I took all the attributes ranked 0.1 or higher. I felt that anything ranked below 0.1 could be an interference with the accuracy of the prediction. Filtering my attributes to a lower amount also helped to speed up the run
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background replacement. BLOCK DIAGRAM: EXISITING SCHEME: Poisson matting‚ Closed-form matting and Non local matting. The assumptions of large kernels by and local color line of are relaxed in KNN matting using non local principles and K nearest neighbors‚ SVR Matting‚ Bayesian Matting‚ and Robust Matting. PROPOSED SCHEME: In this paper‚ a new non-parametric sampling based method is presented that uses texture as an additional feature for the matting task. Our sampling strategy considers
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