...true Display = International Currency Conversion Program Display = Make a Selection Display = International Currency Types Display = Canadian Dollars Display = Mexican Pesos Display = English Pounds Display = Japanese Yen Display = French Francs Display = Quit Display = Enter a Selection Input Currency Type If Currency Type >= 5 and Currency Type <=25 then Set = false Else if currency Type = 26 Display = Quitting Currency Conversion Display = Error 4 Invalid Menu Selection End Display Menu Display Menu Test Produce As the user is deciding which they would like to select an international currency type. When the user enters a number between 5 and 26 corresponding with a currency type, then the program is set to false. If 26 is selected the program displays “Quitting Currency Conversion.” But if the user does not enter a number between 5 and 26 the program displays “Error 4: Invalid Menu Selection” and the set it true. Test Case 1 Input: user enters 5 for Canadian Dollars Set = false Test Case 2 Input: user enters 6 for Mexican Pesos Set = false Test Case 3 Input: user enters 7 for English Pounds Set = false Test Case 4 Input: use enters 8 for Japanese Yen Set = false Test Case 5 Input: user enters 9 for French Francs Set = false Test Case 6 Input: user enter 10 for Quit Set = false Test Case 7 Input: user enters 11 Output: display “Error 4: Invalid Menu Selection” Set = true Get International Value Declare value as integer Declare...
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...CONVERSION: Main Module Declare selection as integer Declare currency type as integer Declare international value as real Declare US value as real Do while user wants to continue Call display Menu Call get international value Call convert currency Call display results End while Print exit message End main module Display Menu Declare continue as Boolean Set continue = true While continue = true Display "Welcome to the international currency conversion program" Display "Please make a selection" Display "International Currency Types:" Display "1: Canadian Dollars" Display "2: Mexican Pesos" Display "3: English Pounds" Display "4: Japanese Yen" Display "5: French Francs" Display "6: Quit" Display "Enter a selection: "; Input currency type If currency type >= 1 and currency type <= 5 then Set continue = false else if currency type = 6 Display “Quitting Currency Conversion” continue = false else Display "Error 4: Invalid menu selection." continue = true end if end While End Display Menu Get International Value Declare value as integer Declare continue as Boolean Set continue = true While continue = true Display "Enter a currency value (positive number):" Input international value if international value > 0 and international value <=(NNN) NNN-NNNNthen continue = false else Display "Error 1: Invalid input--Negative Number" continue = true end if end while End Get International Value ...
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...a job offer following an employment interview.The perceptual error that is affecting recruiters’ perceptions in this case is the halo perceptual error. The halo effect influences the interviewer’s perceptions of the applicant based the applicant’s answers to questions. The recruiter then develops a corresponding overall positive or negative impression of the applicant. According to Rosenweig (2007), applicants answers can and have been judged as either intelligent or unintelligent based simply on the name of the university they attended and how well known it is. Of course name recognition is not the only instance in which the halo effect can determine employment selection. Looks or attractiveness tend to lead to the halo effect in hiring too. Attractive candidates are perceived as having more favorable and agreeable qualities, they are trusted more and the expectations of them are higher. The unfortunate thing is that these halo errors can and have caused the right candidate to be overlooked while the wrong candidate is chosen due to nothing more than another person’s skewed perception. While there is actually nothing in the case study that deals directly with stereotyping in a technical sense, there is still conscious and unconscious stereotyping that occurs during the interview and job selection process. In an article by Elizabeth Umphress (2008), if selection decisions are made by someone who has a negative attitude or prejudice against a particular group then the evaluation...
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...Zhouzghzu “Jack” Liang Sallie Smith April 15, 2016 0 MIS6324.503/BUAN 6324.503 Table Of Contents Executive Summary ............................................................................................................... 2 1. Project Motivation........................................................................................................... 3 2. Data Description .............................................................................................................. 4 3. Data Preprocessing-Variable Exploration, Imputation and Transformation...................... 5 4. Variable Selection ............................................................................................................ 6 5. Descriptive Statistics of Data ......................................................................................... 11 6. Model Selection............................................................................................................. 12 7. What is a Regression Model? ......................................................................................... 13 8. Logistic Regression Model - Stepwise............................................................................. 14 9. Chosen Models .............................................................................................................. 15 10. Analyzing Stepwise Regression Model: ....................................................................... 16 11...
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...end user sees. In this paper, one can find instructions on what the end user will need to enter and in which order to enter such information in order to give accurate data output. This will assist in avoiding errors, in order to avoid the garbage in garbage out cliché from occurring to the end user. To avoid this cliché, there is a piece in the instructions and end user prompts, which will identify obvious errors in the process, before finishing the process, which in turn will minimize incorrect data. This step would prompt the end user to review, confirm and correct any information which may look incorrect to the system. One can also find in this paper the examples of how each process works in the background to give accurate results. There will be a total of three artifacts and one set of instructions on how end users must enter data. There will be instructions in programing order, but one can also find following these instructions, charts which may make instructions easier to follow, or at least easier to understand. Currency Conversion Input Data and Output Process | Input | Processes | Output | Select Country | Generate Currency List | Display Currencies available for the country selected. | Select Currency Option | Process currency selection | Display US currency amount requesting dollar amount to convert. | Enter US dollar amount to convert. | Perform conversion USD to Currency selected. | Display amount in foreign currency. | Confirm amount in...
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...Abstract Abstract The goal of this master’s thesis is to identify and evaluate data mining algorithms which are commonly implemented in modern Medical Decision Support Systems (MDSS). They are used in various healthcare units all over the world. These institutions store large amounts of medical data. This data may contain relevant medical information hidden in various patterns buried among the records. Within the research several popular MDSS’s are analysed in order to determine the most common data mining algorithms utilized by them. Three algorithms have been identified: Naïve Bayes, Multilayer Perceptron and C4.5. Prior to the very analyses the algorithms are calibrated. Several testing configurations are tested in order to determine the best setting for the algorithms. Afterwards, an ultimate comparison of the algorithms orders them with respect to their performance. The evaluation is based on a set of performance metrics. The analyses are conducted in WEKA on five UCI medical datasets: breast cancer, hepatitis, heart disease, dermatology disease, diabetes. The analyses have shown that it is very difficult to name a single data mining algorithm to be the most suitable for the medical data. The results gained for the algorithms were very similar. However, the final evaluation of the outcomes allowed singling out the Naïve Bayes to be the best classifier for the given domain. It was followed by the Multilayer Perceptron and the C4.5. Keywords: Naïve Bayes, Multilayer...
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...Tutorial on Classification Igor Baskin and Alexandre Varnek Introduction The tutorial demonstrates possibilities offered by the Weka software to build classification models for SAR (Structure-Activity Relationships) analysis. Two types of classification tasks will be considered – two-class and multi-class classification. In all cases protein-ligand binding data will analyzed, ligands exhibiting strong binding affinity towards a certain protein being considered as “active” with respect to it. If it is not known about the binding affinity of a ligand towards the protein, such ligand is conventionally considered as “nonactive” one. In this case, the goal of classification models is to be able to predict whether a new ligand will exhibit strong binding activity toward certain protein biotargets. In the latter case one can expect that such ligands might possess the corresponding type of biological activity and therefore could be used as ‘’hits” for drug design. All ligands in this tutorial are described by means of an extended set of MACCS fingerprints, each of them comprising 1024 bits, the “on” value of each of them indicating the presence of a certain structural feature in ligand, otherwise its value being “off”. Part 1. Two-Class Classification Models. 1. Data and descriptors. The dataset for this tutorial contains 49 ligands of Angeotensin-Converting Enzyme (ACE) and 1797 decoy compounds chosen from the DUD database. The set of "extended" MACCS fingerprints is used as...
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...R. S. Rajesh Reader, Department of CSE, Manonmanium Sundaranar University,, Tirunelveli, Tamilnadu, India rs_rajesh@yahoo.co.in Abstract A variety of intrusion prevention techniques, such as user authentication using passwords, avoidance of programming errors and information protection, has been used to protect computer systems. However information prevention alone is not sufficient to protect our systems as those systems become even more complex with the rapid growth and expansion of Internet technology and local network systems. Moreover, programming errors, firewall configuration errors and ambiguous or undefined security policies add to the system’s complexity. An Intrusion Detection system (IDS) is therefore needed as another layer to protect computer systems. The IDS is one of the most important techniques of information dynamic security technology. It is defined as a process of monitoring the events occurring in a computer system or network and analyzing them to differentiate between normal activities of the system and behaviors that can be classified as suspicious or intrusive. Current Intrusion Detection Systems have several known shortcomings, such as low accuracy (registering high False Positives and False Negatives); low real-time performance (processing a large amount of traffic in real time); limited scalability (storing a large number of user profiles and attack signatures); an inability to detect new attacks (recognizing new attacks when they are launched for the...
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...Data Mining Third Edition This page intentionally left blank Data Mining Practical Machine Learning Tools and Techniques Third Edition Ian H. Witten Eibe Frank Mark A. Hall AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Morgan Kaufmann Publishers is an imprint of Elsevier Morgan Kaufmann Publishers is an imprint of Elsevier 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA This book is printed on acid-free paper. Copyright © 2011 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must...
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...Data Mining Practical Machine Learning Tools and Techniques The Morgan Kaufmann Series in Data Management Systems Series Editor: Jim Gray, Microsoft Research Data Mining: Practical Machine Learning Tools and Techniques, Second Edition Ian H. Witten and Eibe Frank Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration Earl Cox Data Modeling Essentials, Third Edition Graeme C. Simsion and Graham C. Witt Location-Based Services Jochen Schiller and Agnès Voisard Database Modeling with Microsoft® Visio for Enterprise Architects Terry Halpin, Ken Evans, Patrick Hallock, and Bill Maclean Designing Data-Intensive Web Applications Stefano Ceri, Piero Fraternali, Aldo Bongio, Marco Brambilla, Sara Comai, and Maristella Matera Mining the Web: Discovering Knowledge from Hypertext Data Soumen Chakrabarti Understanding SQL and Java Together: A Guide to SQLJ, JDBC, and Related Technologies Jim Melton and Andrew Eisenberg Database: Principles, Programming, and Performance, Second Edition Patrick O’Neil and Elizabeth O’Neil The Object Data Standard: ODMG 3.0 Edited by R. G. G. Cattell, Douglas K. Barry, Mark Berler, Jeff Eastman, David Jordan, Craig Russell, Olaf Schadow, Torsten Stanienda, and Fernando Velez Data on the Web: From Relations to Semistructured Data and XML Serge Abiteboul, Peter Buneman, and Dan Suciu Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations Ian H. Witten and Eibe Frank ...
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...Psychological Assessment PYC 4807 Assignment 1 Unique number: 553283 IRMA VISAGIE Student number: 42056829 Open Rubric 2 Table of Content Pages 1. Introduction p4 Section A 2. Steps in developing a measure p4 2.1 The planning phase p4 2.1.1 Specify the aim of the measure 2.1.2 Define the content of the measure 2.1.3 Develop the test plan 2.2 Item writing p7 2.2.1 Write the items 2.2.2 Review the items 2.3 Assemble and pre-test the experimental version of the measure p8 2.3.1 Arrange the items 2.3.2 Finalise the length 2.3.3 Answer protocols 2.3.4 Develop administration instruction 2.3.5 Pre-test the experimental version of the measure 2.4 The item-analysis phase p9 2.4.1 Classical test-theory item analysis: Determine item difficulty (p) 2.4.2 Classical test-theory item analysis: Determine discriminating power 2.4.3 Item response theory (IRT) 2.4.4 Identify items for final pool 2.5 Revise and standardise the final version of the measure p 11 2.5.1 Revise the items and test 2.5.2 Select items for the final version 2.5.3 Refine administration instructions and scoring procedures 2.5.4 Administer the final version 2.6 Technical evaluation and establishing norms 2.6.1 Establish validity and reliability 2.6.1.1 Reliability 2.6.1.2 Validity 2.6.2 Establish norms, set performance standards or cut-scores p 11 3 2.7 Publish and refine continuously p 15 2.7.1 Compile the test manual 2.7.2 Submit the measure for classification 2.7.3 Publish and market the measure...
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...Selection statements Selection is used to select which statements are to be performed next based on a condition being true or false. Relational expressions In the solution of many problems, different actions must be taken depending on the value of the data. The if statement in C I used to implement such s decision structure in its simplest form – that of selecting a statement to be executed only if a condition is satisfied. Syntax: if(condtion) statement executed if condition is true When an executing program encounters the if statement, the condition is evaluated to determine its numerical value, which is then interpreted as either true or false. If the condition evaluates to any non-0 value (positive or negative), the condition is considered as a “true” condition and the statement following the if is executed; otherwise this statement is not executed. Relational Operators In C Relational operator | Meaning | Example | < | Less than | age < 30 | > | Greater than | height > 6.2 | <= | Less than or equal to | taxable <= 200000 | >= | Greater than or equal to | temp >= 98.6 | == | Equal to | grade == 100 | != | Not equal to | number !=250 | In creating relational expressions, the relational operators must be typed exactly as given in the above table. Thus, although the following relational expressions are all valid: age > 40 length <= 50 temp >= 98.6 3 < 4 flag == done day != 5 The following are invalid: length =< 50 ...
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...sacrifice their social life in order to keep high grades and be productive at work? Usually, part time jobs does not require deep knowledge or experience, so are students taking these jobs only for financial purposes, or they also have career perspectives in the field/company they are working in part-time? Lastly, have students ever considered leaving their jobs if studying results were decreasing, or maybe they had thoughts to drop- out of university as salary was satisfying enough not to seek further degree? To find out these answers 20 random university students will be surveyed. First, and most common error is stereotyping. Companies loose qualified and skilled workers only because of this kind of unintentional discrimination. Grouping and labeling people according to one of their characteristics can warn managers about incompetent candidate, but also can be a false judgment. In this case we can take very simplistic examples: person having facial piercing is not a suitable for a manager position, even if having all the qualities and experience needed, he will not be able to represent company in a solid way. On the other hand female can be not hired for a taxi...
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...Review of Outlier Detection Methods INTRODUCTION Outliers or anomalies can exist in all types of collected data. The presence of outliers may indicate something sinister such as unauthorised system access or fraudulent activity, or may be a new and previously unidentified occurrence. Whatever the cause of these outliers, it is important they are detected so appropriate action can be taken to minimise their harm if malignant or to exploit a newly discovered opportunity. Chandola, Banerjee and Kumar (2007) conducted a comprehensive survey of outlier detection techniques, which highlighted the importance of detection across a wide variety of domains. Their survey described the categories of outlier detection, applications of detection and detection techniques. Chandola et al. identified three main categories of outlier detection - supervised, semi-supervised and unsupervised detection. Each category utilises different detection techniques such as classification, clustering, nearest neighbour and statistical. Each category and technique has several strengths and weaknesses compared with other outlier detection methods. This review provides initial information on data labelling and classification before examining some of the existing outlier detection techniques within each of the three categories. It then looks at the use of combining detection techniques before comparing and discussing the advantages and disadvantages of each method. Finally, a new classification technique...
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...* Chapter Introduction * 7-1Recruiting Employees * 7-1aInternal Recruiting * 7-1bExternal Recruiting * 7-1cMethods for External Recruiting * 7-1dInternships: A Mixed Model * 7-1eRealistic Job Previews * 7-2The Selection Process * 7-2aSteps in Selection * 7-2bBasic Selection Criteria * 7-3Popular Selection Techniques * 7-3aApplications and Background Checks * 7-3bEmployment Tests * 7-3cWork Simulations * 7-3dPersonal Interviews * 7-3eOther Selection Techniques * 7-4The Selection Decision * 7-4aMultiple Indicators * 7-4bSelection Errors * 7-4cReliability and Validity * 7-4dLegal and Effectiveness Issues in Recruiting and Selection * 7-5Training and Development * 7-5aManagement Development * 7-5bOrganizational Development * 7-5cEvaluating Training and Development * Chapter Review * Chapter Summary * Learning Objectives * Key Terms * Key Points for Future Managers The Coaching Carousel It’s called the coaching carousel. Every year when football season ends, coaches get fired and new coaches get hired. And because every team needs a head coach, there is precisely one job at each team and that job has to be filled—quickly. Consider the National Football League. Every team wants to make the playoffs, so that becomes the critical measure of success. The day after the regular season ends is known as Black...
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