...REVIEW ON SORTING ALGORITHMS A comparative study on two sorting algorithms By Pooja Adhikari A Term Paper Submitted to the Faculty of Dr. Gene Boggess Mississippi State University In the Department of Computer Science & Engineering Mississippi State, Mississippi 04 20072 ABSTRACT Any number of practical applications in computing requires things to be in order. The performance of any computation depends upon the performance of sorting algorithms. Like all complicated problems, there are many solutions that can achieve the same results. One sort algorithm can do sorting of data faster than another. A lot of sorting algorithms has been developed to enhance the performance in terms of computational complexity, memory and other factors. This paper choose two of the sorting algorithms among them selection sort and shell sort and compares the various performance factor among them. 1. INTRODUCTION Sorting is the rearrangement of things in a list into their correct lexicographic order. A number of sorting algorithms have been developed like include heap sort , merge sort, quick sort, selection sort all of which are comparison based sort .There is another class of sorting algorithms which are non comparison based sort. This paper gives the brief introduction about sorting algorithms [2] where it discuss about the class of sorting algorithms and their running times. It mainly analyses the performance between two...
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...SCHEME OF EXAMINATION FOR MASTER OF COMPUTER APPLICATIONS (MCA) (SIX-SEMESTER Programme) |Semester – I | |Paper |Title of the Paper |Duration |Maximum Marks |Total | |No. | |Of Exam | | | | | | |Theory |Sessional* | | |MCA-101 |Computer Fundamentals and Problem Solving Using C |3 Hours |80 |20 |100 | |MCA-102 |Computer Organisation |3 Hours |80 |20 |100 | |MCA-103 |Discrete Mathematical Structures |3 Hours |80 |20 |100 | |MCA-104 |Software Engineering |3 Hours |80 |20 |100 | |MCA-105 |Computer Oriented Numerical and Statistical Methods |3 Hours |80 |20 |100 | |MCA-106 |Software Laboratory - I |3 Hours | | |100 | | |C (Based on MCA-101) |...
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...International Journal of Software Engineering and Its Applications Vol. 7, No. 2, March, 2013 Integrating Knowledge Management with Business Intelligence Processes for Enhanced Organizational Learning Rizwan Shehzad and Muhammad Naeem Ahmed Khan Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science & Tech., Islamabad, Pakistan rizince@yahoo.com, mnak2010@gmail.com Abstract Knowledge Management (KM), either in a tacit or explicit form, plays an important role in decision making. Business Intelligence (BI) has acquired significant importance in the business world due to the recent technological advancements and availability of state-of-theart software tools. Presently, most of the organizations across the globe are adopting BI solutions to promote business, retain customers, earn maximum profits, reduce recurring expenditures, etc. KM, on the other hand, has not gained significant acknowledgement from an implementation perspective. Moreover, the effect of BI on KM and vice versa is rarely observed. In this paper, we propose a model where KM and BI can benefit from each other. Since both of the said fields have a wide range of features, the proposed model is based on specific features. For that purpose, we have identified a number of Critical Success Factors (CSFs) related to both BI and KM technologies reported in contemporary research. Afterwards, we have drawn a comparison pertinent to the effectiveness of these CSFs with the similar...
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...Souvorov1, Grigory Starchenko1, Tatiana A. Tatusova1, Lukas Wagner1, Yanli Wang1, W. John Wilbur1, Eugene Yaschenko1 and Jian Ye1 1 Downloaded from http://nar.oxfordjournals.org/ by guest on March 20, 2015 National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA and 2University Clinic of Blood Group Serology and Transfusion Medicine, Medical University of Graz, Auenbruggerplatz 3, A-8036 Graz, Austria Received September 16, 2010; Revised October 29, 2010; Accepted November 1, 2010 ABSTRACT In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI Web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central (PMC), Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Primer-BLAST, COBALT, Electronic PCR, OrfFinder, Splign,...
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...THE FUTURE OF EMPLOYMENT: HOW SUSCEPTIBLE ARE JOBS TO COMPUTERISATION?∗ Carl Benedikt Frey† and Michael A. Osborne‡ September 17, 2013 . Abstract We examine how susceptible jobs are to computerisation. To assess this, we begin by implementing a novel methodology to estimate the probability of computerisation for 702 detailed occupations, using a Gaussian process classifier. Based on these estimates, we examine expected impacts of future computerisation on US labour market outcomes, with the primary objective of analysing the number of jobs at risk and the relationship between an occupation’s probability of computerisation, wages and educational attainment. According to our estimates, about 47 percent of total US employment is at risk. We further provide evidence that wages and educational attainment exhibit a strong negative relationship with an occupation’s probability of computerisation. Keywords: Occupational Choice, Technological Change, Wage Inequality, Employment, Skill Demand JEL Classification: E24, J24, J31, J62, O33. We thank the Oxford University Engineering Sciences Department and the Oxford Martin Programme on the Impacts of Future Technology for hosting the “Machines and Employment” Workshop. We are indebted to Stuart Armstrong, Nick Bostrom, Eris Chinellato, Mark Cummins, Daniel Dewey, David Dorn, Alex Flint, Claudia Goldin, John Muellbauer, Vincent Mueller, Paul Newman, Seán Ó hÉigeartaigh, Anders Sandberg, Murray Shanahan, and Keith ...
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...multi-objective multi-mode model for solving discrete time–cost–quality trade-off problems (DTCQTPs) with preemption and generalized precedence relations. The proposed model has three unique features: (1) preemption of activities (with some restrictions as a minimum time before the first interruption, a maximum number of interruptions for each activity, and a maximum time between interruption and restarting); (2) simultaneous optimization of conflicting objectives (i.e., time, cost, and quality); and (3) generalized precedence relations between activities. These assumptions are often consistent with real-life projects. A customized, dynamic, and self-adaptive version of a multiobjective evolutionary algorithm is proposed to solve the scheduling problem. The proposed multi-objective evolutionary algorithm is...
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...multi-objective multi-mode model for solving discrete time–cost–quality trade-off problems (DTCQTPs) with preemption and generalized precedence relations. The proposed model has three unique features: (1) preemption of activities (with some restrictions as a minimum time before the first interruption, a maximum number of interruptions for each activity, and a maximum time between interruption and restarting); (2) simultaneous optimization of conflicting objectives (i.e., time, cost, and quality); and (3) generalized precedence relations between activities. These assumptions are often consistent with real-life projects. A customized, dynamic, and self-adaptive version of a multiobjective evolutionary algorithm is proposed to solve the scheduling problem. The proposed multi-objective evolutionary algorithm is...
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...------------------------------------------------- Data compression From Wikipedia, the free encyclopedia (Redirected from Video compression) "Source coding" redirects here. For the term in computer programming, see Source code. In digital signal processing, data compression, source coding,[1] or bit-rate reduction involves encoding information using fewer bits than the original representation.[2]Compression can be either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by identifying unnecessary information and removing it.[3] The process of reducing the size of a data file is referred to as data compression. In the context of data transmission, it is called source coding (encoding done at the source of the data before it is stored or transmitted) in opposition to channel coding.[4] Compression is useful because it helps reduce resource usage, such as data storage space or transmission capacity. Because compressed data must be decompressed to use, this extra processing imposes computational or other costs through decompression; this situation is far from being a free lunch. Data compression is subject to a space–time complexity trade-off. For instance, a compression scheme for video may require expensive hardware for the video to be decompressed fast enough to be viewed as it is being decompressed, and the option to decompress the video...
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...Interdisciplinary Journal of Information, Knowledge, and Management Volume 2, 2007 Approach to Building and Implementing Business Intelligence Systems Celina M. Olszak and Ewa Ziemba University of Economics, Katowice, Poland olszak@ae.katowice.pl ewa.ziemba@ae.katowice.pl Abstract Abstract: The article aims at describing processes of building Business Intelligence (BI) systems. Taking the BI systems specifics into consideration, the authors present a suggested methodology of the systems creation and implementation in organisations. The considerations are focused on objectives and functional areas of the BI in organisations. Hence, in this context the approach to be used while building and implementing the BI involves two major stages that are of interactive nature, i.e. BI creation and BI “consumption”. A large part of the article is devoted to presenting objectives and tasks that are realised while building and implementing BI. Key words: Business Intelligence, methodology of BI construction, designing BI, business decision-making, knowledge management Introduction Socio-economic reality of contemporary organisations has made organisations face some necessity to look for instruments that would facilitate effective acquiring, processing and analysing vast amounts of data that come from different and dispersed sources and that would serve as some basis for discovering new knowledge. For long time management information systems (MIS) have been supporting organisations in their...
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...Chemistry/Physics Laboratory Workshop Physical Training-I NCC/NSO/NSS L 3 3 3 1 3 0 0 0 0 13 T 1 0 1 0 0 0 0 0 0 2 1 1 1 1 0 0 0 0 4 1 1 0 0 0 0 0 0 2 0 0 0 0 P 0 0 0 3 0 2 3 2 2 8 0 0 0 0 0 2 2 2 2 0 0 0 0 0 2 2 2 6 0 0 8 2 C 8 6 8 5 6 2 3 0 0 38 8 8 8 8 6 2 0 0 40 8 8 6 6 6 2 2 2 40 6 6 8 2 Course No EC-1101 CS-1101 MA-1102 ME-1101 PH-1101/ CH-1101 CS-1111 EE-1111 PH-1111/ CH-1111 Course Name Semester-2 Basic Electronics Introduction to Computing Mathematics-II Engineering Mechanics Physics/Chemistry Computing Laboratory Electrical Science Laboratory Physics/Chemistry Laboratory Physical Training –II NCC/NSO/NSS Semester-4 Structural Analysis-I Hydraulics Environmental Engg-I Structural Design-I Managerial Economics Engg. Geology Laboratory Hydraulics Laboratory Physical Training-IV NCC/NSO/NSS Semester-6 Structural Design-II Structural Analysis-III Foundation Engineering Transportation Engineering-II Hydrology &Flood Control Concrete Lab Structural Engineering Lab L 3 3 3 3 3 0 0 0 0 0 15 3 3 3 3 3 0 0 0 0 15 3 3 3 3 3 0 0 T 0 0 1 1 1 0 0 0 0 0 3 1 1 0 1 0 0 0 0 0 3 1 1 1 0 0 0 0 P 0 0 0 0 0 2 2 2 2 2 6 0 0 0 0 0 2 2 2 2 4 0 0 0 0 0 2 2 C 6 6 8 8 8 2 2 2 0 0 42 8 8 6 8 6 2 2 0 0 40 8 8 8 6 6 2 2 MA-1201 CE- 1201 CE -1202 CE -1203 CE-1204 CE-1211 Semester-3 Mathematics-III Building Materials and...
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...Computing, as the basis for a Fraud Detection solution. Indeed, we believe that this architecture will stimulate research, and more importantly organizations, to invest in Analytics and Statistical Fraud-Scoring to be used in conjunction with the already in-place preventive techniques. Therefore, in this research we explore different strategies to build a Streambased Fraud Detection solution, using advanced Data Mining Algorithms and Statistical Analysis, and show how they lead to increased accuracy in the detection of fraud by at least 78% in our reference dataset. We also discuss how a combination of these strategies can be embedded in a Stream-based application to detect fraud in real-time. From this perspective, our experiments lead to an average processing time of 111,702ms per transaction, while strategies to further improve the performance are discussed. Keywords: Fraud Detection, Stream Computing, Real-Time Analysis, Fraud, Data Mining, Retail Banking Industry, Data Preprocessing, Data Classification, Behavior-based Models, Supervised Analysis, Semi-supervised Analysis Sammanfattning Privatbankerna har drabbats hårt av bedrägerier de senaste åren. Bedragare har lyckats kringgå forskning och tillgängliga system och lura bankerna och deras kunder. Därför vill vi införa en ny, polyvalent...
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...Computing, as the basis for a Fraud Detection solution. Indeed, we believe that this architecture will stimulate research, and more importantly organizations, to invest in Analytics and Statistical Fraud-Scoring to be used in conjunction with the already in-place preventive techniques. Therefore, in this research we explore different strategies to build a Streambased Fraud Detection solution, using advanced Data Mining Algorithms and Statistical Analysis, and show how they lead to increased accuracy in the detection of fraud by at least 78% in our reference dataset. We also discuss how a combination of these strategies can be embedded in a Stream-based application to detect fraud in real-time. From this perspective, our experiments lead to an average processing time of 111,702ms per transaction, while strategies to further improve the performance are discussed. Keywords: Fraud Detection, Stream Computing, Real-Time Analysis, Fraud, Data Mining, Retail Banking Industry, Data Preprocessing, Data Classification, Behavior-based Models, Supervised Analysis, Semi-supervised Analysis Sammanfattning Privatbankerna har drabbats hårt av bedrägerier de senaste åren. Bedragare har lyckats kringgå forskning och tillgängliga system och lura bankerna och deras kunder. Därför vill vi införa en ny, polyvalent...
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...1 KHYBER PAKHTUNKHWA PUBLIC SERVICE COMMISSION SYLLABUS FOR PROVINCIAL MANAGEMENT SERVICE COMPETITIVE EXAMINATION The Syllabus and standard for the Competitive Examination for the Provincial Management Service shall be as under : 1. The Examination shall include compulsory and optional subjects, and every candidate shall take all the compulsory subjects and opt for three of the optional subjects carrying 600 marks in all but not more than 200 marks from a single group. 2. A candidate shall answer the language papers in the language concerned. The question paper in Islamiat is to be answered in Urdu or English. All other papers must be answered in English. Violation of this instruction shall incur cancellation of the concerned paper(s) and consequently award of Zero. 3. The compulsory and optional subjects and maximum marks fixed for each subject shall be as below: Sr. No. 1 2 3 COMPULSORY SUBJECTS Subjects English (Précis & Composition) English Essay General Knowledge (a) Current Affairs 100 (b) Every Day Science 100 (c) Pakistan Affairs 100 Islamiat Viva Voce Total Maximum Marks 100 100 300 100 300 900 600 120 4 5 Qualifying marks in the aggregate of written papers: Qualifying marks in the Viva Voce: The non-Muslim candidates will have the option to take Islamiat as a compulsory subject or otherwise Pakistan Affairs (General Knowledge PaperIII) will be treated of 200 marks and counted in lieu of Islamiat. A candidate who fails to appear in any of the compulsory...
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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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...Natural Computing Series Series Editors: G. Rozenberg Th. Bäck A.E. Eiben J.N. Kok H.P. Spaink Leiden Center for Natural Computing Advisory Board: S. Amari G. Brassard K.A. De Jong C.C.A.M. Gielen T. Head L. Kari L. Landweber T. Martinetz Z. Michalewicz M.C. Mozer E. Oja G. P˘ un J. Reif H. Rubin A. Salomaa M. Schoenauer H.-P. Schwefel C. Torras a D. Whitley E. Winfree J.M. Zurada For further volumes: www.springer.com/series/4190 Franz Rothlauf Design of Modern Heuristics Principles and Application Prof. Dr. Franz Rothlauf Chair of Information Systems and Business Administration Johannes Gutenberg Universität Mainz Gutenberg School of Management and Economics Jakob-Welder-Weg 9 55099 Mainz Germany rothlauf@uni-mainz.de Series Editors G. Rozenberg (Managing Editor) rozenber@liacs.nl Th. Bäck, J.N. Kok, H.P. Spaink Leiden Center for Natural Computing Leiden University Niels Bohrweg 1 2333 CA Leiden, The Netherlands A.E. Eiben Vrije Universiteit Amsterdam The Netherlands ISSN 1619-7127 Natural Computing Series ISBN 978-3-540-72961-7 e-ISBN 978-3-540-72962-4 DOI 10.1007/978-3-540-72962-4 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011934137 ACM Computing Classification (1998): I.2.8, G.1.6, H.4.2 © Springer-Verlag Berlin Heidelberg 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations...
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