...mind the end goal to settle on information driven forecasts or choices, as opposed to taking after entirely static project guidelines. Machine learning is firmly identified with computational statistics; a specialty that goes for the configuration of calculation for executing factual techniques on computers. It has solid ties to numerical enhancement, which conveys techniques, hypothesis and application areas to the field. Machine learning is utilized in a scope of registering errands where outlining and programming unequivocal calculations is in feasible (Marshland, 2009). Concepts of machine learning 1. Bayesian networks A Bayesian network is a probabilistic graphical model that speaks to an arrangement of irregular variables and their restrictive independencies through a coordinated non-cyclic chart. For instance, a Bayesian system could speak to the probabilistic connections between forex market and political unrests. Given the instances of political unrests, the system can be utilized to figure the probabilities of the forex market dropping. Proficient calculations exist that perform surmising and learning. 2. Decision tree learning It uses a decision tree as a predictive model, which maps observations about an item to conclusions about the item's target value. 3. Association rule learning It is a method for discovering interesting relations between variables in large databases. 4. Artificial neural networks It is a learning algorithm inspired by structure...
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...– 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: Part...
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...The Research Paper Factory Join Search Browse Saved Papers Search over 100,000 Essays Home Page » Business and Management Forecasting In: Business and Management Forecasting Forecasting HSM/260 University of Phoenix 06/20/2013 Exercise 9.1 The following data represent total personnel expenses for the Palmdale Human Service Agency for past four fiscal years: 20X1 $5,250,000 20X2 $5,500,000 20X3 $6,000,000 20X4 $6,750,000 Forecast personnel expenses for fiscal year 20X5 using moving averages, weighted moving averages, exponential smoothing, and time series regression. For moving averages and weighted moving averages, use only the data for the past three fiscal years. For weighted moving averages, assign a value of 1 to the data for 20X2, a value of 2 to the data for 20X3, and a value of 3 to the data for 20X4. For exponential smoothing, assume that the last forecast for fiscal year 20X4 was $6,300,000. You decide on the alpha to be used for exponential smoothing. For time series regression, use the data for all four fiscal years. Which forecast will you use? Why Moving Averages Fiscal Year Expenses Please login to view the full essay... Essay's Statistics Submitted by: hapikampr Date shared: 08/07/2013 10:35 AM Words: 707 Pages: 3 20X2 $ 5,500,000 20X3 6,000,000 20X4 6,750,000 20X2X4 6...
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...performance of its financial markets, since they facilitate the financing of productive activity and hence national output and economic growth. In this research report, the key roles and function of the financial markets are highlighted with the thrust of the discussion on the core issue of how the market works; directly and indirectly. One of the most important factors for rapid economic development is the effective mobilization and allocation of scarce resources within an economy. These resources can be real or financial, but they are scarce and command a price. The establishment of effective and efficient channel for the mobilization and allocation of scarce financial resources is therefore essential. The financial market, comprising of the money and capital markets, occupies an important place in most economies of the world. The primary function of a financial market is to enable funds to be sufficiently allocated from the surplus units of the economy to the deficit units for productive investment. The greater the transmission efficiency is, the higher the rate of growth of the economy (Olowe, 1997). The money market trades only in securities or debt instruments maturing in less than twelve months, while in the capital market, longer term debts as well as equity instruments are traded. The complementarity between money market and capital market is necessary for a balanced development of the financial system. (NSE Fact Book 2010) The money market is a market for the mobilization...
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...and theorists continue to debate if computers will actually be able to think for themselves at one point (Patterson 7). The generally accepted theory is that computers do and will think more in the future. AI has grown rapidly in the last ten years chiefly because of the advances in computer architecture. The term artificial intelligence was actually coined in 1956 by a group of scientists having their first meeting on the topic (Patterson 6). Early attempts at AI were neural networks modeled after the ones in the human brain. Success was minimal at best because of the lack of computer technology needed to calculate such large equations. AI is achieved using a number of different methods. The more popular implementations comprise neural networks, chaos engineering, fuzzy logic, knowledge based systems, and expert systems. Using any one of the aforementioned design structures requires a specialized computer system. For example, Anderson Consulting applies a knowledge based system to commercial loan officers using multimedia (Hedburg 121). Their system requires a fast IBM desktop computer. Other systems may...
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...greatly benefit from using Windows Server 2012 to manage their network. Windows Server 2012 brings great updates to security and functionality over past editions of Windows. In this proposal, the major areas I will cover are Active Directory, Group Policy, DNS, File Services, Remote Services, and WSUS (Windows Server Update Services). These major roles have features that will make Shiv LLC Company’s infrastructure run smoothly. Active Directory Shiv LLC will have sites across a large geographic location. Because of this, a regional domain model should be used. Domains will be broken down into where users are working. For example, currently Shiv LLC has three locations, Los Angeles, Dallas, and Houston. This means there will be three domains, representing each of these cities. This strategy will make sure the network can be maintained by regional administrators, who will only worry about users in their area of the network. With this style of deployment, it can be difficult to decide what will be the forest root domain. The main staff for the company is in two cities, Dallas and Houston. This makes it difficult to make one of these sites the forest root domain. To make this decision neutral, a dedicated forest root domain can be suggested. This domain will be created only to function as the forest root. It will not contain and users, except for service administrator accounts for the forest root. It will not represent any geographical region of the network, and the other domains...
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...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 by organizations and how you can use them to help make informed decisions and improve business processes. opening case study Information Systems...
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...Finance & Banking, vol.1, no.3, 2011, 125-138 ISSN: 1792-6580 (print version), 1792-6599 (online) International Scientific Press, 2011 Stock Market Analysis in Practice: Is It Technical or Fundamental? Gil Cohen1, Andrey Kudryavtsev2 and Shlomit Hon-Snir3 Abstract Investors use varies tools in the investment process. Some use technical or fundamental analysis, or both in that process. The aim of the following survey research is first, to examine differences between professional portfolio managers to amateur investors in their approach towards technical and fundamental analysis. Second, we want to study the difference of use of fundamental and technical tools in the buying versus selling stocks. We used online survey in one of the leading business portals in addition to asking professional investors in a leading investment house in Israel. Our results show no significant difference between professional and non-professional investors in terms of how frequently they use fundamental and technical investment tools. Both groups of investors use more frequently fundamental tools than technical when they make buy/sell decisions. We also found that non-professional investors use more fundamental tools such as "analysts' recommendations" when they buy stocks and more technical tools such as "support and resistance lines" when they sell stocks. Moreover, our study Economics and Management Department, The Max Stern Academic...
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...automated, future-ori- ented analyses made possible by data mining move beyond the analyses of past events typically provided by history-oriented tools such as decision support systems. Data mining tools answer business questions that in the past were too time-consuming to pursue. Yet, it is the answers to these questions make customer relationship management possible. Various techniques exist among data mining software, each with their own advantages and challenges for different types of applications. A particular dichotomy exists between neural networks and chi-square automated interaction detection (CHAID). While differing approaches abound in the realm of data mining, the use of some type of data mining is necessary to accomplish the goals of today’s customer relationship management philosophy. 2002 Elsevier Science Ltd. All rights reserved. Keywords: Customer relationship management (CRM); Relationship marketing; Data mining; Neural net- works; Chi-square automated interaction...
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...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, a 20 percent share of the market, years of experience...
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...Contents Introduction------------------------------------------------------------------------------------------------------2 Part A: Woolworths Ltd Strategic Analysis---------------------------------------------------------------2 SWOT analysis for Woolworths Ltd------------------------------------------------------------------------3 Key Capability Analysis---------------------------------------------------------------------------------------4 Major stakeholder analysis for Woolworths Ltd---------------------------------------------------------5 Assessment of Business Level Strategy---------------------------------------------------------------------6 PART B: Woolworths Ltd Strategic Review--------------------------------------------------------------6 Analysis of Key Strategic Plan Elements-------------------------------------------------------------------6 Stakeholder implication assessment for growing the Multi-channel strategy----------------------7 Stakeholder implication assessment for growing the Cost consciousness strategy----------------8 Strategy Map---------------------------------------------------------------------------------------------------10 Balanced Scorecard-------------------------------------------------------------------------------------------11 Conclusion-------------------------------------------------------------------------------------------------------12 Appendix-------------------------------------------...
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...many studies that have tested alternative methods in order to determine which ones are most effective. We review this evidence in order to provide guidelines for forecasting for marketing. The coverage includes intentions, Delphi, role playing, conjoint analysis, judgmental bootstrapping, analogies, extrapolation, rule-based forecasting, expert systems, and econometric methods. We discuss research about which methods are most appropriate to forecast market size, actions of decision makers, market share, sales, and financial outcomes. In general, there is a need for statistical methods that incorporate the manager's domain knowledge. This includes rule-based forecasting, expert systems, and econometric methods. We describe how to choose a forecasting method and provide guidelines for the effective use of forecasts including such procedures as scenarios. INTRODUCTION Forecasting has long been important to marketing practitioners. For example, Dalrymple (1987), in his survey of 134 U.S. companies, found that 99 percent prepared formal forecasts when they used formal marketing plans. In Dalrymple (1975), 93 percent of the companies sampled indicated that sales forecasting was one of the most critical' aspects, or a ‘very important’ aspect of their company's success. Jobber, Hooley and Sanderson (1985), in a survey of 353 marketing directors from British textile firms, found that sales forecasting was the most common of nine activities on which they reported. Managers' forecasting needs...
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...Chapter 01:An Introduction to Information Systems TRUE/FALSE 1. Information and data are essentially the same thing. ANS: F PTS: 1 REF: 5 2. Computers are required to organize or process data. ANS: F PTS: 1 REF: 7 3. Using a computer to forecast future sales and order more inventory before a shortage can occur is an example of information system feedback. ANS: T PTS: 1 REF: 12 4. A CBIS is a single set of hardware, software, databases, telecommunications, people, and procedures configured to collect, manipulate, store, and process data into information. ANS: T PTS: 1 REF: 12 5. The technology infrastructure is a set of shared IS resources that form the foundation of each computer-based information system. ANS: T PTS: 1 REF: 12 6. Today’s more advanced processor chips have the power of 1990s-era supercomputers. ANS: T PTS: 1 REF: 13 7. Teraflops is a measure of computer storage capacity. ANS: F PTS: 1 REF: 13 8. Application software such as Windows Vista and Windows Seven control basic computer operations such as start-up and printing. ANS: F PTS: 1 REF: 14 9. Software is needed for computers of all sizes from cell phones and small hand held devices to the largest supercomputers. ANS: T PTS: 1 REF: 14 10. Private cloud computing applications are available to everyone. ANS: F PTS: 1 REF: 15 11. Information about the documents on the Web and access to these documents are controlled and provided by tens of...
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...r. c o m / l o c a t e / d s s Detection of financial statement fraud and feature selection using data mining techniques P. Ravisankar a, V. Ravi a,⁎, G. Raghava Rao a, I. Bose b a b Institute for Development and Research in Banking Technology, Castle Hills Road #1, Masab Tank, Hyderabad 500 057, AP, India School of Business, The University of Hong Kong, Pokfulam Road, Hong Kong a r t i c l e i n f o a b s t r a c t Recently, high profile cases of financial statement fraud have been dominating the news. This paper uses data mining techniques such as Multilayer Feed Forward Neural Network (MLFF), Support Vector Machines (SVM), Genetic Programming (GP), Group Method of Data Handling (GMDH), Logistic Regression (LR), and Probabilistic Neural Network (PNN) to identify companies that resort to financial statement fraud. Each of these techniques is tested on a dataset involving 202 Chinese companies and compared with and without feature selection. PNN outperformed all the techniques without feature selection, and GP and PNN outperformed others with feature selection and with marginally equal accuracies. © 2010 Elsevier B.V. All rights reserved. Article history: Received 20 November 2009 Received in revised form 14 June 2010 Accepted 3 November 2010 Available online 12 November 2010 Keywords: Data mining Financial fraud detection Feature selection t-statistic Neural networks SVM GP 1. Introduction Financial fraud is a serious problem worldwide and more so in fast growing...
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...Exam Name___________________________________ MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) The six important business objectives of information technology are new products, services, and business models; customer and supplier intimacy; survival; competitive advantage; operational excellence; and: 1) _______ A) improved decision making. B) improved business practices. C) improved efficiency. D) improved flexibility. 2) Which of the following choices may lead to competitive advantage (1) new products, services, and business models; (2) charging less for superior products; (3) responding to customers in real time? 2) _______ A) 1 only B) 1 and 2 C) 2 and 3 D) 1, 2, and 3 3) An information system can be defined technically as a set of interrelated components that collect (or retrieve), process, store, and distribute information to support: 3) _______ A) decision making and control in an organization. B) the creation of new products and services. C) communications and data flow. D) managers analyzing the organization's raw data. 4) The three activities in an information system that produce the information organizations use to control operations are: 4) _______ A) information retrieval, research, and analysis. B) input, processing, and output. C) input, output, and feedback...
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