ARTIFICIAL NEURAL NETWORKS AND THEIR APPLICATIONS IN BUSINESS Ankit Chauhan Ist Semester – MBA (GEN) University School of Management Guru Gobind Singh Indraprastha University Abstract- This report is an introduction to Artificial Neural Networks. The various types of neural networks are explained and demonstrated‚ applications of neural networks like ANNs in business and organizations are described‚ and a detailed historical background is provided. The connection between the artificial
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Introductory Network Concepts‚ Network Standards‚ and the OSI Model 1. There are many reasons for a company to network its computers‚ some are as follows; Share software‚ information with others on networks‚ cheaper than buying individual software and hardware for each standalone especially if the software often offers deals for amount being purchased‚ e-mail between network users‚ and flexible access from any computer on the network. 2. Two fundamental network models are peer-to-peer(P2P)
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The National Center of Excellence Federal Aviation Administration For Aviation Operations Research 2nd National Airspace System Infrastructure Management Conference NAS Infrastructure in Transition June 13‚ 2006 University of California Washington Center Washington‚ DC 115 West Avenue • Jenkintown‚ PA 19046 • USA ℡ 215-884-7500 • 215-884-1385 richg@gra-inc.com Richard Golaszewski GRA‚ Incorporated Acknowledgement The following presentation includes work performed by a number of organizations
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and uses of different media CHAPTER 8 Network Media Types Network media is the actual path over which an electrical signal travels as it moves from one component to another. This chapter describes the common types of network media‚ including twisted-pair cable‚ coaxial cable‚ fiber-optic cable‚ and wireless. Twisted-Pair Cable Twisted-pair cable is a type of cabling that is used for telephone communications and most modern Ethernet networks. A pair of wires forms a circuit that can transmit
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IS3120 Network Communications Infrastructure Unit 10 Network Management—FCAPS © ITT Educational Services‚ Inc. All rights reserved. Learning Objective Apply network management and security techniques using the FCAPS process. IS3120 Network Communications Infrastructure © ITT Educational Services‚ Inc. All rights reserved.Page 2 Key Concepts Fault management—SNMP alarms Configuration management—change control board and procedures Administration management—asset and inventory management
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Kirchhoff’s Law Kirchhoff’s current law (KCL) imposes constraints on the currents in the branches that are attached to each node of a circuit. In simplest terms‚ KCL states that the sum of the currents that are entering a given node must equal the sum of the currents that are leaving the node. Thus‚ the set of currents in branches attached to a given node can be partitioned into two groups whose orientation is away from (into) the node. The two groups must contain the same net current. In general
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nowadays. It does not matter what kind of the profession is‚ social networks is used daily by people around the world. Even though today social networking is very famous among teenagers‚ it is also has bad effect on teenagers. Social networking can affect the mental of teenagers. It gives influence to teenagers. For example‚ many couples have met each other through Facebook‚ fell in love and also broke up through this social network. Things are negatively affect relationship and real relationships
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wireless sensor networks can be promptly understood simply by thinking about what they essentially are: a large number of small sensing self-powered nodes which gather information or detect special events and communicate in a wireless fashion‚ with the end goal of handing their processed data to a base station. Sensing‚ processing and communication are three key elements whose combination in one tiny device gives rise to a vast number of applications [A1]‚ [A2]. Sensor networks provide endless
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Chapter 1 1. INTRODUCTION Artificial Neural Networks are being touted as the wave of the future in computing. They are indeed self learning mechanisms which don ’t require the traditional skills of a programmer. But unfortunately‚ misconceptions have arisen. Writers have hyped that these neuron-inspired processors can do almost anything. Fig. 1.1 Neural Network These exaggerations have created disappointments for some potential users who
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| | | |Network Project Strategy and Planning Document | |
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