Artificial Neural Network Sphoorti Sood1and Divya Gupta2 1{Student of Computer Science Department SRMSWCET‚ Bareilly} sphoortisood@yahoo.in 2{Student of Computer Science Department SRMSWCET‚ Bareilly} divyagupta1309@gmail.com Abstract— Artificial neural networks (ANNs) are simplified models of human brain. These are networks of computing elements that have the ability to respond to input stimuli
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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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ARTIFICIAL NEURAL NETWORK: USE IN MANAGEMENT Neuron Transfer Function: The transfer function of a neuron is chosen to have a number of properties which either enhance or simplify the network containing the neuron. Crucially‚ for instance‚ any multilayer perceptron using a linear transfer function has an equivalent single-layer network; a non-linear function is therefore necessary to gain the advantages of a multi-layer network. Types of neuron transfer function • Pure Linear Transfer Function
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ARTIFICIAL NEURAL NETWORKS ABSTRACT: “Human Brain‚” the most intelligent device on the Earth‚ is the driving force the ever progressive nature of the Human Species. This is only the reason why _Homo sapien_ has built variety of intelligent devices like supercomputers which can do billions and trillions of calculations each second. But still Human is not able to build a device which can actually mimic its brain. This idea of imitating human brain gave a new area of research known as Artificial
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PAPER ON ARTIFICIAL NEURAL NETWORKS ABSTRACT The developments in Artificial Intelligence (AI) appear promising‚ but when applied to real world intelligent task such as in speech‚ vision and natural language processing‚ the AI techniques show their inadequacies and ‘brittleness’ in the sense that they become highly task specific. The computing models inspired by biological neural networks can provide new directions to solve problems arising in natural tasks.
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process of attempting to identify instances of network attacks by comparing current activity against the expected actions of an intruder. Most current approaches to misuse detection involve the use of rule-based expert systems to identify indications of known attacks. However‚ these techniques are less successful in identifying attacks which vary from expected patterns. Artificial neural networks provide the potential to identify and classify network activity based on limited‚ incomplete‚ and nonlinear
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H.O.D. Electrical Engg. Dept. APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN VOLTAGE AND REACTIVE POWER CONTROL Abstract The aim of this seminar is to introduce a new technique to control the voltage and reactive power in power systems based on Artificial Neural Network (ANN). Feed-forward ANN with Back Propagation training algorithm is used and the training data is obtained by solving several abnormal conditions
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we want to create group that have maximum similarity among members within each group… 5. Which of the following is the reason why neural networks have been applied in business classification problems? Able to learn the data‚ able to learn the models ’ nonparametric nature‚ its ability to generalize‚ All of the above 6. The main processing elements of a neural network are individual neurons 7. A software suite is created when several software products are integrated into system 8. One dose not
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BSc and Diploma in Computing and Related Subjects Neural networks A. Vella and C. Vella 2009 2910311 The material in this subject guide was prepared for the University of London External System by: Dr Alfred D. Vella‚ BSc‚ PhD‚ MSc‚ BA‚ FIMA‚ MBCS‚ CEng‚ CMath and Carol A. Vella‚ BSc‚ MSc‚ BA‚ PGCert. T&L in HE‚ PGDip. T&L in HE. This is one of a series of subject guides published by the University. We regret that due to pressure of work the authors are unable to enter into any
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REAL TIME POWER SYSTEM SECURITY ASSESSMENT USING ARTIFICIAL NEURAL NETWORKS REAL TIME POWER SYSTEM SECURITY ASSESSMENT USING ARTIFICIAL NEURAL NETWORKS Abstract Contingency analysis of a power system is a major activity in power system planning and operation. In general an outage of one transmission line or transformer may lead to over loads in other branches and/or sudden system voltage rise or drop. The traditional approach of security analysis known as exhaustive security analysis involving
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