© SEMINAR ON ARTIFICIAL NEURAL NETWORK AND ITS APPLICATIONS By Mr. Susant Kumar Behera Mrs. I. Vijaya

© ARTIFICIAL NEURAL NETWORKS DEVELOPED BY: Warren McCulloch & Walter Pits.

© A Tribute To Mr.Frank Rosenblatt Father of Artificial Neuron Networking

© INTRODUCTION There is no known algorithm for predicting solvent accessibility or coordination number. Many different approaches were tried, and most of them utilized the concept of neural networks. We shall discuss what these networks are, how do they work, and how we use them for our cause.

© ARTIFICIAL NEURAL NETWORK Attempts to mimic the actions of the neural networks of the human body Let’s first look at how a biological neural network works –A neuron is a single cell that conducts a chemically-based electronic signal –At any point in time a neuron is in either an excited or inhibited state

© STRUCTURE OF A NEURON –A series of connected neurons forms a pathway –A series of excited neurons creates a strong pathway –A biological neuron has multiple input tentacles called dendrites and one primary output tentacle called an axon –The gap between an axon and a dendrite is called a synapse

© Electron Micrograph of a Real Neuron

© NEURAL NETWORKING IN A BIOLOGICAL CELL

© ARTIFICIAL NEURAL NETWORKS Each processing element in an artificial neural net is analogous to a biological neuron –An element accepts a certain number of input values and produces a single output value of either 0 or 1 –Associated with each input value is a numeric weight

© FEATURES OF ANN NNs attempt to model the way the brain is structured: –10 billion neurons that communicate via 60 trillion connections (synapses). –Parallel rather than sequential processing. NNs are composed of the following elements: –Neuron (soma) –Inputs (dendrites) –Outputs of Neurons (axons) –Weights (synapse)

© THE ACTIVITIES WITHIN A PROCESSING UNIT

© HOW ANN WORK? In the preceding figure, all of the zero th inputs to either the hidden our output layer are referred to as thresholds and are typically set to -1. The weights of a neural network can be any positive or negative value. The input values are multiplied by the weights that connect them to a particular neuron. Neurons take this weighted sum as input and use an activation function to compute the neurons output. The output of one neuron becomes the input to another neuron multiplied by a different subset of weights.

© TYPES OF NETWORK Multilayer Perceptron Radial Basis Function Kohonen Linear Hopfield Adaline/Madaline Probabilistic Neural Network (PNN) General Regression Neural Network (GRNN) and at least thirty others

© NEURAL NETWORKS USES Speech recognition Speech synthesis Image recognition Pattern recognition Stock market prediction Robot control and navigation

© Strengths of Artificial Neural NetworksNeural Networks Are Versatile  Neural Networks Can Produce Good Results in Complicated Domains  Neural Networks Can Handle Categorical and Continuous Data Types  Neural Networks Are Available in Many Off-the-Shelf Packages STRENGTHS OF NEURAL NETWORKING

©  All Inputs and Outputs Must Be Massaged to  Neural Networks Cannot Explain Results  Neural Networks May Converge on an Inferior Solution WEAKNESSES OF ARTIFICIAL NEURAL NETWORKS

© CONCLUSION  Neural network are very flexible and powerful.  If used sensibly they can produce some amazing results.  It has a very vast scope in this modern world.

© REFERENCES i. Neural Networks at Pacific Northwest National Laboratory. ii. Artificial Neural Networks in Medicine. iii. Electronic Noses for Telemedicine. keller.ccc95.abs.html iv. Pattern Recognition of Pathology Images.

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