Neural

NEURAL NETWORK &
THEIR APPLICATIONS
BY DAKSHIMA SHARMA
COMPUTER SCIENCE
ENGINEERING
3RD YEAR

INTRODUCTION
• Models of the brain and nervous system
• Process information much more like the brain than a serial computer
• Very simple principles and complex behaviours.
• An Artificial Neural Network (ANN) is an information processing paradigm that
is inspired by biological nervous systems.
• It is composed of a large number of highly interconnected processing elements
called neurons.
• An ANN is configured for a specific application, such as pattern recognition or
data classification

NEURAL SYSTEM
BIOLOGICAL ARTIFICIAL
• They are made up of real biological • They are composed of interconnecting
neurons that are connected or functionally artificial neurons (programming
related in a nervous system . constructs that mimic the properties
• In the field of neuroscience, they are often of biological neurons) for solving
identified as groups of neurons that artificial intelligence problems without
perform a specific physiological function creating model of real system.
in laboratory analysis.
• The algorithms abstract away the
biological complexity by focusing on the
most important information. The goal
of artificial neural networks human-
like, predictive ability.

WHY TO USE ANN???
• ability to derive meaning from complicated or imprecise data
• extract patterns and detect trends that are too complex to be
noticed by either humans or other computer techniques
• Adaptive learning
• Real Time Operation
• Conventional computers use an algorithmic approach, but neural
networks works similar to human brain and learns by example.

ARTIFICIAL NEURAL NETWORKS(ANN)-:
• Also called simulated neural network (SNN), is an interconnected group of natural or
artificial neurons that uses a mathematical or computational model for information
processing based on a connectionistic approach to computation.
• In most cases an ANN is an adaptive system that changes its structure based on
external or internal information that flows through the network.
• ANNs incorporate the two fundamental components of biological neural nets:
1. Neurones (nodes)
2. Synapses (weights)

Analogy between ANN and NNN
NODE V/S NEURON & WEIGHT V/S SYNAPSE

BASICS OF NEURAL SYSTEM
1 A set of synapses or connecting
links, each link characterized by a
weight:
W1, W2, …, Wm
2 An adder function (linear combiner)
which computes the weighted sum
of the inputs: m
u wjxj
j 1
3 Activation function (squashing
function) for limiting the amplitude
of the output of the neuron.
y (u b)

ARCHITECTURE OF NEURAL
SYSTEM
FEED FORWARD :
Neurons are arranged in acyclic layer Output
and this arrangement can be of: Input layer layer
of of
source nodes neuron
3-4-2 Network s

1)- Single layer
Input Output
layer layer

2)- Multilayer
Hidden Layer

FEED FORWARD ANN
• Information flow is unidirectional
▫ Data is presented to I nput layer
▫ Passed on to Hidden Layer
▫ Passed on to Output layer

• Information is distributed

• Information processing is parallel

RECURRENT ANN
▫ Nodes connect back to other nodes
or themselves z-1
▫ Information flow is multidirectional
▫ Sense of time and memory of
BLUE-input
previous state(s) z-1 BROWN-hidden
GREEN-output
unit delay operator z-1 implies
dynamic system
z-1

APPLICATIONS
FINGERPRINT RECOGNITION

Image edge Ridge Thinin Feature classifi
acquisiti detecti extractio g extracti cation
on on n on

• Image Acquisition: the acquired image is digitalized into 512x512
image with each pixel assigned a particular gray scale value
(raster image).
• Edge Detection and Thinning: these are preprocessing of the
image , remove noise and enhance the image.

FINGERPRINT RECOGNITION
SYSTEM
• Feature extraction: this the step
where we point out the features such
as ridge bifurcation and ridge endings
of the finger print with the help of
neural network.
• Classification: here a class label is
assigned to the image depending on the
extracted features.

PREPROCESSING SYSTEM
The first phase is to capture a image
The image is captured using TIR .
The image is stored as a two dimensional
array of 512x512 size, each element of
array representing a pixel and assigned a
gray scale value from 256 gray scale
levels.
Image is captured ,noise is removed using.
Edge detection: the edge is defined where
the gray scale levels changes greatly.
also, orientation of ridges is determined
for each 32x32 block of pixels using gray
scale gradient.
Ridge extraction: are extracted using the
fact that gray scale value of pixels are
maximum along the direction normal to the
ridge orientation.

PREPROCESSING SYSTEM
Thinning: the extracted ridges are converted into
skeletal structure in which ridges are only one
pixel wide. thinning should not-
 Remove isolated as well as surrounded
pixel.
 Break connectedness.
 Make the image shorter.
• Multilayer perceptron network of three layers is
trained to detect minutiae in the thinned image.
 The first layer has nine perceptrons
 The hidden layer has five perceptrons
 The output layer has one perceptron.
The network is trained to output ‘1’ when the
input window is centered at the minutiae and it
outputs ‘0’ when minutiae are not present.

FEATURE EXTRACTION

• Trained neural networks are used to
analyze the image by scanning the image
with a 3x3 window.
• To avoid falsely reported features which
are due to noise –
 The size of scanning window is
increased to 5x5
 If the minutiae are too close to
each other than we ignore all of
them.

FACE RECOGNITION

90% accurate learning head pose, and recognizing 1-of-20 faces

OTHER APPLICATIONS
Character Recognition - The idea of character recognition has become very
important as handheld devices like the Palm Pilot are becoming increasingly popular.
Neural networks can be used to recognize handwritten characters.

Image Compression - Neural networks can receive and process vast amounts of
information at once, making them useful in image compression. With the Internet
explosion and more sites using more images on their sites, using neural networks for
image compression is worth a look.

OTHER APPLICATIONS
Stock Market Prediction - The day-to-day business of the stock market is extremely
complicated. Many factors weigh in whether a given stock will go up or down on any
given day. Since neural networks can examine a lot of information quickly and sort it all
out, they can be used to predict stock prices.

Travelling Salesman Problem- Interestingly enough, neural networks can solve the
travelling salesman problem, but only to a certain degree of approximation.

Medicine, Electronic Nose, Security, and Loan Applications - These are some
applications that are in their proof-of-concept stage, with the acceptance of a neural
network that will decide whether or not to grant a loan, something that has already
been used more successfully than many humans.

Miscellaneous Applications - These are some very interesting (albeit at times a little
absurd) applications of neural networks.

SUMMARY
• Neural network solutions should be kept as simple as possible.
• For the sake of the gaming speed neural networks should be applied preferably off-
line.
• A large data set should be collected and it should be divided into
training, validation, and testing data.
• Neural networks fit as solutions of complex problems.
• A pool of candidate solutions should be generated, and the best candidate solution
should be selected using the validation data.
• The solution should be represented to allow fast application.

THE END..
DAKSHIMA SHARMA

SIGNING OFF…..

Neural

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