neutral applications

1. SAMBHRAM INSTITUTE OF TECHNOLOGY, BENGALURU
Department of Electronics & Communication Engineering
SEMINAR Presentation
on
ADVANCED APPLICATION OF
ARTIFICIAL INTELLIGENCE AND
NEURAL NETWORKS
Presentation by
RAJARAJESHWARI K DIVATE (1ST13EC727)
VIII Semester B.E.
Seminar coordinator Class Coordinator
Dr. C.V. Ravi Shankar S. Sowndeswari
HOD, Dept of ECE, SaIT Asst.Prof. Dept. ECE, SaIT

2. Introduction to AI
 The ability to acquire and apply
knowledge and skills is called
intelligence.
 This phenomenon is exhibited by
human brain.
 Artificial intelligence(AI) is
intelligence exhibited by machines.
 It is the theory and development of
computer systems able to perform
tasks normally requiring human
intelligence .
 Such as visual reception , speech
recognition, decision making, and
translation between languages.

3. Biological neural network
 The basic computational unit in the
nervous systyem is the nerve cell or
neuron.
 A neuron has:
dendrites (inputs)
cell body
axon (output)
 The human brain contains about 10
billion neurons.
 On average , each neuron is connected
to other neurons through about 10000
synapses.
Figure : Structure of a neuron

4. Artificial neural network
 ANN is a computer system
modeled on the human brain and
nervous system.
 An ANN is an interconnected
group of nodes, akin to vast
network of neurons in a brain.
 Each circular node in the figure
represents an artificial neuron
and an arrow represents a
connection from the output of
one neuron to the input of
another. Figure: Artificial neuron

5. Terminology
Biological terminology ANN terminology
Neuron Node/unit cell/neurode
Synapse Connection/edge/link
Synaptic efficiency Connection strength/weight
Firing frequency Node output

6. Types of neural network
 Fixed networks
In which the weights cannot be changed, that is
dW/dt=0. In such networks, the weights are fixed a
priori according to the problem to solve.
 Adaptive networks
Which are able to change their weights, that is
dW/dt not=0.

7. ANN Overview: computational model
for artificial neuron

8. ANN Overview:
Network architecture

10. Hopfield network
 A hopfield network is a form of
recurrent ANN invented by john
Hopfield in 1982.
 It can be seen as a fully connected
single layer auto associative
network.
 Hopfield nets serve as content
addressable memory systems with
binary threshold nodes.
Hopfield net

11.  Hopfield networks are constructed from artificial
neurons.
 These artificial neuron have N inputs. With each input
i there is a weight wi associated.
 They have an output. The state of the output is
maintained, until the neuron is updated.

12.  A HNN consists of a set of
neurons where each neuron
corresponds to a pixel of the
different image and is connected
to all the neurons in the
neighbourhood.
 The output of the neuron is
feedback to each of the other
neurons in the network.
 The number of feedback loops is
equal to the number of neurons.
 There is no self feedback loop.

13.  A recurrent network with all nodes connected to all
other nodes.
 Nodes have binary outputs (either 0,1 or 1,-1)
 Weights between the nodes are symmetric.
 No connection from node to itself is allowed.
 Nodes are updated asynchronously (the nodes are
selected at random)
 The network has no hidden layers or nodes.

14. Energy
 Hopfield defined the energy function of the network
by using the network architecture, i.e.,
 the number of neurons
 their output functions
 threshold values
 connection between neurons
 The strength of the connections
 Thus the energy function represents the complete
status of the network.

15. Contd…
 Hopfield has also shown that at each iteration of the processing
of the network, the energy value decreases and the network
reaches a stable state when its energy value reaches a
minimum.
 The energy function E of the discrete model is given by
Where i,j,k are the variables
W is the weight
V is the output the neuron
I is external input bias

16. Features of HNN
 HNN can perform the functions of memory recall in a
manner analogous to the way the brain functions.
 In addition, pattern recognition, solving linear
programming problems and solving combinatorial
optimization problems(COPs).
 Simple technical implementation using electronic or
optical device.

17. Applications of ANN
 ANN has been successfully applied to broad spectrum
of data-intensive applications like financial, data
mining, operational analysis, industrial, sales and
marketing.
 One such important application is in the field of
medical science. Such as
 Medical diagnosis
 Detection and evaluation of medical phenomena
 Patient’s length of stay forecasts
 Treatment cost estimation

18. Lung cancer detection using ANN
 The early detection of the lung cancer is a challenging
problem, due to the structure of the cancer cells.
 The manual analysis of the sputum sample is time
consuming, inaccurate and requires intensive trained
person to avoid diagnostic errors.
 There aremany techniques to diagnosis lung cancer,
such as chest radiograph(x-ray), computed
tomography(CT), magnetic resonance imaging(MRI
scan) and sputum cytology.
 However, most of these techniques are expensive and
time consuming.

19.  Most of these techniques are detecting the lung cancer
in its advanced stages, where the patient’s chance of
survival is very low.
 Hence, Hopfield neural network segmentation
method is used for segmenting sputum colour images
to detect the lung cancer in early stages.
 The segmentation results will be used as a base for a
Computer Aided Diagnosis ( CAD)system for early
detection of lung cancer.
 This method is designed to classify the image of N
pixels.

20. The HNN segmentation algorithm
1. Initialize the input of neurons to random values.
2. Apply the input- output relation given by
to obtain the new output value for each neuron,
establishing the assignment of pixel to classes.

21. 3. Compute the centroid for each class as follows:
4. Solve the set of differential equation
to update the input of each neuron:
5. Repeat from step 2 until convergence then terminate

22. Conclusion
 The HNN algorithm is applied with the specification
mentioned above to one thousand sputum colour
images and maintained the result for further
processing in the following steps.
 This algorithm could segment 97% of the images
successfully in nuclei, cytoplasm regions and clear
background
 Furthermore, HNN took short time to achive the
desired results.
 By experiment, HNN needed less than 120 iterations to
reach the desired segmentation result in 36 seconds.

23. THANK YOU