advanced applications of artificial intelligence and neural networks

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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

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Page 1: Advanced applications of artificial intelligence and neural networks

SAMBHRAM INSTITUTE OF TECHNOLOGY, BENGALURU

Department of Electronics & Communication EngineeringSEMINAR Presentation

on

ADVANCED APPLICATION OF ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKS

Presentation by

RAJARAJESHWARI K DIVATE (1ST13EC727)VIII Semester B.E.

Seminar coordinator Class CoordinatorDr. C.V. Ravi Shankar S. SowndeswariHOD, Dept of ECE, SaIT Asst.Prof. Dept. ECE, SaIT

Page 2: Advanced applications of artificial intelligence and neural networks

Introduction to AIThe 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.

Page 3: Advanced applications of artificial intelligence and neural networks

Biological neural networkThe 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

Page 4: Advanced applications of artificial intelligence and neural networks

Artificial neural networkANN 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

Page 5: Advanced applications of artificial intelligence and neural networks

Terminology Biological terminology ANN terminology

Neuron Node/unit cell/neurode

Synapse Connection/edge/link

Synaptic efficiency Connection strength/weight

Firing frequency Node output

Page 6: Advanced applications of artificial intelligence and neural networks

Types of neural networkFixed 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.

Page 7: Advanced applications of artificial intelligence and neural networks

ANN Overview: computational model for artificial neuron

Page 8: Advanced applications of artificial intelligence and neural networks

ANN Overview: Network architecture

Page 9: Advanced applications of artificial intelligence and neural networks
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Hopfield networkA 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

Page 11: Advanced applications of artificial intelligence and neural networks

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.

Page 12: Advanced applications of artificial intelligence and neural networks

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.

Page 13: Advanced applications of artificial intelligence and neural networks

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.

Page 14: Advanced applications of artificial intelligence and neural networks

Energy Hopfield defined the energy function of the

network by using the network architecture, i.e., the number of neuronstheir output functionsthreshold values connection between neuronsThe strength of the connections

Thus the energy function represents the complete status of the network.

Page 15: Advanced applications of artificial intelligence and neural networks

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

Page 16: Advanced applications of artificial intelligence and neural networks

Features of HNNHNN 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.

Page 17: Advanced applications of artificial intelligence and neural networks

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 diagnosisDetection and evaluation of medical

phenomenaPatient’s length of stay forecastsTreatment cost estimation

Page 18: Advanced applications of artificial intelligence and neural networks

Lung cancer detection using ANNThe 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.

Page 19: Advanced applications of artificial intelligence and neural networks

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.

Page 20: Advanced applications of artificial intelligence and neural networks

The HNN segmentation algorithm1. 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.

Page 21: Advanced applications of artificial intelligence and neural networks

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

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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.

Page 23: Advanced applications of artificial intelligence and neural networks

THANK YOU