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Advanced applications of artificial intelligence and neural networks

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Advanced applications of artificial intelligence and neural networks

  1. 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. 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. 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. 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. 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. 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. 7. ANN Overview: computational model for artificial neuron
  8. 8. ANN Overview: Network architecture
  9. 9. 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
  10. 10.  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.
  11. 11.  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.
  12. 12.  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.
  13. 13. 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.
  14. 14. 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
  15. 15. 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.
  16. 16. 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
  17. 17. 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.
  18. 18.  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.
  19. 19. 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.
  20. 20. 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
  21. 21. 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.
  22. 22. THANK YOU