Artificial Neural Network, Machine Learning, Types of Neural Network, Report on Cardiovascular Systems

1. ARTIFICIAL NEURAL NETWORK
SUBMITTED BY:
GAURAV PANDEY

2. OUTLINE
ARTIFICIALNEURAL
NETWORK
03
01
04
02
ARTIFICIALNEURAL
NETWORK
07
05
08
06
WHAT IS ANN?
ARCHITECTURE
ALGORITHMS
APPLICATIONS
ADVANTAGES/
DISADVANTAGES
WHY USE ANN?
WORKING
PERCEPTRON

3. What is ANN ?
ANN stand for Artificial Neural
Network.
- an information processing paradigm
inspired by an Human nervous system .
- Composed of large number of highly
interconnected processing elements
(neurons).
- The ability to learn from experience in
order to improve their performance.
- Similar to biological system ANN also
involves synaptic connections between
neurons.

4. Why use ANN ?
- Knowledge acquisition under noise and
uncertainty.
- 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 and fault tolerance.
- Flexible Knowledge representation.
- Efficient knowledge processing.

5. The Architecture of Neural Network
Feed forward Neural Network
This neural network is one of the simplest for of ANN, where the data or
the input travels in one direction. The data passes through the input nodes
and exit on the output nodes.

6. The Architecture of Neural Network
Radial basis function Neural Network

7. The Architecture of Neural Network
Kohonen Self Organizing Neural Network
The objective of a Kohonen map is to input vectors
of arbitrary dimension to discrete map comprised of
neurons. The map needs to be trained to create its
own organization of the training data. It comprises
of either one or two dimensions.

8. The Architecture of Neural Network
Recurrent Neural Network
The Recurrent Neural
Network works on the
principle of saving the output
of a layer and feeding this
back to the input to help in
predicting the outcome of the
layer.

9. The Perceptron
The most basic form of an activation function is a simple binary function
that has only two possible results.
The Heaviside Step function. This function returns 1 if the input is
positive or zero, and 0 for any negative input. A neuron whose
activation function is a function like this is called a Perceptron.

10. WORKING OF NEURAL NETWORK.
INPUT
LAYER
HIDDEN
LAYER
OUTPUT
LAYER
INPUTS
OUTPUT

11. Neural Network Learning Algorithms
Supervised Learning Reinforced Learning Unsupervised Learning
Supervised learning is a
learning in which we teach
or train the machine using
data which is well labeled
that means some data is
already tagged with correct
answer. After that, machine
is provided with new set of
examples(data) so that
supervised learning
algorithm analyses the
training data(set of training
examples) and produces an
correct outcome from
labeled data.
Reinforcement learning is
a type of dynamic
programming that trains
algorithms using a system of
reward and punishment.
A reinforcement learning
algorithm, or agent, learns
by interacting with its
environment. The agent
receives rewards by
performing correctly and
penalties for performing
incorrectly. The agent learns
without intervention from a
human by maximizing its
reward and minimizing its
Unsupervised learning is
the training of machine
using information that is
neither classified nor
labeled and allowing the
algorithm to act on that
information without
guidance. Here the task of
machine is to group
unsorted information
according to similarities,
patterns and differences
without any prior training
of data.

12. APPLICATION OF NEURAL NETWORK
Pattern recognition.
Medical Field.
Control system and monitoring.
Forecasting – sales, market research, meteorology.
Marketing and financial application.
Robotics, Facial animation , Event prediction.
Signal Filtering (reduction of radio noise).
Adaptive Control (vehicle Guidance).

13. ADVANTAGES
A neural network can perform a task that a linear program
cannot.
When an element of the neural network fails, it can continue
without any problem by their parallel nature.
A neural network learns and does not need to be
reprogrammed.
It can be implemented in any applications.
They can perform associative memory (associative - like working
memory in humans) as opposed to addressable memory (typical
for classical computers).
They do not require knowledge of the algorithm solving the
problem (automatic learning).

14. DIS-ADVANTAGES
The neural network needs training to operate.
 The architecture of a neural network is different from the
architecture of microprocessors therefore needs to be
emulated.
 Requires high processing time for large neural networks.
: There are many parameters to be set in a neural network
and optimizing the network can be challenging, especially to
avoid overtraining.
Require high processing time for large neural networks.

15. Modeling and Diagnosing the Cardiovascular
System
Neural Networks are used experimentally to model the human cardiovascular
system.
Diagnosis can be achieved by building a model of the cardiovascular system of
an individual and comparing it with the real time physiological measurements
taken from the patient.
If this routine is carried out regularly, potential harmful medical conditions can
be detected at an early stage and thus make the process of combating the
disease much easier.
A model of an individual's cardiovascular system must mimic the relationship
among physiological variables (i.e., heart rate, systolic and diastolic blood
pressures, and breathing rate) at different physical activity levels.
If a model is adapted to an individual, then it becomes a model of the physical
condition of that individual. The simulator will have to be able to adapt to the
features of any individual without the supervision of an expert.

16. CONCLUSION
A Neural Network can be successfully used in the
project, given that we have access to enough training
data.
Since we need it to be highly accurate, we need to
carefully gather contextual variables that closely relate to
diagnosing heart murmur and its various grades.

17. REFERENCES
• Siewnicka A., Janiszowski K., Gawlikowski M. (2014) “A Physical Model of the
Human Circulatory System for the Modeling, Control and Diagnostic of
Cardiac Support Processes”. In: Březina T., Jabloński R. (eds) Mechatronics
2013. Springer, Cham, 978-3-319-02294-9_104.
• Ferrari, G., Kozarski, M., De Lazzari, C., Gorczynska, K., Mimmo, R., Guaragno,
M., Darowski, M.: “Modelling of cardiovascular system: development of a
hybrid (numerical-physical) model”. International Journal of Artificial
Organs 26(12), 1104–1114 (2003).
• The Perceptron https://appliedgo.net/perceptron/accessed on 21/04/2018 at
22:00
• Types of neural network https://analyticsindiamag.com/6-types-of-artificial-
neural-networks-currently-being-used-in-todays-technology/ accessed on
21/04/2018 at 22:10
• Artificial Neural Network https://data-flair.training/artificial-neural-network-
applications/ accessed on 22/04/2018 at 20:50

18. THANK YOU!!