This document discusses various applications of neural networks, including pattern recognition, autonomous vehicles, medicine, sports prediction, and virus detection. Some key applications mentioned are using neural networks for patient diagnosis, detecting coronary artery disease from medical images, predicting sports outcomes based on team statistics, and forecasting space weather events. The document also notes some limitations of neural networks, such as requiring large datasets and not providing explanations for decisions.

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NIKHIL KANSARI
UE-4345
CSE 4th
YEAR

2.  Pattern Recognition
 Autonomous Walker & Swimming Eel
 Neural Networks in Medicine
 Neural Networks in Sports
 Forecasting space weather
 Neural networks for computer virus recognition
 Limitations of neural networks
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3.  An important application of neural networks
 can be implemented by using a feed-forward
neural network that has been trained accordingly.
 the network is trained to associate outputs with
input patterns.
 it identifies the input pattern and tries to output the
associated output pattern.
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4.  The power of neural networks comes to life when
a pattern that has no output associated with it, is
given as an input.
 In this case, the network gives the output that
corresponds to a taught input pattern that is least
different from the given pattern
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8.  combining biology, mechanical engineering
and information technology in order to
develop the techniques necessary to build a
dynamically stable legged vehicle
controlled by a neural network.
 This would incorporate command signals, sensory
feedback and reflex circuitry in order to produce
the desired movement.
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9.  particularly well suited to problems with a high
degree of complexity for which there is no
algorithmic solution or the solution is too complex
for traditional techniques to determine.
 drug development, patient diagnosis, and image
analysis , detection of coronary artery disease and
the processing of EEG signals.
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10.  Single Photon Emission Computed Tomography
(SPECT), operates by collecting a series of two-
dimensional scintigraphic images from around the
body.
 In each image, a pixel's value is the count of the
number of photons that were recorded by the gamma
camera in that spot.
 A 3-D model of the chest is created from these
images, and this model is subjected to an algorithm
which produces a two dimensional polar plot of the
regions of the heart
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11.  Networks have been deployed in practice for pre-
screening of patients and deciding those who
need more detailed examinations.
 networks have been found to have equal or better
accuracy and faster convergence than traditional
probabilistic and statistical techniques.
 neural networks to analyze the data obtained from
this process with the goal of improving diagnosis.
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12.  effective at predicting the outcomes of sports
events due to they have strong pattern matching
capabilities .
 A neural network is a computerized system that
can learn which combinations of inputs (such as a
team’s performance statistics) lead to a particular
output (such as the probability of the team
winning).
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13.  predicting the outcome of thoroughbred horse
races.
 providing a neural network with historical
information on horses -speed, horse position
during previous races, class, earnings, in-the-
money percentages, and postposition in today's
and previous races .
 network can use its advanced pattern matching
capabilities to predict the outcome of future races.
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14.  system to predict the arrival of interplanetary (IP)
shocks at the Earth .
 detected by the Electron, Proton, and Alpha Monitor
(EPAM) instrument aboard NASA .
 Using EPAM data, we trained an artificial neural
network to predict the time remaining
until the shock arrival.
After training this algorithm
on 37 events, it was able to
forecast the arrival time for
19 previously unseen events.
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15.  for generic detection of a particular class of computer
viruses-the so called boot sector viruses.
 as part of the IBM Antivirus software package .
 designing an appropriate input representation scheme;
dealing with the scarcity of available training data;
finding an appropriate trade off point between false
positives and false negatives to conform to user
expectations; and making the software conform to
strict constraints on memory and speed of
computation needed to run on PCs.
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16.  Neural network learning algorithm are inductive,
requiring large amount of data, whereas strategic
decision making deals with unique and non
routine types of decision making.
 Neural networks do not provide explanations for
their decisions.
 Neural network decisions are not supported by
significant tests, hence low validity.
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