ai and neural networks

APPLICATION

OF

NEURAL NETWORK

APPLICATION OF NEURAL NETWORKS 1

NIKHIL KANSARIUE-4345CSE 4th YEAR

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


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.


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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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.


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.


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


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.


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).


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.


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.


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.


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.


ai and neural networks

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