PROJECT HIGHLIGHTS

Long-term forecast for 6 months or more

High accuracy (over 85%)

Adjustability for any region of the world

Low resource using for neural network learning

SKILLS & TECHNOLOGIES

Using the most advanced technologies in programming neural networks and computer training based on past periods

Development
Design
Marketing
Testing
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Days of Work
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Prototypes Done

SOME DETAILS

  • Neural Network Model for Flu Sickness Rate prediction.
  • We propose new quantitative methods utilizing deep learning for flu sickness rate forecast.
  • The accuracy of predictions according to test results is 85%.
  • The neural network consists of a group of receptors, 4 hidden layers and one output neuron.
  • The group of receptors is supplied with a segment of historical flu sickness data.
  • Each hidden layer of a neural network consists of n-neurons; each following layer is completely connected to the next one.
  • The function of activating hidden blocks ensures non-linearity of the network.
  • The network was trained by using the Back Propagation (BP) algorithm.
  • The output layer consists of one output neuron which is producing an appropriate sickness rate.
  • The node of the output level has a sigmoidal activation function.