Machine Learning and Compression Systems in Communications and Healthcare

Communication Systems

Compression

Mobile Networks

  • end to end network slicing
  • IoT edge computing
  • RAN feature extraction
  • Caching for MEC
  • Handover optimization
  • Traffic classification
  • many many more.

Healthcare

  • standardized input data sets
  • confirmation after diagnosis and treatment for each patient
  • both public training and private test sets
  • metrics for comparison
  • allowing the algorithms to compete for accuracy with public results

Conclusions From Wiegard

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