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Time Series Analysis: Accelerometer Sensors of Object Inclination and Vibration. Time Series Analysis by using different (State of Art Models) Machine and Deep Learning. Recurent Neural Network with CuDNNLSTM Model, Convolutional Autoencoder, Residual Network (ResNet) and MobileNet Model.

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aaaastark/Accelerometer-Sensors-Analysis

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Time Series Analysis: Accelerometer Sensors of Object Inclination and Vibration

Agenda: Time Series Analysis by using different (State of Art Models) Machine and Deep Learning.

  • Recurent Neural Network with CuDNNLSTM Model
  • Convolutional Autoencoder
  • Residual Network (ResNet) and MobileNet Model

Workflow that is used in this Project

  • Data Processing/Transformation
  • Data Normalization
  • Image Transformation: Markov Transition Field
  • State of Art Models (Machine and Deep Learning)
  • Visulization of Train and Test Models: Accuracy and Loss
  • Classification Report, Accuracy, and Loss

APIs that are used in this Project

  • tensorflow
  • sklearn
  • keras
  • matplotlib
  • numpy
  • pandas
  • pyts (Python Time Series Classification)

Time Series Dataset: Accelerometer Sensors

Image Transformation: Markov Transition Field

Visulization of Train and Test Models: Accuracy and Loss

Classification Report

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Time Series Analysis: Accelerometer Sensors of Object Inclination and Vibration. Time Series Analysis by using different (State of Art Models) Machine and Deep Learning. Recurent Neural Network with CuDNNLSTM Model, Convolutional Autoencoder, Residual Network (ResNet) and MobileNet Model.

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