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Method and system for recommending features for developing an iot application

  • US 10,277,682 B2
  • Filed: 11/20/2017
  • Issued: 04/30/2019
  • Est. Priority Date: 11/22/2016
  • Status: Active Grant
First Claim
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1. A method for recommending a set of features for developing an Internet of Things (IoT) analytics application, the method comprising a processor implemented steps of:

  • providing an input signal received from a sensor to the processor;

    applying a four level discrete wavelet transform by selecting a suitable mother wavelet on the input signal to generate a time frequency domain (TFD) set of features;

    applying a short term Fourier transform on the input signal to generate a frequency domain (FD) set of features;

    generating a time domain (TD) set of features from the input signal, wherein the TFD set of features, the FD set of features and the TD set of features are an initial set of features;

    applying a first feature selection algorithm and a second feature selection algorithm to the time domain, the frequency domain and the time-frequency domain set of features, wherein the application of selection algorithms result in selection of a first set of features for the time domain, the frequency domain and the time-frequency domain;

    taking a union of the first set of features for TD, FD and TFD recommended by the first selection algorithm and the second selection algorithm;

    generating a combination of the first set of features using an exhaustive search, wherein the combination of the first set of features is lesser than the initial set of features;

    applying a classification algorithm including a support vector machine learning method using a different set of kernels on the combination of the first set of features; and

    recommending the first set of features as the set of features for developing the IoT analytics application if a predefined condition is satisfied.

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