The method of claim 5, wherein features generation techniques are based on unsupervised learning techniques that include deep learning and signal processing techniques (feature space exploration) information theoretic, and statistical features along with features of pre-trained deep recurrent neural network.ħ. The method of claim 1, wherein the plurality of hybrid learning techniques are generated based on feature generation, feature recommendation and classification techniques.Ħ. The method of claim 1, wherein the plurality of learning techniques are chosen using a rule based engine and domain constraints, wherein the domain constraints include business requirements and computational constraints.ĥ. The method of claim 1, wherein the plurality of sensors are from diverse application domains including exemplary domains physiological time series sensor signals that include Electrocardiography (ECG), electroencephalography (EEG), motion sensors that include accelerometer, gyro meter, magnetometer, temperature sensors.Ĥ. The method of claim 1, wherein hybrid learning techniques refers to learning techniques that are a hybrid combination a plurality of techniques that include of deep learning, machine learning and signal processing.ģ. ![]() A processor-implemented method for generating a hybrid learning technique for sensor signal analytics, the method comprising: receiving sensor signals as an input, wherein the sensor signals are captured using a plurality of different sensors processing the received the sensor signals for noise removal choosing a plurality of learning techniques for the processed sensor signal based on a plurality of domain constraints generating a plurality of hybrid learning techniques from the plurality of learning techniques generating a performance matrix individually for each of the plurality of hybrid learning techniques based on optimization techniques predicting the hybrid learning technique from the generated plurality of hybrid learning techniques based on the performance matrix and generating an unique feature representation for the predicted hybrid learning techniques.Ģ.
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