The proposed system provides a smart and energy efficient means of waste segregation.

Most of the driving maneuver classification methods follow supervised learning techniques and utilize ground truth in order to train classifiers. However, collecting ground truth is the most troublesome, expensive, and significant task of classification and effects a classifier’s performance. The work proposes an empirical framework for automatic labeling of timeseries data that can be further used in training phrases during semi-supervised learning. The proposed algorithm generates class labels and find that generated label of 4895 data matched with 11077 manual labeled data. The work analyzes the challenges involved in the driving time series data labeling. So, reasons behind mismatch of data label can also be explained.

The proposed work develops a Long Short Term Memory (LSTM) model for multi class classification of driving maneuver from sensor fusion time series dataset. The work also analyzes the significance of sensor fusion data change rule and utilized the idea with deep learning time series multi class classification of driving maneuver. We also proposed some hypotheses which are proven by the experimental results. The proposed model provides Train Accuracy: 99.98, Test Accuracy: 97.2021, Precision: 0.974848, Recall: 0.960154 and F1 score: 0.967028. The Mean Per Class Error (MPCE) is 0.01386. The significant rules can accelerate the feature extraction process of driving data. Moreover, it helps in automatic labeling of unlabeled dataset. Our future approach is to develop a tool for generating categorical label for unlabeled dataset. Besides, we have plan to optimize the proposed classifier using grid search.