An IoT aware nature inspired Multilayer Hybrid Dropout Deep-learning paradigm for waste image classification and management

In this manuscript, the combination of IoT and Multilayer Hybrid Dropout Deep-learning Model for waste image categorization is proposed to categorize the wastes as bio waste and non-bio waste. The input captured images are pre-processed and remove noises in the captured images. Under this approach, a Nature inspired Multilayer Hybrid Dropout Deep-learning Model is proposed. Multilayer Hybrid Dropout Deep-learning Model is the consolidation of deep convolutional neural network and Dropout Extreme Learning Machine classifier. Here, deep convolutional neural network is used for feature extraction and Dropout Extreme Learning Machine classifier for categorizing the waste images. To improve the classification accurateness, Horse herd optimization algorithm is used to optimize the parameter of the Dropout Extreme Learning Machine classifier. The objective function is to maximize the accuracy by minimize the computational complexity. The simulation is executed in MATLAB. The proposed Multilayer Hybrid Dropout Deep-learning Model and Horse herd optimization algorithm attains higher accuracy 39.56% and 42.46%, higher Precision 48.74% and 34.56%, higher F-Score 32.5% and 45.34%, higher Sensitivity 24.45% and 34.23%, higher Specificity 31.43% and 21.45%, lower execution time 0.019(s) and 0.014(s) compared with existing waste management and classification using convolutional neural network with hyper parameter of random search optimization algorithm waste management and classification using clustering approach with Ant colony optimization algorithm. Finally, the proposed method categorizes the waste image accurately.