Dog behaviors identification model using ensemble convolutional neural long short-term memory networks

Abstract

This paper presents a new model based on Convolutional Neural Networks (CNN) with a long short-term memory network (LSTM) and ensemble technique for identifying seven different dogs’ behaviors. The proposed model uses data collected from two sensors attached to the dog’s back and neck. In the initial step in the model, the undefined tasks are removed, and the synthetic minority oversampling technique (SMOTE) is performed to address the imbalanced data problem. Then, CNN_LSTM and ensemble classifier are adapted to identify various dog behaviors. Finally, two experiments are performed to evaluate the model. The first experiment is performed utilizing the original data (imbalanced datasets) while the second uses a balanced dataset. Experimental results can identify seven dog behaviors with a potential accuracy of 96.73%, 96.76% sensitivity, 96.73% specificity, and 96.73% F1 score. Therefore, the SMOTE method, a data balancing strategy, not only overcomes the unbalanced data problem but also significantly improves minority class accuracy. Additionally, the suggested model is tested against cutting-edge algorithms, and the outcomes demonstrate its superior performance.