Abstract
The emerging of new models in machine learning enhances the performance of algorithms proposed to address several challenging tasks such as object recognition, classification and identification purpose. Nowadays. the deep learning algorithms are playing a massive role in accurately addressing complex problems due to their capability of learning various complex features from data. One limitation of deep learning is a lack of sufficient data for training. In this study, we proposed variants of Long Short Term Memory (LSTM) model and ensemble learning methods such as XGBoost, AdaBoost, Bagging, Stacking and Random forest. The experimentation is carried out on Time series data generated from the Internet of Things (IoT) devices. To validate the proposed method, we have used a freely available dataset on the web namely Smart-Fall datasets. To measure the performance of the proposed method, we have used standard performance measures namely, accuracy, precision, recall, f-score, specificity, geometric mean and confusion matrix. A set of experimental details are carried out on the SmartFall dataset and the experimental results exhibit that the Random forest algorithm performs better when compared with a single deep LSTM model and different ensemble techniques.