Comparing of deep neural networks and extreme learning machines based on growing and pruning approach

•Deep Neural Networks and Extreme Learning Machines based models are designed.•The effectiveness of growing and pruning approach is examined on these models.•Deep Neural Networks models are designed by utilizing ‘Keras’ library.•Extreme Learning Machines are designed by utilizing ‘hpelm’ library.•Deep neural Networks performed better or comparable to the Extreme Learning Machines. Recently, the studies based on Deep Neural Networks and Extreme Learning Machines have become prominent. The models of parameters designed in these studies have been chosen randomly and the models have been designed in this direction. The main focus of this study is to determine the ideal parameters i.e. optimum hidden layer number, optimum hidden neuron number and activation function for Deep Neural Networks and Extreme Learning Machines architectures based on growing and pruning approach and to compare the performances of the models designed. The performances of the models are evaluated on two datasets; Parkinson and Self-Care Activities Dataset. Multi experiments have verified that the Deep Neural Networks architectures present a good prediction performance and this architecture outperforms the Extreme Learning Machines.