Comparing of deep neural networks and extreme learning machines based on growing and pruning approach
Yazarlar (1)
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Expert Systems with Applications (Q1) | ||
| Dergi ISSN | 0957-4174 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 02-2020 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-02-2020 |
| Cilt / Sayı / Sayfa | 140 / 1 / 1–7 | DOI | 10.1016/j.eswa.2019.112875 |
| Makale Linki | https://linkinghub.elsevier.com/retrieve/pii/S0957417419305858 | ||
| Özet |
| 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. |
| Anahtar Kelimeler |
| Deep Neural Networks | Extreme Learning Machines | Growing and pruning | Parkinson | Self-care activities |
Science Direct
Comparing of deep neural networks and extreme learning machines based on growing and pruning approach
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 56 |
| Google Scholar | 70 |