Name
Abstract | ||
|---|---|---|
The need for the internet of things technologies becomes a state-of-the-art in this era. Human beings do many activities during their daily life which, in certain cases, should to be recognized and understood. Intelligent systems are considered to be the most advanced methods to analyze such these complex tasks. Spiking neural network is one of the most powerful intelligent techniques that has the ability to solve such these problems. In this paper, a hybrid spiking neural network model is proposed for clustering user's activities which are recognized in a smart environment. The model is composed of both recurrent and adaptive spiking neural networks. The results show that the proposed hybrid spiking neural model is able to do the clustering of users' activities in a distinguishing way. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/INDIN.2017.8104772 | 2017 IEEE 15th International Conference on Industrial Informatics (INDIN) |
Keywords | Field | DocType |
recurrent spiking neural network,adaptive threshold neuron,smart environment | Smart environment,Intelligent decision support system,Intelligent sensor,Internet of Things,Artificial intelligence,Engineering,Decoding methods,Cluster analysis,Spiking neural network,Hidden Markov model,Machine learning | Conference |
ISSN | ISBN | Citations |
1935-4576 | 978-1-5386-0838-8 | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hesham H. Amin | 1 | 6 | 2.94 |
| Wael A. Deabes | 2 | 5 | 4.12 |
| Kheireddine Bouazza | 3 | 0 | 0.34 |