**6. Conclusion**

In food recognition phase, integrated networks (DNNs) showed higher recognition accuracy (80%) than a single network. Since the proposed reliability score allowed the integrated networks to select a suitable recognition result obtained from the different network with different domains. The performance of networks where *GoogLeNet* applied gave higher recognition accuracy. In addition, it was found that when we used the test dataset different from the trained dataset, we could not get the suitable results.

In Data mining phase, we could extract some meaningful rules by applying the *Apriori* algorithm to recognize the results of canteen image dataset. In our future work, we will modify this system in recognition phase and will increase the performance of the networks. We will evaluate the effectiveness of modified system using bigger size of food data. In addition, we are developing visual food mining using mixed model of DNN and RNN (recurrent neural networks) for continuing our research.

### **Acknowledgements**

This research is a part of academic cooperation between Chulalongkorn University, Thailand and Kanagawa Institute of Technology (KAIT), Japan. Authors would like to thank KAIT for providing scholarship and deeply appreciate Professor Kosuke Takano for his advice and facility in his Laboratory at KAIT.

#### **Conflict of interest**

The authors declare no potential conflict of interest.
