**4. Experimental and evaluation**

We implemented our prototype of merging the results from different DNNs using MATLAB. The performance of recognition accuracy and the relationship between the recognition accuracy and the number of classes were evaluated. We used the dataset of food images for creating the DNNs and evaluated them as shown in **Table 1**. Three DNNs with different number of recognition classes for each dataset were created as given in **Table 2**. In addition, we made each DNN from *Scratch* by applying the transfer learning using *GoogLeNet*. Accordingly, we totally had 18 DNNs in this experiment.

Recognition accuracy was calculated using the following equation:

$$\text{Accuracy} = \left(\text{TP} + \text{TN}\right) / \left(\text{TP} + \text{TN} + \text{FP} + \text{FN}\right) \tag{3}$$

where TP, TN, FP, and FN were True Positive, True Negative, False Positive, and False Negative respectively.

#### *Artificial Intelligence - Latest Advances, New Paradigms and Novel Applications*


#### **Table 1.**

*Dataset of food images.*


#### **Table 2.**

*Deep neural network with different number of classes.*


#### **Table 3.**

*Integrated deep neural network.*


#### **Table 4.**

*Food labels used for association rule.*

To evaluate the performance of integrated deep neural network, we created two single DNNs and four integrated DNNs as shown in **Table 3**. The *UNIMIB2016* dataset for the test was employed. Recognition accuracy then was calculated using Eq. (3).

**81**

**Figure 8.**

*Result of integrated deep neural network.*

**Figure 7.**

*Recognition results of food-101, uec-256 and fruit-360.*

*A Food Recommender Based on Frequent Sets of Food Mining Using Image Recognition*

labels as shown in **Table 4** for extracting the association rule.

To evaluate the accuracy of extracted frequent food set, we employed three food

**Figure 7** shows the recognition accuracy of DNN by food-101 dataset, uec-256 dataset, and fruit-360 dataset respectively. There was a relationship between the recognition accuracy of DNN and the increase of the number of class for each database. It was found that the recognition accuracy of DNN slightly decreased according to the increase of the number of recognition classes. In addition, the performance of DNNs where *GoogLeNet* applied was higher than the DNNs made

**Figure 8** shows the result of integrated deep neural network. It implied that if the number of networks was increased, the recognition accuracy of the integrated

*DOI: http://dx.doi.org/10.5772/intechopen.97186*

**5. Results and discussion**

from *Scratch*.

*A Food Recommender Based on Frequent Sets of Food Mining Using Image Recognition DOI: http://dx.doi.org/10.5772/intechopen.97186*

To evaluate the accuracy of extracted frequent food set, we employed three food labels as shown in **Table 4** for extracting the association rule.
