**Step-2: Classification**

This process executes image classification. There are two main layers as follows:


At this stage, the pretrained models such as *AlexNet, VGGNet, GoogLeNet*, and *ResNet* will be applied in order to integrate the CNNs. The procedure is to merge the multiple CNNs with fixed number of CNNs and trained them after merging.

## **2.2 Association rule**

Association rule is utilized in data mining phase as it is a machine learning method which finds the relationship between the items based on the frequency of item sets [18, 19]. To do this, a mathematical algorithm is needed to arrange the frequent item sets, for extracting association rule of food sets.

There are three measures in association rule task as shown in **Figure 2**:


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**Figure 3.**

*Schematic diagram of food recommender.*

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

As the uncertainty of convolutional architecture due to the fix number of CNNs and the interpretability after merging of CNNs [20]. Simpler and more highly scalable method to integrate the multiple networks is necessary and therefore there are three features in consideration - liability score for the system, no further training after integration, and high scalability. Deep neural network (DNN) may be an alternative choice to solve this problem [21]. It provides the neural basis for efficient visual object recognition in humans. Its architecture comprises more than three layers. Each of layers contains a combination of convolution, max-pooling and normalization stages, whereas these layers are fully connected. It inherently fuses the process of feature extraction with classification into learning using Fuzzy Support Vector Machine (FSVM) and enables the decision making [22, 23]. Many efforts were made to speed up both the training time as well as inference time of DNNs. Since the flexibility and the performance scalability to deal with various types of networks are crucial requirement of the DNN accelerator design [24], we therefore proposed a scalable architecture for integrating different deep neural networks with a reliability score to increase the probability to return correct class as the result of food recognition. The reliability score allowed the integrated DNN to select a suitable recognition result obtained from the different DNNs that were independently constructed. In this study, we evaluated the feasibility of our proposed method. The frequent set of foods extracted from food images was applied to a data mining algorithm such as the *Apriori* algorithm for the food recommendation process. **Figure 3** shows the schematic diagram of food recommender. This system would recommend a set of foods suitable for users based on association rule, which were

The process of food recognition and extraction of association rule is given in **Figure 4**. Firstly, the food set images from database were collected. Secondly, the reliability score was calculated by Eq. (1). Then the food recognition and

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

extracted by food set mining from food images.

**3. Proposed method**

**Figure 2.** *Basic terminologies of association rule measure.* *A Food Recommender Based on Frequent Sets of Food Mining Using Image Recognition DOI: http://dx.doi.org/10.5772/intechopen.97186*
