Legalisation

Legalisation

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5 outputs for 5 types of recommendations: hospitals (yes or no), gas stations (yes or no), restaurants
(yes or no) Places of Interest (yes or no) and Movie Theater (yes or no) Table 1 shows example data sets.
After that, we use a classification machine using WEKA tools to train the recommended system when to call each of the five types of instructions. Suggest the right type at the right context.
Android Mobile Application System Test
After we install the system, as shown in the previous section. We conducted a series of experiments to test the system. In the first batch of experiments, we created 1500 randomized test data to test several types of curator systems. Table 2 shows the primary results for the test data generated. The first column in Table 2 shows five types of recommendations. We got a very good result. The main reason for this difference is the training data set. For example, for a hospital, if we have a human temperature or abnormal blood pressure, we will recommend the hospital. However, for cinemas, attractions and restaurants, it is a little more complicated to process contextual information, it makes these recommendations.

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