The Recommendation System plays an increasingly important role in our daily lives. With the increasing amount of information on the internet, the recommendation system can also solve problems caused by increasing information quickly. Collaborative filtering is one method in the recommendation system that makes recommendations by analyzing correlations between users.Collaborative filtering accumulates customer product ratings, identifies customers with common ratings, and offers recommendations based on inter-customer comparisons. It’s based on the idea that people who agree in their evaluations of certain items in the past are likely to agree again in the future. In this research, we analyze the differences in various implementation of collaborative filtering approaches such as item-based, item-based with the genre, and user-based.
The EMG signals can be produced from the human muscle activity. This paper explains the control system of the robot mobile which uses the EMG signal from three muscles contraction such as the jaw muscles, the right arm muscles, and the left arm muscles. The peak values of the EMG signal was used as the input to the ANN system using the Backpropagation algorithm, and it produces the output which has nine target conditions; the forward, then turn right and then turn left where each of them has the variations such as the slow, the medium and the fast. The ANN has been trained with 270 of the training data, and it has been tested with 135 of the testing data from 15 respondents who performed muscle contraction activities. Based on the test result, there are only two unrecognizable data with the rate of the system accuracy is 98.52%. The movements of the robot mobile can be controlled based on the recognized movement patterns with the specified target conditions.
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