## 4 Discussion of Results
### General Discussion about Deep Formula Detection and Root-Extracting Classifier  
Compared with QRNN, the proposed method is 3% less accurate. Our best results (63.15%) were 3% less accurate than those of QRNN (66.59%) under the same test conditions. In comparison, RAW (64.27) had a 1% lower accuracy. FBCNN with the same weight can achieve this accuracy or less. When compared with CNN models, our proposed method has a 10% lower accuracy than CNN, 7% lower than CNN, 17% lower than DBN, 19% lower than RNN-GRU, and 16% lower than RN-LSTM. In the two-class classification, our proposed method achieved 12% lower accuracy than CNN (Relu, tanh(FC), and sigmoid(FC)), 17% lower than CNN, and 10% lower than CNN (ERlu (conv), and softmax (FC)). The essential purpose of deep neural networks is to extract complex features to distinguish patterns and increase the accuracy. Therefore, classifiers can obtain the best accuracy and discrimination using these complex features. This paper presents, for the first time, a new and different method called deep recognition instead of deep learning, which requires identifying the roots of formulas for classes. This study introduces a new model for artificial intelligence and machine learning. By developing this model, a model for recognizing the roots of formulas for activities can be presented as deep detection, which can help expand and further understand artificial intelligence fields. Based on this formula, the reasons for the influence of the environment formulas on that formula can be examined. However, in deep learning, one cannot go into detail because the layers do not express the information in detail. Due to the high complexity, it is not easy to interpret the layers. The deep formula detection structure is such that the first layer, all electrodes, or channel data are converted into a formula model. In this study, only one channel was used, but with development, two or more dimensions can be used, but the implementation is somewhat difficult. After the first layer, processing was performed on the formula model. Therefore, this study changed the processing model from a data-processing structure to a formula-processing structure (the first layer converts the data into a formula). In the other layers, the formulas are converted into a simple formula. After the extraction in the last layer, formulas are sent to the classifier to extract and identify the roots. Our proposed model requires further research to be excellent, although this model can obtain 55-60% accuracy for a single channel. However, this method is less accurate. In articles using similar data (except for the formula model), there is less difference between them in a single channel. The maximum accuracy for a single channel is between 60 and 70. One of the reasons that it is lower than the others is that it uses a frequency range of 4 Hz for processing. The rest use a 26 Hz frequency for processing. The acceptable amount for one channel is compared to articles with results in various contexts in this field (limited frequency range, single channel, appropriate window size, appropriate function definition, etc.). By solving this problem, the accuracy can be increased to between 60 and 70 or higher, because most information is lost during the creation of the filter bank. Other reasons can be related to the following: (1) choosing a window of the right size to convert the data into a formula; (2) choosing an appropriate sample size for the coefficients; (3) selecting or creating a suitable sampling function for the coefficients; (4) choosing the appropriate frequency range; (5) choosing the appropriate interval in which the root is located; (6) selecting long times in the first run of one second. Finally, by developing the proposed method, it was possible to discover the main formula for complex activity. The novelty is that we convert the specified roots for each class that is different from other classes into a formula. We also convert the formula of the common roots of all classes into a formula.