## 4 Results and Discussion
* Scenario 1  
Since the data that are collected from the patients are numerical in character, they cannot be processed at various stages, due to incomplete information in the central database. As a result, the data-mining methodology cannot be applied at various stages, as only manual changing process is present. To avoid the aforementioned condition and to convert the representation of the system, a signal representation technique is followed in medical treatment. This automatic procedure provides complete details of persistent characteristics where a sensor is used in the intermediate process as a channel to sense the values, and it reports the same to the control center. Moreover, the sensor can sense the received signals only when the signal strength is much higher; otherwise, intermediate noise will be present that will occupy the entire space in the system. The automatic signal representation is very important, as it gives rise to the perception learning mechanism, using fuzzy set models with limited exploration. Furthermore, if perception learning is precise with the maximization of signal strength, then the proposed system will provide a clear insight about classification features. Figure 3 and Table 2 show the signal strength of the acquired data that can be visualized by using normalized frequency values. Before plotting the strength of the signal, the noise present in the system is removed and filtered by using the fuzzy least-square method. Therefore, the system is completely set as noise-free, and the received signal strength is maximized. This can be observed from the number of signal records, as it increases to a high amount; the low signal representations are transformed to high signals in both existing [6] and proposed models. However, the augmentation of signal strength in the proposed fuzzy model is much higher, as the Hadoop physical structure is incorporated in the system. Furthermore, if the signal does not reach a maximum value, it is stationary at average values, thus maintaining complete operation at a minimized risk factor. Since the process carried out in the proposed method is discussed with the received signal, it is classified with low, center (medium), and upper strengths. All three values specify that, when the process is integrated by using the fuzzy set, it should not be lesser or greater than the reference data set of signals that is indicated in Table 1. The major reason for dividing these values is that a greater number of signals will be  
Figure 3: Measurement and comparison of signal strength. patients is higher; therefore, to avoid correlations with different signals, they are grouped as low-, medium-, and high-range signals. * Scenario 2  
Due to incorporation of fuzzy set an intersection occurs in signal transmission process and these intersection points must be different for all signals. Therefore, a distance transmission technique is incorporated for biomedical signals for recording multiple values in the system. The distance of biomedical signals is measured by using the installed electrodes at several points in real-time implementation. For each time period the electrodes will change the position as the signal type will differ at applied input side. Moreover, fuzzy systems will develop a set of distance with respect to signals which are measured using a serrate electrode. Both the values of electrodes are combined as mean values as given in Equation (3). Furthermore, the distance of measurement is separated by using sample frequency values for processing the signals, which are represented as reference representations. The installed electrodes also vary according to the number of signals that are received from different patients. The entire system was notified once the values are exceeded with compared reference values. In such a case, the system can be prevented from entering in the same node; therefore, signal distance can be measured at this point.