## 4 Results and Discussion
If the signals are closer at the intersection point, then a high risk factor will be assured, whereas, if the signals are farther away from intersection points, the risk factor can be reduced to a great extent. Usually, the signals are measured from bonce position to the bottom of the pole by using a data-acquisition device; therefore, it is better to incorporate the points at the first stage, before measurement. This gives a clear insight about distance, and if any wrong values are measured, then points can be changed with immediate effect. Figure 4 and Table 3 show the simulated distance of biomedical signals with two pole points, and in the proposed method, the points are not varied until maximum signal transmission is observed. The varying distance is measured for time periods that start at 6:00 h and end at 24:00 h, and in this intermediate period, the distance of measurement cannot be varied, as the tolerant limits remain in a steady condition. Moreover, the separated clusters will have individual separated distance; thus, they are grouped within the primary cluster. It can be observed that, even during changing hours, the distance of separation is maximized for the proposed method, whereas, in the existing system [6,15], the separated distance of all combined signals is much lower. Due to the low signal representations, a correlation factor is determined in the existing models, but low correlation factors exist in the fuzzy interference system. This proves that, at varying time factors, the proposed method can provide accurate signals at distant points without any interruption from other clustered signals. \begin{table}
\begin{tabular}{c c c|c c c} \hline \hline
**Number of** & **Low [6]** & **Medium [6]** & **High [6]** & **Low** & **Medium** & **High** \\
**Signal Records** & & & & & & \\ \hline
10,000 & 34 & 39 & 44 & 52 & 68 & 78 \\
20,000 & 37 & 46 & 52 & 65 & 74 & 83 \\
30,000 & 41 & 54 & 63 & 73 & 82 & 89 \\
40,000 & 43 & 59 & 67 & 78 & 86 & 94 \\
50,000 & 45 & 61 & 72 & 81 & 89 & 96 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Variation of signal strength. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time Period** & **Distance Separation [6]** & **Distance Separation [15]** & **Distance Separation (Proposed)** \\ \hline
6 & 12.7 & 17.8 & 36.8 \\
7 & 12.4 & 17.9 & 34.9 \\
8 & 12.1 & 16.4 & 32.1 \\
9 & 11.7 & 16.9 & 30.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Distance of separation vs. time periods. * Scenario 3  
The handling capability of biomedical signals using fuzzy system can be proved by using a loss-function parametric evaluation, wherein low loss must be achieved. This loss function can be determined by using the database reference set and square of logical functionalities, wherein the enhanced signal outputs are represented. During this determination process, the loss function is mapped with real period representations that are associated with various clusters. However, a least-square technique can be applied in this case to prevent such signal losses in the system. In addition, the distance of measurement that is observed in Scenario 2 is also higher; therefore, the proposed system will provide a much lower loss, and the problem of uncertainty can also be solved. The biomedical signals exhibit two different properties, namely variance and symmetric, for which values in terms of magnitude are measured. Thus, the two property limits are examined and after careful experimentation error in loss values are found and plotted in Figure 5.  
From Figure 5 and Table 4, it can be perceived that, if the errors are present at one side of the plot, it will be propagated through all distinct directions. The simulation plot is represented with a number of iterations, and for each variation, the total loss in the system is calculated. The major reason for plotting the number of iterations is that the expected output will be compared with achieved fuzzy set outputs, and if any variations  
Figure 4: Comparison of signal separation with distance measurements.