## 4 Results and Discussion  
The output values of the developed system model are integrated for the purpose of data analysis using fuzzy systems. Since, in fuzzy systems, binary representations are made, probabilistic determinations will be made for decisions, thus dividing the input ecological data at maximized signal distance. In order to establish a complete relationship with knowledge representation, it is necessary to implement the data that are processed with master and slave blocks in a Hadoop infrastructure, using fuzzy interface systems. Thus, the developed system model is integrated with loop mechanisms with a high correlation matrix, using activation functions. Furthermore, the integrated process will have high similarities for all divided clusters, but this is eliminated by using fuzzy interface systems. This makes the signals to travel at maximized distance without any duplication representations. The outcome of the integrated system model a with fuzzy system is deliberated with simulation analysis in this section, where a Hadoop physical system is represented with a cluster formation technique. Moreover, for the tranquil analysis, the input and output nodes are separated, and individual node configurations are added in the specifications, where, in both nodes, the biomedical signals are passed at various points. During this signal flow point, three stages are designed, namely incorporation, dispensation, and cognitive therapy. For all of the abovementioned stages, a set of layered tools are organized, and they are examined under five different scenarios as follows: \begin{tabular}{c c c c} \hline \hline Scenario 1: & Amount of signal strength; & \\ Scenario 2: & Measurement of signal distance; & \\ Scenario 3: & Minimization of loss function; & \\ Scenario 4: & Fuzzy-signal correlation; & \\ Scenario 5: & Cost of implementation. & \\ \hline \hline \end{tabular}   
All five scenarios will exhibit unique characteristics in the designed system model; therefore, a high-end processor is used for simulation with MATLAB. Furthermore, for biomedical signal transmission, reception data are collected from the control center; therefore, more readings are congregated to examine the possible outcomes. The configuration setup with a data set is given in Table 1. \begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{4}{c}{**Reference Data Set**} \\ \cline{2-5}  & **Total Volume of** & **Percentage of** & **Percentage of** & **Position of Data** \\  & **the Signal** & **Signal Error** & **Signal Projection** & **Signals** \\ \hline
10 & 222.23 & 0.3 & +1.78 & \(-\)0.56 \\ \hline
100 & 2356.15 & 1.4 & +1.94 & \(-\)0.28 \\ \hline
1000 & 5891.01 & 1.6 & +2.15 & +0.01 \\ \hline
2000 & 8942.07 & 2.0 & +2.28 & +0.07 \\ \hline
5000 & 11,247.56 & 2.2 & +3.16 & +1.21 \\ \hline
10,000 & 14,892.18 & 2.4 & +3.49 & +1.45 \\ \hline \hline \end{tabular} \begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Signal characteristics**} \\ \hline \hline \multicolumn{3}{c}{**Impetus of signal: 50 Volts**} \\ \hline \hline \multicolumn{3}{c}{**Time series: 200-400 microseconds**} \\ \hline \hline \multicolumn{3}{c}{**Input velocity: 20 m/s**} \\ \hline \hline \multicolumn{3}{c}{**Bandwidth: 20,000 Hz**} \\ \hline \hline \multicolumn{3}{c}{**Length of the biological signal (input): 25 micro volts**} \\ \hline \hline \multicolumn{3}{c}{**Length of the biological signal (output): 18 micro volts**} \\ \hline \hline \multicolumn{3}{c}{**Number of signals represented: 1768**} \\ \hline \hline \end{tabular} \begin{tabular}{c c} \hline \hline Scenario 2: & Measurement of signal distance; \\ Scenario 3: & Minimization of loss function; \\ Scenario 4: & Fuzzy-signal correlation; \\ Scenario 5: & Cost of implementation. & \\ \hline \hline \end{tabular}   
All five scenarios will exhibit unique characteristics in the designed system model; therefore, a high-end processor is used for simulation with MATLAB.