## 2 System Model: Data Mining Approach  
In all biomedical applications, the major need is to gather the data from various sources for comparison cases, and any redundant data must be removed from the system. Therefore, the proposed technique provides high importance to data-mining procedures, in addition to fuzzy incorporation systems. In the first step, the initial strength of biomedical signals must be monitored, and it can be mathematically represented by using Equation (1). \[s_{i}=\sum_{i=1}^{n}\left|\frac{\alpha_{i}}{\alpha_{n}^{s}}\right|\times 100 \tag{1}\]  
where \(\alpha_{i}\) denotes the stored reference vector, and \(\alpha_{n}^{s}\) represents the observed strength vector values. From Equation (1), we can see that both vector values that are distributed in different regions are calculated, and a test is made at the initial stage. If the biomedical signal strengths are higher, then there is no necessity for boosting the process. During the testing process, the strength of the biomedical signals is much higher without any attenuation. Therefore, a cross-point intersection of signals can be formulated by using Equation (2), as follows:  
\[\partial_{i}=\sum_{i=1}^{n}\frac{\left|M_{i}\cap\mu_{i}\right|}{\rho_{i}}\times 100 \tag{2}\]  
where \(M_{i}\) denotes data mining of \(i\)th set paths, \(\mu_{i}\) represents the cipher score values, and \(\rho_{i}\) indicates the maximum threshold values. From Equation (2), a set of intersection points of different biomedical signals is observed and is later separated into distinct forms. This leads to the calculation of distance between two signals; as in many signal-processing techniques, the closer the signal levels are, the higher the accuracy of the fuzzy incorporated systems will be. Therefore, after intersection points, the distance measurements of biomedical signals are calculated by using Equation (3). \[d_{i}=\sum_{i=1}^{n}\frac{ME_{i}-MR_{in}}{\max(ME_{i},MR_{in})} \tag{3}\]  
where \(ME_{i}\) denotes the mean distance of signals, and \(MR_{in}\) represents the input reference representation of signals. Equation (3) denotes the difference between the mean distances that will provide variation, and, at this point, maximization can be performed within quick representation points. This maximization of points provide conjoint correlation between sets of points, and it can be mathematically modeled by using Equation (4). \[C_{i}=\sum_{i=1}^{n}\frac{\varepsilon_{x}\varepsilon_{y}-l_{i}}{\sqrt{\left( \varepsilon_{x}-\varepsilon_{y}\right)^{2}}} \tag{4}\]  
where \(\varepsilon_{x}\) and \(\varepsilon_{y}\) denote the coefficient matrix of \(x\) and \(y\) parameters, respectively; and \(I_{i}\) represents the independent correlation matrix. From Equation (4), we can see that the square of difference between the coefficients will provide higher values; thus, the values in the upper segment will rise to a larger extent. Therefore, the criteria for selecting signals can be given as follows:  
\[P_{i}=\sum_{i=1}^{n}\varepsilon_{i}^{2}-\left[\varepsilon_{n}^{2},\varepsilon_ {in}^{2}\right] \tag{5}\]  
where \(\varepsilon_{i}\), \(\varepsilon_{in}\), and \(\varepsilon_{in}\) denote the dimensional feature set of input and output variables. The process of selecting signals will vary for each iteration; hence, Equation (5) can be redesigned in mathematical terms, where the signals can be differentiated by using Equation (6), as follows:  
\[\varepsilon_{in}=\sum_{i=1}^{n}\left|\frac{1}{\gamma}\right|(l_{i}) \tag{6}\]  
where \(\gamma\) indicates the absolute signaling factor terms, and \(l_{i}\) denotes the length of total signal representations. The membership set can be used for defining a set of biomedical signal representations where multiple inputs and outputs can be defined. This can be expressed in mathematical form, as follows:  
\[DF_{i}=\sum_{i=1}^{n}\frac{M_{i}(O_{i})*O_{i}}{O_{i}^{2}} \tag{7}\]  
where \(M_{i}(O_{i})\) denotes the membership function in de-fuzzified output at the receiver, and \(O_{i}\) represents the output functions.