**5. Analysis results**

Tests and access points for the best algorithm will be presented using a good statistical test called the Wilcoxon Proficient Placement Test Scale.

#### **5.1 Wilcoxon marked**

Positional evaluation of the Wilcoxon marked positioning test determines the difference between two illustrations [16] and provides an optional territory trial that is influenced by the sizes and indications of these distinctions. The following theories are addressed by this test:

$$\begin{aligned} \, \, H0 &: mean(A) = mean(B) \\ \, \, H1 &: mean(A) \neq mean(B) \end{aligned} \tag{5}$$

The solutions to the first and second hypothesis are denoted by the letters A and B, correspondingly. Furthermore, this metric determines if one prediction outperforms the other. Let di denote the gap between the presentation scores of two calculations when it comes to dealing with the *ith* out of n difficulties. Let *R*<sup>þ</sup> represent the number of sites for instances where the main computation beats the second. Finally, let R- deal with the number of places for the instances where the next estimate outperforms the previous. Several 0's are equitably spread across the entireties. If any of these totals have an odd number, one of them has been discarded:

$$\begin{aligned} \mathbf{R}^+ &= \sum\_{\mathbf{d}\_\mathbf{i} > 0} \mathbf{rank}(\mathbf{d}\_\mathbf{i}) + \frac{\mathbf{1}}{2} \sum\_{\mathbf{d}\_\mathbf{i} = \mathbf{0}} \mathbf{rank}(\mathbf{d}\_\mathbf{i})\\ \mathbf{R}^- &= \sum\_{\mathbf{d}\_\mathbf{i} < 0} \mathbf{rank}(\mathbf{d}\_\mathbf{i}) + \frac{\mathbf{1}}{2} \sum\_{\mathbf{d}\_\mathbf{i} = \mathbf{0}} \mathbf{rank}(\mathbf{d}\_\mathbf{i}) \end{aligned} \tag{6}$$

We utilize MATLAB to find p self-worth in order to contrast the equations at a large degree of alpha = 0.05. Also rand (di) represents the random number between the interval (0, 1).

The invalid hypothesis is rejected when the p-esteem is not exactly the essential part. R+ deals with a high mean estimate that demonstrates predominance over processes of planning using a variety of test setups. This method outperforms all other algorithms in all tests. While *<sup>R</sup>*<sup>þ</sup> <sup>¼</sup> *<sup>n</sup>* <sup>∗</sup> ð Þ *<sup>n</sup>*þ<sup>1</sup> <sup>2</sup> surpasses all other techniques in all of adventure.
