Page 112 - IJEEE-2023-Vol19-ISSUE-1
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108 | Abed, Wali, & Alaziz
(c) V =2.5 m/s algorithm is more complicated. The DT is considered a
Fig. 12: Continued. confirmation algorithm more than SVM because it does not
deal with the dependent and independent data as linear or
Fig. 13: Pressure distribution of various velocities, gas is non-linear regression. While SVM should be specified for
used fluid. linear or nonlinear expressions which must be solved by
Gaussian approximation [33], the accuracy is determined by
the following equation:
7889:;8< = ?@AC?@@AA??BBACB (6)
The following equations also determine the precision, recall,
and F1-score for the SVM model:
=:>8?@?AB = ?@?A@C@ (7)
(8)
(9)
!>8;CC = ?@?A@CB
Where: D1 - @8A:> = 2 ? @@DD''EFEGFFGHFH"" ?AJJ''EE%%KKKK
TP is True Positive
TN is True Negative
FP is False Positive, and
FN is False Negative
TABLE II
Precision, recall, and f1-score for the (SVM) and (DT)
models.
Model Precision % Recall % F1-score %
SVM 91.67 88 89.8
DT 100 97 100
Fig. 15, shows that the optimization plots are developed
based on trained values; the parallel coordinate of column
interactions (position, pressure at 0.1 m/s, pressure at 1 m/s,
and pressure at 2.5 m/s). The scatter plot indicates the
pressure distribution of the leaked and non-leaked points are
presented based on optimization plots and confusion matrix.
Ball position
Fig. 14: Outlet Oil between Lleeaakks and Inlet Oil
Pressure comparison non-leaks
cases where oil flows in 2.5 m/s.
B. Machine learning models results (a) Parallel coordinates of optimization.
The training phase of SVM and DT is divided into 70% Fig. 15: The optimization plot and Scattering
for training and 30% for testing by cross-validation, and the examination using the SVM model.
average accuracy is 98.8%, and 99.87%, respectively. Table
II shows the SVM and DT model's average precision, recall,
and F1-score of the present work. The DT has perfect
precision, Recall, and F1-score as compared with SVM. The
membership function of DT is more than SVM, the DT
