## 6 Results and Discussion
### Performance Analysis  
The performances of the suggested HC + SU-CMO are computed over extant classification models and optimization models regarding varied metrics. Moreover, evaluations were set up using datasets [34; 35] respectively, and relevant outcomes are plotted in Figures 3-7. Analysis of suggested HC + SU-CMO model is analyzed and compared with HC + ALO, HC + AO, HC + BOA, HC + CMBO, HC + SSA, NN, RNN, Bi-GRU, SVM, and KNN for LPs which ranges from 60 to 80 with an interval of 10. Further, the assessment of the adopted scheme over the traditional approaches for accuracy and MCC is signified in Figures 3 and 5 along with an assessment of the adopted scheme over traditional approaches for F-measure and rand index is signified in Figures 4 and 6 for dataset 1 and dataset 2 respectively. In the case of this estimation, the HC + SU-CMO model has grown superior results when compared to other schemes. On looking through Figures 3a and 5a, the accuracy of the HC + SU-CMO model raise compared to other schemes for dataset 1 and dataset 2. Especially, better outputs are attained at the 60 th and 70 th LP for the proposed scheme regarding accuracy for both datasets. The superiority of the developed model is proven over HC + ALO, HC + AO, HC + BOA, HC + CMBO, HC + SSA, NN, RNN, Bi-GRU, SVM, and KNN models.  
The proposed model shows a good accuracy level at the 60 th and 70 th learning rates; however, at the 80 th learning rate, it shows less accuracy level compared to the lower learning rate. Bi-GRU performs poorly on both metrics, accuracy, and MCC.  
Figure 3: Performance of the adopted model over other existing schemes for dataset 1 (**a**) Accuracy (b) MCC.  
Figure 4: Performance of the adopted model over other existing schemes for dataset 1 (**a**) F-measure (**b**) Rand Index.  
Figure 5: Performance of the adopted model over other existing schemes for dataset 2 (**a**) Accuracy (**b**) MCC.  
Figure 6: Performance of the adopted model over other existing schemes for dataset 2 (**a**) F-measure (**b**) Rand Index.  
F-measure is a classification evaluation measure defined as the harmonic mean of recall and precision. Figure 4a depicts the f-measure of the adopted model, producing a positive outcome at learning rate 60 th and 70 th.  
The Rand Index is another widely used measure. It is a measurement of the degree to which two data clusters are like one another. The adopted model's rand index for dataset 1 is illustrated in Figure 4b. The measure is nearly constant across all learning rates which is preferable to other existing schemes.  
The metrics findings for dataset 2 are likewise satisfactory. Figure 5a,b demonstrates the accuracy and MCC of the model used for dataset 2. Accuracy is almost consistent for all given learning rates, whilst MCC also outperforms other known techniques at all learning rates in the graph. SVM, on the other hand, exhibits worse accuracy than other approaches across all learning rates, as well as very poor outcomes in the MCC measure.  
After the proposed model, KNN and NN also perform well for dataset 2 in both metrics, i.e., accuracy and MCC.  
Figure 6a,b illustrates the F-measure and rand index for the adopted model, as well as other known techniques for dataset 2. The adopted model also performs well in these metrics over the whole learning rate. SVM performs very poorly at the 60 th learning rate and improves at the 80 th learning rate for the f-measure metric.  
All the techniques show outcomes over 60%, the adopted model having results above 80%, outperforming the other known techniques in the paper. Furthermore, we can see that no techniques underperform in the rand index metric.