Machine Vision Inspection Systems, Machine Learning-Based Approaches. Группа авторов

      Figure 1.16 Classification result by applying NN technique.

      Figure 1.17 Classification result by applying kNN technique.

Figure 1.18 Classification result by applying NB technique.

      Case-IV (NoF = 10)

Figure 1.19 Classification result by applying LR technique.

      Figure 1.20 Classification result by applying NN technique.

      Figure 1.21 Classification result by applying kNN technique.

Figure 1.22 Classification result by applying NB technique.

      Case-V (NoF = 20)

Figure 1.23 Classification result by applying LR technique.

      Figure 1.24 Classification result by applying NN technique.

       Figure 1.25 Classification result by applying kNN technique.

Figure 1.26 Classification result by applying NB technique.

      From the analysis of Figures 1.7–1.26 and Table 1.1, it is observed that NB technique provides better classification results with CA values 0.667, 0.733 and 0.767 as compared to LR, NN and kNN when the NoF is 2, 10 and 20 respectively. Again, NB and kNN techniques provide better classification results with CA value 0.633 as compared to LR and NN technique when the NoF is 3. Similarly, NB and NN techniques provide better classification results with CA value 0.667 as compared to other techniques when the NoF is 5. Here, the maximum CA value is 0.767 which is provided by NB technique when the NoF is 20. So, CA value varies for each technique when different NoF are considered. However, NB technique attempts to provide better classification results in three different cases such as cases-I, -IV, and -V. In case-II, NB and kNN provide same CA values as 0.633 and in case-III, NB and NN provide same CA values as 0.667. However, the number of instances present in different cells of confusion matrix varies for the classification results of NB and kNN in case-II as well as NB and NN in case-III. So, NB technique is able to provide better classification results overall as compared to other technique.

Table 1.1 CA (in unit) of different classification techniques.

Method	NoF = 2	NoF = 3	NoF = 5	NoF = 10	NoF = 20
LR	0.567	0.533	0.633	0.567	0.600
NN	0.567	0.567	0.667	0.667	0.667
kNN	0.467	0.633	0.600	0.633	0.633
NB	0.667	0.633	0.667	0.733	0.767

1.5 Conclusion

      This chapter focuses on the processing of several TEMVIs such as EV, ENV, LV, SARS-CoV-2 and ZV using ML-based approach. The TEMVIs are analyzed by applying ML-based classification techniques such as LR, NN, kNN and NB. Each technique carries out the classification mechanism on several TEMVIs. From the analysis of results, it is concluded that the CA values changes for each classification technique when the NoF changes. The maximum CA value is provided by NB technique when the NoF is considered as 20. The NB technique provides overall better classification results as compared to LR, NN and kNN techniques by considering different NoF. This work will be extended to analyze the performance of these ML-based classification techniques along with other classification techniques by focusing on other types of TEMVIs as well as coronavirus dis-ease-19 (COVID-19) images in future.

References

      1. Ray, U., Chouhan, U., Verma, N., Comparative study of machine learning approaches for classification and prediction of selective caspase-3 antagonist for Zika virus drugs. Neural Comput. Appl., 32, 11311–11328, 2020.

      2. Singh, J.P., Pradhan, C., Das, S.C., Image