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

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СКАЧАТЬ 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 Processing and Machine Learning Techniques to Detect and Classify Paddy Leaf Diseases: A Review, in: Machine Learning and Information Processing, pp. 161–172, Springer, Singapore, 2020.

      3. Cao, Z., Identification of the Association between Hepatitis B Virus and Liver Cancer using Machine Learning Approaches based on Amino Acid, in: Proceedings of the 2020 10th International Conference on Bioscience, Biochemistry and Bioinformatics, 2020, January, pp. 56–63.

      4. Sambasivam, G. and Opiyo, G.D., A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egypt. Inform. J., 2020.

      5. Al-Kasassbeh, M., Mohammed, S., Alauthman, M., Almomani, A., Feature Selection Using a Machine Learning to Classify a Malware, in: Handbook of Computer Networks and Cyber Security, pp. 889–904, Springer, Cham, 2020.

      6. Yang, X., Yang, S., Li, Q., Wuchty, S., Zhang, Z., Prediction of human-virus protein–protein interactions through a sequence embedding-based machine learning method. Comput. Struct. Biotechnol. J., 18, 153–161, 2020.

      7. Dey, L., Chakraborty, S., Mukhopadhyay, A., Machine Learning Techniques for Sequence-based Prediction of Viral–Host Interactions between SARS-CoV-2 and Human Proteins, Biomedical J., 2020.

      8. Gibert, D., Mateu, C., Planes, J., The rise of machine learning for detection and classification of malware: Research developments, trends and challenges. J. Netw. Comput. Appl., 153, 1–22, 2020.

      9. Karanja, E.M., Masupe, S., Jeffrey, M.G., Analysis of internet of things malware using image texture features and machine learning techniques. Internet Things, 9, 100153, 2020.

      10. Sen, P.C., Hajra, M., Ghosh, M., Supervised Classification Algorithms in Machine Learning: A Survey and Review, in: Emerging Technology in Modelling and Graphics, pp. 99–111, Springer, Singapore, 2020.

      11. Ahuja, R., Chug, A., Gupta, S., Ahuja, P., Kohli, S., Classification and Clustering Algorithms of Machine Learning with their Applications, in: Nature-Inspired Computation in Data Mining and Machine Learning, pp. 225–248, Springer, Cham, 2020.

      12. Di Noia, A., Martino, A., Montanari, P., Rizzi, A., Supervised machine learning techniques and genetic optimization for occupational diseases risk prediction. Soft Comput., 24, 6, 4393–4406, 2020.

      13. Firdausi, I., Erwin, A., Nugroho, A.S., Analysis of machine learning techniques used in behavior-based malware detection, in: 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2010, December, IEEE, pp. 201–203.

      14. Muda, Z., Yassin, W., Sulaiman, M.N., Udzir, N.I., Intrusion detection based on K-Means clustering and Naïve Bayes classification, in: 2011 7th International Conference on Information Technology in Asia, 2011, July, IEEE, pp. 1–6.

      15. Chen, Y., Luo, Y., Huang, W., Hu, D., Zheng, R.Q., Cong, S.Z., Wang, X.Y., Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B. Comput. Biol. Med., 89, 18–23, 2017.

      16. Shruthi, U., Nagaveni, V., Raghavendra, B.K., A review on machine learning classification techniques for plant disease detection, in: 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 2019, March, IEEE, pp. 281–284.

      17. Trishna, T.I., Emon, S.U., Ema, R.R., Sajal, G.I.H., Kundu, S., Islam, T., Detection of Hepatitis (A, B, C and E) Viruses Based on Random Forest, K-nearest and Naïve Bayes Classifier, in: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2019, July, IEEE, pp. 1–7.

      18. Mahajan, G., Saini, B., Anand, S., Malware Classification Using Machine Learning Algorithms and Tools, in: 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), 2019, February, IEEE, pp. 1–8.

19. Kaur, D., Machine Learning Approach for Credit Card Fraud Detection (KNN & Naïve Bayes), Machine Learning Approach for Credit Card Fraud Detection (KNN & Naïve Bayes) in International Conference on Innovative Computing & Communications (ICICC), 2020.

      20. Goyal, S., Naïve Bayes Model Based Improved K-Nearest Neighbor Classifier for Breast Cancer Prediction, in: International Conference on Advanced Informatics for Computing Research, 2019, June, Springer, Singapore, pp. 3–11.

      21. Devika, R., Avilala, S.V., Subramaniyaswamy, V., Comparative Study of Classifier for Chronic Kidney Disease prediction using Naive Bayes, KNN and Random Forest, in: 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), 2019, March, IEEE, pp. 679–684.

      22. Wahid, M.F., Hasan, M.J., Alom, M.S., Mahbub, S., Performance Analysis of Machine Learning Techniques for Microscopic Bacteria Image Classification, in: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2019, July, IEEE, pp. 1–4.

      23. Matuszewski, D.J. and Sintorn, I.M., Reducing the U-Net size for practical scenarios: Virus recognition in electron microscopy images. Comput. Methods Programs Biomed., 178, 31–39, 2019.

      24. Kumar, D. and Maji, P., An Efficient Method for Automatic Recognition of Virus Particles in TEM Images, in: International Conference on Pattern Recognition and Machine Intelligence, 2019, December, Springer, Cham, pp. 21–31.

      25. Steur, N.A. and Mueller, C., Classification of Viral Hemorrhagic Fever Focusing Ebola and Lassa Fever Using Neural Networks. Int. J. Mach. Learn. Comput., 9, 3, 334–343, 2019.

      26. Dreiseitl, S. and Ohno-Machado, L., Logistic regression and artificial neural network classification models: A methodology review. J. Biomed. Inf., 35, 5–6, 352–359, 2002.

      27. Ito, E., Sato, T., Sano, D., Utagawa, E., Kato, T., Virus particle detection by convolutional neural network in transmission electron microscopy images. Food Environ. Virol., 10, 2, 201–208, 2018.

      28. Devan, K.S., Walther, P., von Einem, J., Ropinski, T., Kestler, H.A., Read, C., Detection of herpesvirus capsids in transmission electron microscopy images using transfer learning. Histochem. Cell Biol., 151, 2, 101–114, 2019.

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