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.
29. Miranda-Saksena, M., Boadle, R.A., Cunningham, A.L., Preparation of Herpes Simplex Virus-Infected Primary Neurons for Transmission Electron Microscopy, in: Herpes Simplex Virus, pp. 343–354, Humana, New York, NY, 2020.
30. Prasad, S., Potdar, V., Cherian, S., Abraham, P., Basu, A., Team, I.N.N., Transmission electron microscopy imaging of SARS-CoV-2. Indian J. Med. Res., 151, 2–3, 241, 2020.
31. Roingeard, P., Raynal, P.I., Eymieux, S., Blanchard, E., Virus detection by transmission electron microscopy: Still useful for diagnosis and a plus for biosafety. Rev. Med. Virol. , 29, 1, e2019, 2019.
32. Xie, L., Song, X.J., Liao, Z.F., Wu, B., Yang, J., Zhang, H., Hong, J., Endoplasmic reticulum remodeling induced by Wheat yellow mosaic virus infection studied by transmission electron microscopy. Micron, 120, 80–90, 2019.
33. Thomas, T., Vijayaraghavan, A.P., Emmanuel, S., Machine Learning and Cybersecurity, in: Machine Learning Approaches in Cyber Security Analytics, pp. 37–47, Springer, Singapore, 2020.
34. Mirjalili, S., Faris, H., Aljarah, I., Introduction to Evolutionary Machine Learning Techniques, in: Evolutionary Machine Learning Techniques , pp. 1–7, Springer, Singapore, 2020.
35. Jena, K.K., Mishra, S., Mishra, S., Bhoi, S.K., Stored Grain Pest Identification Using an Unmanned Aerial Vehicle (UAV)-Assisted Pest Detection Model, in: Machine Vision Inspection Systems: Image Processing, Concepts, Methodologies and Applications , vol. 1, pp. 67–83, 2020.
36. Nayak, S.R., Mishra, J., Khandual, A., Palai, G., Fractal dimension of RGB color images. Optik , 162, 196–205, 2018.
37. Nayak, S.R. and Mishra, J., Analysis of Medical Images Using Fractal Geometry, in: Histopathological Image Analysis in Medical Decision Making , pp. 181–201, IGI Global, Hershey, Pennsylvania, 2019.
38. Nayak, S.R., Mishra, J., Palai, G., Analysing Roughness of Surface through Fractal Dimension: A Review. Image Vision Comput., 89, 21–34, 2019.
39. Jena, K.K., Mishra, S., Mishra, S.N., Bhoi, S.K., Nayak, S.R., MRI Brain Tumor Image Analysis Using Fuzzy Rule Based Approach. J. Res. Lepid., 50, 98–112, 2019.
40. Nayak, S.R. and Mishra, J., A modified triangle box-counting with precision in error fit. J. Inf. Optim. Sci., 39, 113–128, 2018.
41. Jena, K.K., Mishra, S., Mishra, S.N., Bhoi, S.K., 2L-ESB: A Two Level Security Scheme for Edge Based Image Steganography. Int. J. Emerg. Technol., 10, 29–38, 2019.
42. Nayak, S.R., Mishra, J., Palai, G., An extended DBC approach by using maximum Euclidian distance for fractal dimension of color images. Optik , 166, 110–115, 2018.
43. Jena, K.K., Mishra, S., Mishra, S., Bhoi, S.K., Unmanned Aerial Vehicle Assisted Bridge Crack Severity Inspection Using Edge Detection Methods, in: 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) , IEEE, pp. 284–289, 2019.
44. Nayak, S.R., Ranganath, A., Mishra, J., Analysing Fractal Dimension of Color Images, in: IEEE International Conference on Computational Intelligence and Networks 2015 , pp. 156–159, 2019.
45. Jena, K.K., Mishra, S., Mishra, S.N., An Edge Detection Approach for Fractal Image Processing, in: Examining Fractal Image Processing and Analysis , pp. 1–22, IGI Global, Hershey, Pennsylvania, 2020.
46. Nayak, S.R., Mishra, J., Padhy, R., A New Extended Differential Box-Counting Method by Adopting Unequal Partitioning of Grid for Estimation of Fractal Dimension of Grayscale Images, in: Computational Signal Processing and Analysis , pp. 45–57, Springer, Singapore, 2018.
47. Bhoi, S.K., Panda, S.K., Jena, K.K., Mallick, C., Khan, A., A fuzzy approach to identify fish red spot disease, in: Grey Systems: Theory and Application, 2020.
48. Das, S.K., Nayak, S.R., Mishra, J., Fractal Geometry: The Beauty of Computer Graphics. J. Adv. Res. Dyn. Control Syst. , 9, 10, 76–82, 2017.
49. Jena, K.K., Mishra, S., Mishra, S.N., An Algorithmic Approach Based on CMS Edge Detection Technique for the Processing of Digital Images, in: Examining Fractal Image Processing and Analysis, pp. 252–272, IGI Global, Hershey, Pennsylvania, 2020.
50. Nayak, S.R., Khandual, A., Mishra, J., Ground truth study on fractal dimension of color images of similar texture. J. Text. Inst., 109, 2018, 1159–1167, 2020.
51. Nayak, S.R., Mishra, J., Jena, P.M., Fractal analysis of image sets using differential box counting techniques. Int. J. Inf. Technol., 10, 39–47, 2018.
52. Nayak, S.R., Mishra, J., Palai, G., A modified approach to estimate fractal dimension of gray scale images. Optik, 161, 136–145, 2018.
53. Jena, K.K., Nayak, S.R., Mishra, S., Mishra, S.N., Vehicle Number Plate Detection: An Edge Image Based Approach, in: 4th Springer International Conference on Advanced Computing and Intelligent Engineering, Advances in Intelligent Systems and Computing , 2019.
Читать дальше