Page 356 - IJB-9-4

International Journal of Bioprinting Bioprinting with machine learning

24. Ghayoomi Mohammadi M, Mahmoud D, Elbestawi M, 2021, 35. Zhang Z, Jiang T, Li S, et al., 2018, Automated feature
On the application of machine learning for defect detection in learning for nonlinear process monitoring—An approach
L-PBF additive manufacturing. Opt Laser Technol, 143:107338. using stacked denoising autoencoder and k-nearest neighbor
rule. J Process Contr, 64:49–61.
https://doi.org/10.1016/j.optlastec.2021.107338
https://doi.org/10.1016/j.jprocont.2018.02.004
25. Caggiano A, Zhang J, Alfieri V, et al., 2019, Machine learning-
based image processing for on-line defect recognition in 36. Basheer IA, Hajmeer M, 2000, Artificial neural networks:
additive manufacturing. CIRP Ann, 68(1):451–454. Fundamentals, computing, design, and application.
J Microbiol Methods, 43(1):3–31.
https://doi.org/10.1016/j.cirp.2019.03.021
26. Gobert C, Reutzel EW, Petrich J, et al., 2018, Application https://doi.org/10.1016/S0167-7012(00)00201-3
of supervised machine learning for defect detection during 37. Wu Y-c, Feng J-w, 2018, Development and application
metallic powder bed fusion additive manufacturing using of artificial neural network. Wireless Pers Commun,
high resolution imaging. Addit Manuf, 21:517–528. 102(2):1645–1656.

https://doi.org/10.1016/j.addma.2018.04.005 https://doi.org/10.1007/s11277-017-5224-x
27. Li R, Jin M, Paquit VC, 2021, Geometrical defect detection 38. Huang Y, 2009, Advances in artificial neural networks–
for additive manufacturing with machine learning models. methodological development and application[J]. Algo,
Mater Design, 206:109726. 2(3):973–1007.
https://doi.org/10.3390/algor2030973
https://doi.org/10.1016/j.matdes.2021.109726
39. Yang GR, Wang X-J, 2020, Artificial neural networks for
28. Bezdek JC, Chuah SK, Leep D, 1986, Generalized k-nearest neuroscientists: A primer. Neuron, 107(6):1048–1070.
neighbor rules. Fuzzy Set Syst, 18(3):237–256.
https://doi.org/10.1016/j.neuron.2020.09.005
https://doi.org/10.1016/0165-0114(86)90004-7
40. Abiodun OI, Jantan A, Omolara AE, et al., 2018, State-of-
29. Gou J, Ma H, Ou W, et al., 2019, A generalized mean the-art in artificial neural network applications: A survey.
distance-based k-nearest neighbor classifier. Expert Syst Heliyon, 4(11):e00938.
Appl, 115:356–372.
https://doi.org/10.1016/j.heliyon.2018.e00938
https://doi.org/10.1016/j.eswa.2018.08.021 41. Vlachas PR, Pathak J, Hunt BR, et al., 2020, Backpropagation
30. Wang C, Shi Y, Fan X, et al., 2019, Attribute reduction based algorithms and reservoir computing in recurrent neural
on k-nearest neighborhood rough sets. Int J Approx Reason, networks for the forecasting of complex spatiotemporal
106:18–31. dynamics. Neural Netw, 126:191–217.
https://doi.org/10.1016/j.ijar.2018.12.013 https://doi.org/10.1016/j.neunet.2020.02.016
42. Chan LW, Fallside F, 1987, An adaptive training algorithm for
31. Gallego A-J, Calvo-Zaragoza J, Valero-Mas JJ, et al., 2018,
Clustering-based k-nearest neighbor classification for large- back propagation networks. Comput Speech Lang, 2(3):205–218.
scale data with neural codes representation. Pattern Recogn, https://doi.org/10.1016/0885-2308(87)90009-X
74:531–543. 43. Hameed AA, Karlik B, Salman MS, 2016, Back-propagation

https://doi.org/10.1016/j.patcog.2017.09.038 algorithm with variable adaptive momentum. Knowledge-
Based Syst, 114:79–87.
32. Zhang Q, Zhou H, Jiang Y, et al., 2019, A simple joint
modulation format identification and OSNR monitoring https://doi.org/10.1016/j.knosys.2016.10.001
scheme for IMDD OOFDM transceivers using K-nearest 44. Wright LG, Onodera T, Stein MM, et al., 2022, Deep physical
neighbor algorithm. Appl Sci, 9(18):3892. neural networks trained with backpropagation. Nature,
601(7894):549–555.
33. Ertuğrul ÖF, Tağluk ME, 2017, A novel version of k nearest
neighbor: Dependent nearest neighbor. Appl Soft Comput, https://doi.org/10.1038/s41586-021-04223-6
55:480–490. 45. Lawrence S, Giles CL, Ah Chung T, et al., 1997, Face
https://doi.org/10.1016/j.asoc.2017.02.020 recognition: a convolutional neural-network approach.
IEEE Trans Neural Netw, 8(1):98–113.
34. Shahabi H, Shirzadi A, Ghaderi K, et al., 2020, Flood https://doi.org/10.1109/72.554195
detection and susceptibility mapping using sentinel-1
remote sensing data and a machine learning approach: 46. Jaderberg M, Simonyan K, Vedaldi A, et al., 2016, Reading
Hybrid intelligence of bagging ensemble based on k-nearest text in the wild with convolutional neural networks. Int J
neighbor classifier. Remote Sens, 12(2):266. Comput Vis, 116(1):1–20.
https://doi.org/10.1007/s11263-015-0823-z

Volume 9 Issue 4 (2023) 348 https://doi.org/10.18063/ijb.739