Title

Computational Intelligence in engineering practice

Journal title

Bulletin of the Polish Academy of Sciences Technical Sciences

Yearbook

2021

Volume

69

Issue

3

Affiliation

Osowski, Stanislaw : Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland ; Osowski, Stanislaw : Military University of Technology, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland ; Sawicki, Bartosz : Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland ; Cichocki, Andrzej : RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0106, Japan

Authors

Osowski, Stanislaw ; Sawicki, Bartosz ; Cichocki, Andrzej

Divisions of PAS

Nauki Techniczne

Coverage

e137052

Bibliography

1.  H. Das, J. K. Rout, and S.C.N. Dey, Maharana, Applied Intelligent Decision Making in Machine Learning, CRC Press, 2020.
2.  B. Zhang, Y. Wu, J. Lu, and K.L. Du, “Evolutionary computation and its applications in neural and fuzzy systems”, Appl. Comput. Intell. Soft Comput. 2011, 938240 (2011), doi: 10.1155/2011/938240
3.  M. Injadat, A. Moubayed, A.B. Nassif, and A. Shami, “Machine learning towards intelligent systems: applications, challenges, and opportunities”, Artif. Intell. Rev. (2021), https://doi.org/10.1007/s10462-020-09948-w.
4.  I. Goodfellow, Y. Bengio, and A. Courville, Deep learning, MIT Press, 2016.
5.  J. Heaton, “Applications of Deep Neural Networks”, arXiv: 2009.05673v2 [cs. LG] 2021, Heaton Research, Inc.
6.  E. Kayacan and M.A. Khanesar, Fuzzy Neural Networks for Real Time Control Applications: Concepts, Modeling and Algorithms for Fast Learning, Elsevier, 2015.
7.  A. Burkov, Machine Learning Engineering, True Positive Inc., 2020.
8.  A. Krizhevsky, I. Sutskever, and G. Hinton, Imagenet classification with deep convolutional neural networks, NIPS, 2012.
9.  A. Cichocki, R. Zdunek, A. H. Phan, and S.-I. Amari, Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation, Wiley, 2009.
10.  A. Khan, A. Sohail, U. Zahoora, and A.S. Qureshi, “A survey of the recent architectures of deep convolutional neural networks”, Artif. Intell. Rev. 53, 5455–5516 (2020), doi: 10.1007/s10462-020-09825-6.
11.  A. Osowska-Kurczab, T. Markiewicz, M. Dziekiewicz, and M. Lorent, “Multi-feature ensemble system for renal tumour classification”, Bull. Pol. Acad. Sci. Tech. Sci. 69(3), e136749 (2021).
12.  E. Kot, Z. Krawczyk, K. Siwek, P. Czwarnowski, and L. Królicki, “Deep learning-based framework for tumour detection and semantic segmentation”, Bull. Pol. Acad. Sci. Tech. Sci. 69(3), e136750 (2021).
13.  Z. Krawczyk and J. Starzyński, “Segmentation of bone structures with the use of deep learning techniques”, Bull. Pol. Acad. Sci. Tech. Sci. 69(3), e136751 (2021).
14.  T. Leś, “U-Net based frames partitioning and volumetric analysis for kidney detection in tomographic images”, Bull. Pol. Acad. Sci. Tech. Sci. 69(3), e137051 (2021).
15.  M. Kołodziej, A. Majkowski, P. Tarnowski, R. Rak, and A. Rysz, “A New Method of Cardiac Sympathetic Index Estimation Using 1D-Convolutional Neural Network”, Bull. Pol. Acad. Sci. Tech. Sci. 69(3), e136921 (2021).
16.  E. Majda-Zdancewicz et al., “Deep learning vs. feature engineering in the assessment of voice signals for diagnosis in Parkinson’s disease”, Bull. Pol. Acad. Sci. Tech. Sci. 69(3), e137347 (2021).
17.  F. Gil and S. Osowski, “Fusion of feature selection methods in gene recognition”, Bull. Pol. Acad. Sci. Tech. Sci. 69(3), e136748 (2021).
18.  K. Godlewski and B. Sawicki, “Optimisation of MCTS player for The Lord of the Rings: the card game”, Bull. Pol. Acad. Sci. Tech. Sci. 69(3), e136752 (2021).

Date

30.06.2021

Type

Article

Identifier

DOI: 10.24425/bpasts.2021.137052