IEEE Computational Intelligence Magazine - November 2020 - 22

[6] N. Siddique, H. Adeli, Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing. Hoboken, NJ: Wiley, 2013.
[7] R. Marks, "Intelligence: Computational versus Artificial," IEEE Trans. Neural Netw.,
vol. 4, no. 5, pp. 737-739, 1993.
[8] C. Stergiou and D. Siganos, "Neural networks," Imperial Coll. London Surprise J.,
vol 4, p. 1.
[9] M. J. Somers, J. C. Casal, "Using artificial neural networks to model nonlinearity,"
Organ. Res. Methods, vol. 12, no. 3, pp. 403-417, 2009. doi: 10.1177/1094428107309326.
[10] V. Novák, I. Perfilieva, J. Mocˇ korˇ, Mathematical Principles of Fuzzy Logic. Dordrecht:
Kluwer, 1999.
[11] K. De Jong, Evolutionary Computation: A Unified Approach. Cambridge, MA: MIT
Press, 2006.
[12] A. K. Palit and D. Popovic, Computational Intelligence in Time Series Forecasting: Theory
and Engineering Applications. New York: Springer-Verlag, 2006, p. 4.
[13] F. Gaillard, "Normal chest CT - Lung window." Accessed: June 28, 2020. [Online].
Available: https://radiopaedia.org/cases/normal-chest-ct-lung-window-1?lang=us
[14] S. Roy et al., "Deep learning for classification and localization of COVID-19 markers
in point-of-care lung ultrasound," IEEE Trans. Med. Imag., vol. 39, no. 8, pp. 2676-2687,
Aug. 2020. doi: 10.1109/TMI.2020.2994459.
[15] L. Wang and A. Wong, "COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images," 2020,
arXiv:2003.09871.
[16] Z. Han et al., "Accurate screening of COVID-19 using attention based deep 3D
multiple instance learning," IEEE Trans. Med. Imag., vol. 39, no. 8, pp. 2584-2594, Aug.
2020, doi: 10.1109/TMI.2020.2996256.
[17] H. Panwar, P.K. Gupta, M. K. Siddiqui, R. Morales-Menendez, and V. Singh, "Application of deep learning for fast detection of COVID-19 in X-rays using nCOVnet,"
Chaos, Solitons, Fractals, vol. 138, p. 109,944, 2020. doi: 10.1016/j.chaos.2020.109944.
[18] Y. J. Oh, S. J. Park, and J. C. Ye, "Deep learning COVID-19 features on CXR using
limited training data sets," IEEE Trans. Med. Imag., vol. 39, no. 8, pp. 2688-2700, Aug.
2020. doi: 10.1109/TMI.2020.2993291.
[19] I. Apostolopoulos, S. Aznaouridis, and M. Tzani, "Extracting possibly representative COVID-19 biomarkers from X-ray images with deep learning approach and image
data related to pulmonary diseases," J Med. Biol. Eng., 2020. doi: 10.1007/s40846-02000529-4.
[20] S. Wang et al., "A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis," Eur. Respir. J., vol. 56, no. 1, p. 2,000,775, 2020. doi:
10.1183/13993003.00775-2020.
[21] S. Ayyoubzadeh, S. M. Ayyoubzadeh, H. Zahedi, M. Ahmadi, and S. R. N. Kalhori, "Predicting COVID-19 incidence through analysis of google trends data in Iran:
Data mining and deep learning pilot study," JMIR Public Health Surveill., vol. 6, no. 2,
p. e18828, 2020. doi: 10.2196/18828.
[22] S. Vaid, R. Kalantar, and M. Bhandari, "Deep learning COVID-19 detection bias:
Accuracy through artificial intelligence," Int. Orthop., vol. 44, pp. 1539-1542, 2020. doi:
10.1007/s00264-020-04609-7.
[23] L. Huang, R. Han, T. Ai, P. Yu, H. Kang, Q. Tao, and L. Xia, "Serial quantitative
chest CT assessment of COVID-19: Deep-learning approach," Radiol., Cardiothorac. Imag.,
vol. 2, no. 2, p. e200075, 2020. doi: 10.1148/ryct.2020200075.
[24] B. Hurt, S. Kligerman, and A. Hsiao, "Deep learning localization of pneumonia:
2019 coronavirus (COVID-19) outbreak," J. Thoracic Imag., vol. 35, no. 3, pp. 87-89, May
2020. doi: 10.1097/RTI.0000000000000512.
[25] O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks or biomedical image segmentation," in Proc. 2015 Int. Conf. Medical Image Computing and Computer-Assisted Intervention, Munich, Oct. 5-9, 2015, pp. 234-241.
[26] Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, "Unet++: A nested u-net
architecture for medical image segmentation," in Proc. Workshop Deep Learning Medical
Image Analysis and Multimodal Learning Clinical Decision Support, Granada, Spain, Sept. 20,
2018, pp. 3-11.
[27] L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, "Encoder-decoder
with atrous separable convolution for semantic image segmentation," in Proc. 2018 European Conf. Computer Vision (ECCV), Munich, Sept. 8-14, 2018, pp. 801-818.
[28] S. Jégou, M. Drozdzal, D. Vazquez, A. Romero, and Y. Bengio, "The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation," in
Proc. 2017 IEEE Conf. Computer Vision and Pattern Recognition Workshops, Honolulu,
HI, pp. 11-19.
[29] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition,"
in Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV,
pp. 770-778. doi: 10.1109/CVPR.2016.90.
[30] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, "MobileNet V2:
Inverted residuals and linear bottlenecks," in Proc. 2018 IEEE Conf. Computer Vision and
Pattern Recognition, Salt Lake City, UT, pp. 4510-4520.
[31] G. Huang, Z. Liu, and K. Q. Weinberger, "Densely connected convolutional networks," in Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las
Vegas, NV, pp. 4700-4708
[32] T.-Y. Lin, P. Doll´ar, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature
pyramid networks for object detection," in Proc. 2017 IEEE Conf. Computer Vision and
Pattern Recognition (CVPR), Honolulu, HI, pp. 936-944. doi: 10.1109/CVPR.2017.106.
[33] N. Dhiman, and M. Sharma, "Fuzzy logic inference system for identification and
prevention of coronavirus (COVID-19)," Int. J. Innov. Technol. Exploring Eng., vol. 9, pp.
2278-3075, 2020. doi: 10.35940/ijitee.F4642.049620.
[34] D. M. Nazarov, "Fuzzy model of digital assessment of donation systems' level in
COVID-19," in Proc. 2nd Int. Scientific and Practical Conf. "Modern Management Trends and
Digital Economy: From Regional Development Global Economic Growth" (MTDE 2020), Yekaterinburg, Russia, 2020. doi: 10.2991/aebmr.k.200502.199.

22

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | NOVEMBER 2020

[35] N. Van Tinh, "Forecasting of COVID-19 Confirmed cases in Vietnam using fuzzy
time series model combined with particle swarm optimization," Comput. Res. Progr. Appl.
Sci. Eng., vol. 6, no. 2, pp. 114-120, 2020.
[36] Z. Yang, X. Li, H. Garg, and M. Qi, "Decision support algorithm for selecting an
antivirus mask over COVID-19 pandemic under spherical normal fuzzy environment,"
Int. J. Environ. Res. Public Health, vol. 17, p. 3407, 2020. doi: 10.3390/ijerph17103407.
[37] Z. Ren, H. Liao, and Y. Liu, "Generalized Z-numbers with hesitant fuzzy linguistic information and its application to medicine selection for the patients with mild symptoms of the
COVID-19," Comput. Ind. Eng., vol. 145, p. 106,517, July 2020, doi: 10.1016/j.cie.2020.106517.
[38] A. Yousefpour, H. Jahanshahi, and S. Bekiros, "Optimal policies for control of the
novel coronavirus COVID-19 outbreak," Chaos, Solitons Fractals, p. 109,883, 2020. doi:
10.1016/j.chaos.2020.109883.
[39] M. Niazkar, and H. R. Niazkar, "COVID-19 outbreak: Application of multi-gene
genetic programming to country-based prediction models," Electron. J. General Med., vol.
17, no. 5, p. em247, 2020.
[40] R. Salgotra, M. Gandomi, and A. Gandomi, "Time series analysis and forecast of the
COVID-19 pandemic in India using genetic programming," Chaos, Solitons, Fractals, vol.
138, p. 109,945, 2020. doi: 10.1016/j.chaos.2020.109945.
[41] R. Salgotra, M. Gandomi, and A. Gandomi, "Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries," Chaos, Solitons Fractals, vol. 140, p.
110,118, Nov. 2020. doi: 10.1016/j.chaos.2020.110118.
[42] S. Dilbag, Singh, V. Chahar, Vaishali, and M. Kaur, "Classification of COVID-19
patients from chest CT images using multi-objective differential evolution-based convolutional neural networks," Eur. J. Clin. Microbiol. Infect. Dis., Official Publ. Eur. Soc.
Clin. Microbiol., vol. 39, no. 7, pp. 1379-1389, 2020. doi: 10.1007/s10096-020-03901-z.
[43] K. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution: A Practical Approach
to Global Optimization. New York: Springer-Verlag, 2006.
[44] R. Duffey, and E. Zio, "Analysing recovery from pandemics by learning theory:
The case of COVID-19," IEEE Access, vol. 8, pp. 110,789-110,795, 2020. doi: 10.1109/
ACCESS.2020.3001344.
[45] G. Wang et al., "A noise-robust framework for automatic segmentation of COVID-19
pneumonia lesions from CT images," IEEE Trans. Med. Imag., vol. 39, no. 8, Aug. 2020.
doi: 10.1109/TMI.2020.3000314.
[46] M. Barstugan, U. Ozkaya, and S. Ozturk, "Coronavirus (COVID-19) classification
using CT images by machine learning methods," 2020, arXiv:2003.094.
[47] G. S. Randhawa, M. P. M. Soltysiak, H. El Roz, C. P. E. de Souza, K. A. Hill, and
L. Kari, "Machine learning using intrinsic genomic signatures for rapid classification of
novel pathogens: COVID-19 case study," PLoS ONE, vol 15, no. 4, pp. 2653-2663, 2020.
doi: 10.1371/journal.pone.0232391.
[48] X. Mei et al., "Artificial intelligence-enabled rapid diagnosis of patients with COVID-19," Nature Med., pp. 1-5, 2020.
[49] I. D. Apostolopoulos and T. A. Mpesiana, "COVID-19: Automatic detection from
x-ray images utilizing transfer learning with convolutional neural networks," Phys. Eng.
Sci. Med., vol. 43, pp. 635-640, 2020. doi: 10.1007/s13246-020-00865-4.
[50] A. Tarvainen and H. Valpola, "Mean teachers are better role models: Weightaveraged consistency targets improve semi-supervised deep learning results," in Proc. 31st
Conf. Neural Information Processing Systems (NIPS 2017), Long Beach, CA, pp. 1195-1204.
[51] L. Yu, S. Wang, X. Li, C. W. Fu, and P. A. Heng, "Uncertainty-aware self-ensembling
model for semi-supervised 3D left atrium segmentation," in Proc. 22nd Int. Conf. Medical
Image Computing and Computer Assisted Intervention, Shenzhen, China, 2019, pp. 605-613.
[52] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale
image recognition," 2014, arXiv:1409.1556.
[53] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, "Inception-V4, inceptionresnet and the impact of residual connections on learning," in Proc. 31st AAAI Conf.
Artificial Intelligence, San Francisco, 2017, pp. 4278-4284.
[54] F. Chollet, "Xception: Deep learning with depth-wise separable convolutions," in
Proc. 2017 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp.
1251-1258. doi: 10.1109/CVPR.2017.195.
[55] F. A. Cássaro and L. F. Pires, "Can we predict the occurrence of COVID-19 cases?
Considerations using a simple model of growth," Sci. Total Environ., vol. 728, p. 138,834,
2020. doi: 10.1016/j.scitotenv.2020.138834.
[56] A. J. Kucharski et al., "Early dynamics of transmission and control of COVID-19: A
mathematical modelling study," Lancet Infect. Dis., vol. 20, no. 5, pp. 553-558, 2020. doi:
10.1016/S1473-3099(20)30144-4.
[57] X. Zhang, R. Ma, and L. Wang, "Predicting turning point, duration and attack rate of covid-19 outbreaks in major western countries," Chaos, Solitons, Fractals, vol. 135, p. 109,829, 2020.
[58] "Using artificial intelligence to help combat COVID-19." Accessed: June 28, 2020.
[Online]. Available: https://www.oecd.org/coronavirus/policy-responses/using-artificial
-intelligence-to-help-combat-covid-19-ae4c5c21/
[59] BlueDot. Accessed: June 28, 2020. [Online]. Available: https://bluedot.global/
[60] EpiRisk. Accessed: June 28, 2020. [Online]. Available: https://epirisk.net/
[61] J. Parrock, "Coronavirus: Belgium hospital employs robot to protect against COVID-19," Euronews, Accessed: June 28, 2020. [Online]. Available: https://www.euronews
.com/2020/06/02/coronavirus-belgium-hospital-employs-robot-to-protect-against
-covid-19
[62] "Canada's COVID-19 Chatbot." Accessed: June 28, 2020. [Online]. Available: https://
covidchatbot.com/
[63] T. S. Perry, "Satellites and AI monitor Chinese economy's reaction to coronavirus."
Accessed: June 28, 2020. [Online]. Available: https://spectrum.ieee.org/view-from-the
-valley/artif icial-intelligence/machine-learning/satellites-and-ai-monitor-chinese
-economys-reaction-to-coronavirus/
[64] WeBank. Accessed: June 28, 2020. [Online]. Available: https://www.webank.com/
#/home

https://www.radiopaedia.org/cases/normal-chest-ct-lung-window-1?lang=us https://www.oecd.org/coronavirus/policy-responses/using-artificial-intelligence-to-help-combat-covid-19-ae4c5c21/ https://www.oecd.org/coronavirus/policy-responses/using-artificial-intelligence-to-help-combat-covid-19-ae4c5c21/ https://www.bluedot.global/ https://www.epirisk.net/ https://www.euronews.com/2020/06/02/coronavirus-belgium-hospital-employs-robot-to-protect-against-covid-19 https://www.euronews.com/2020/06/02/coronavirus-belgium-hospital-employs-robot-to-protect-against-covid-19 https://www.euronews.com/2020/06/02/coronavirus-belgium-hospital-employs-robot-to-protect-against-covid-19 http://www.covidchatbot.com/ http://www.covidchatbot.com/ https://spectrum.ieee.org/view-from-the-valley/artificial-intelligence/machine-learning/satellites-and-ai-monitor-chinese-economys-reaction-to-coronavirus/ https://spectrum.ieee.org/view-from-the-valley/artificial-intelligence/machine-learning/satellites-and-ai-monitor-chinese-economys-reaction-to-coronavirus/ https://spectrum.ieee.org/view-from-the-valley/artificial-intelligence/machine-learning/satellites-and-ai-monitor-chinese-economys-reaction-to-coronavirus/ https://www.webank.com/#/home https://www.webank.com/#/home

IEEE Computational Intelligence Magazine - November 2020

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - November 2020