{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:19:30Z","timestamp":1743092370385,"version":"3.40.3"},"publisher-location":"Cham","reference-count":78,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030633066"},{"type":"electronic","value":"9783030633073"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-63307-3_9","type":"book-chapter","created":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T17:05:15Z","timestamp":1615395915000},"page":"135-160","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning Technology for Tackling COVID-19 Pandemic"],"prefix":"10.1007","author":[{"given":"Mona","family":"Soliman","sequence":"first","affiliation":[]},{"given":"Asahraf","family":"Darwish","sequence":"additional","affiliation":[]},{"given":"Aboul Ella","family":"Hassanien","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,11]]},"reference":[{"key":"9_CR1","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1002\/jppr.1655","volume":"50","author":"P Fowler","year":"2020","unstructured":"Fowler, P.: A chance to be our best. J. Pharm. Pract. Res. 50, 122\u2013123 (2020). https:\/\/doi.org\/10.1002\/jppr.1655","journal-title":"J. Pharm. Pract. Res."},{"key":"9_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chom.2020.04.004","author":"X Xie","year":"2020","unstructured":"Xie, X., Muruato, A., Lokugamage, K., LeDuc, J., Menachery, V., Shi, P.: An Infectious cDNA Clone of SARS-CoV-2. Cell Host Microbe. Published (2020). https:\/\/doi.org\/10.1016\/j.chom.2020.04.004","journal-title":"Cell Host Microbe. Published"},{"key":"9_CR3","doi-asserted-by":"publisher","first-page":"2690","DOI":"10.3390\/ijerph17082690","volume":"17","author":"F Di Gennaro","year":"2020","unstructured":"Di Gennaro, F., Pizzol, D., Marotta, C., Antunes, M., Racalbuto, V., Veronese, N., Smith, L.: Coronavirus diseases (COVID-19) current status and future perspectives: a narrative review. Int. J. Environ. Res. Public Health 17, 2690 (2020). https:\/\/doi.org\/10.3390\/ijerph17082690","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"9_CR4","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.jare.2020.03.005","volume":"24","author":"M Adnan","year":"2020","unstructured":"Adnan, M., Suliman, S., Kazmi, A., Bashir, N., Siddiquea, R.: COVID-19 infection: origin, transmission, and characteristics of human coronaviruses. J. Adv. Res. 24, 91\u201398 (2020)","journal-title":"J. Adv. Res."},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Dargan, S., Kumar, M., Ayyagari, M.R., et\u00a0al.: A survey of deep learning and its applications: a new paradigm to machine learning. Arch. Computat. Methods Eng. (2019). https:\/\/doi.org\/10.1007\/s11831-019-09344-w","DOI":"10.1007\/s11831-019-09344-w"},{"key":"9_CR6","doi-asserted-by":"publisher","unstructured":"Ongsulee, P.: Artificial intelligence, machine learning and deep learning. In: 2017 15th International Conference on ICT and Knowledge Engineering, Bangkok, pp. 1\u20136 (2017). https:\/\/doi.org\/10.1109\/ICTKE.2017.8259629","DOI":"10.1109\/ICTKE.2017.8259629"},{"key":"9_CR7","unstructured":"Loey, M., Smarandache, F., Eldeen, N., Khalifa, M.: Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning. preprint"},{"key":"9_CR8","doi-asserted-by":"publisher","unstructured":"Bruns, D., Kraguljac, N., Bruns, T.: COVID-19: facts, cultural considerations, and risk of stigmatization. J. Transcultural Nurs., pp. 1\u20137. https:\/\/doi.org\/10.1177\/1043659620917724","DOI":"10.1177\/1043659620917724"},{"key":"9_CR9","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMcp2009249","author":"RT Gandhi","year":"2020","unstructured":"Gandhi, R.T., Lynch, J.B., del Rio, C.: Mild or moderate Covid-19. N. Engl. J. Med. (2020). https:\/\/doi.org\/10.1056\/NEJMcp2009249","journal-title":"N. Engl. J. Med."},{"key":"9_CR10","doi-asserted-by":"publisher","unstructured":"Dai, X.: ABO blood group predisposes to COVID-19 severity and cardiovascular diseases. Eur. J. Prev. Cardiol. The European Society of Cardiology 2020. Article reuse guidelines: sagepub.com\/journals-permissions. https:\/\/doi.org\/10.1177\/2047487320922370","DOI":"10.1177\/2047487320922370"},{"key":"9_CR11","doi-asserted-by":"publisher","unstructured":"Bhattacharyya, S., Maulik, U., DuttaQuantum: Inspired computational intelligence. Res. Appl., -33-83 (2017). ISBN: 978-0-12-804409-4. https:\/\/doi.org\/10.1016\/C2015-0-01859-7","DOI":"10.1016\/C2015-0-01859-7"},{"key":"9_CR12","isbn-type":"print","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/978-3-642-70911-1","volume-title":"A Logical Calculus of the Ideas Immanent in Nervous Activity","author":"G Palm","year":"1986","unstructured":"Palm, G., McCulloch, W., Pitts, W.: A Logical Calculus of the Ideas Immanent in Nervous Activity, pp. 229\u2013230. Springer, Berlin, Heidelberg (1986). ISBN 978-3-642-70911-1","ISBN":"https:\/\/id.crossref.org\/isbn\/9783642709111"},{"key":"9_CR13","unstructured":"Linnainmaa, S.: The Representation of the Cumulative Rounding Error of an Algorithm as a Taylor Expansion of the Local Rounding Errors, (In Finnish). Master\u2019s Thesis, Department of Computer Science, University of Helsinki, Helsinki, Finland (1970)"},{"key":"9_CR14","unstructured":"Ivakhnenko, A.G.: Polynomial theory of complex systems. IEEE Trans. Syst. Man Cybern. MC-1 4, 364\u2013378 (1971)"},{"issue":"10","key":"9_CR15","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1109\/5.58337","volume":"78","author":"P Werbos","year":"1990","unstructured":"Werbos, P.: Backpropagation through time: what it does and how to do it. Proc. IEEE 78(10), 1550\u20131560 (1990). https:\/\/doi.org\/10.1109\/5.58337","journal-title":"Proc. IEEE"},{"key":"9_CR16","unstructured":"Olivier, D., Bengio, Y.: Shallow vs. deep sum-product networks. In: Advances in Neural Information Processing Systems, vol. 24, pp. 666\u2013674 (2011)"},{"key":"9_CR17","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1109\/IJCNN.1992.287150","volume":"1","author":"J Weng","year":"1992","unstructured":"Weng, J., Ahuja, N., Huang, T.: Cresceptron: a self-organizing neural network which grows adaptively. International Joint Conference on Neural Networks (IJCNN) 1, 576\u2013581 (1992)","journal-title":"International Joint Conference on Neural Networks (IJCNN)"},{"key":"9_CR18","unstructured":"Williams, T., Li, R.: Wavelet Pooling for Convolutional Neural Networks. Published as a conference paper at ICLR 2018"},{"key":"9_CR19","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1023\/A:1007967800668","volume":"25","author":"J Weng","year":"1997","unstructured":"Weng, J., Ahuja, N., Huang, T.S.: Learning recognition and segmentation using the cresceptron. Int. J. Comput. Vis. 25, 109\u2013143 (1997). https:\/\/doi.org\/10.1023\/A:1007967800668","journal-title":"Int. J. Comput. Vis."},{"key":"9_CR20","unstructured":"Freund, Y., Haussler, D.: Unsupervised Learning of Distributions on Binary Vectors Using Two Layer Networks. Technical Report UCSC-CRL-94-25. University of California, Santa Cruz (1994)"},{"key":"9_CR21","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative Adversarial Nets"},{"key":"9_CR22","unstructured":"Segmentation of neuronal structures in EM stacks challenge. In: IEEE International symposium on biomedical imaging. http:\/\/tinyurl.com\/d2fgh7g (2012)"},{"key":"9_CR23","unstructured":"Ciresan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: Advances in Neural Information Processing Systems (NIPS), pp. 2852\u20132860 (2012)"},{"key":"9_CR24","unstructured":"Traian, T.: NVIDIA Tegra Inside Every Audi 2010 Vehicle. Retrieved 2016-08-03 from http:\/\/news.softpedia.com\/news\/NVIDIATegra-Inside-Every-Audi-2010-Vehicle-131529.shtml"},{"key":"9_CR25","doi-asserted-by":"publisher","unstructured":"Nayyar, Z.: Feature Engineering in Machine Learning (2015). https:\/\/doi.org\/10.13140\/RG.2.1.3564.3367","DOI":"10.13140\/RG.2.1.3564.3367"},{"key":"9_CR26","unstructured":"Huang, J., Dong, Q., Gong, S., Zhu, X.: Unsupervised Deep Learning by Neighbourhood Discovery"},{"key":"9_CR27","doi-asserted-by":"crossref","unstructured":"Caron, M., Bojanowski, P., Joulin, A., Douze, M.: Deep clustering for unsupervised learning of visual features. In Proceedings of the European Conference on Computer Vision (ECCV), pp. 1\u201318 (2018)","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Dizaji, K.G., Herandi, A., Deng, C., Cai, W., and Huang, H. Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 5747\u20135756 (2017)","DOI":"10.1109\/ICCV.2017.612"},{"key":"9_CR29","unstructured":"Yang, B., Fu, X., Sidiropoulos, N.D., Hong, M.: Towards k-means-friendly spaces: Simultaneous deep learning and clustering. In: Proceedings of the International Conference on machine learning (ICML), pp. 1\u201314 (2017)"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.: Split-brain autoencoders: unsupervised learning by cross-channel prediction. In:The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1058\u20131067 (2017)","DOI":"10.1109\/CVPR.2017.76"},{"key":"9_CR31","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Stella, X., Lin, Y.: Unsupervised feature learning via non-parametric instance discrimination. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3733\u20133742 (2018)","DOI":"10.1109\/CVPR.2018.00393"},{"key":"9_CR32","unstructured":"Nair, V., Alonso, J., Beltramell, T.: RealMix: Towards Realistic Semi-Supervised Deep Learning Algorithms (2019)"},{"key":"9_CR33","first-page":"3581","volume":"27","author":"D Kingma","year":"2014","unstructured":"Kingma, D., Rezende, D., Mohamed, S., Welling, M.: Semi-supervised learning with deep generative models. Proceedings of Advances in Neural Information Processing Systems 27, 3581\u20133589 (2014)","journal-title":"Proceedings of Advances in Neural Information Processing Systems"},{"key":"9_CR34","unstructured":"Lavet, V., Henderson, P., Islam, R., Bellemare, M., Pineau, J.: An Introduction to Deep Reinforcement Learning (2018). arXiv:1811.12560v2"},{"key":"9_CR35","unstructured":"Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: 31st Conference on Neural Information Processing Systems (2017)"},{"key":"9_CR36","doi-asserted-by":"publisher","unstructured":"Arif, W.M., Ahmed, B., Saduf, A., Iqbal, K.: Advances in Deep Learning: Basics of Supervised Deep Learning. Springer, Singapore, pp. 13\u201329 (2020). https:\/\/doi.org\/10.1007\/978-981-13-6794-6-2","DOI":"10.1007\/978-981-13-6794-6-2"},{"key":"9_CR37","unstructured":"Wang, H., Hiksha, B.: On the Origin of Deep Learning (2017). arXiv:1702.07800v4 [cs.LG]"},{"key":"9_CR38","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems (2014)"},{"key":"9_CR39","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015)","journal-title":"Neural Netw."},{"key":"9_CR40","doi-asserted-by":"crossref","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Parallel Distributed Processing. Vol. 1: Foundations. MIT Press, Cambridge, MA (1986)","DOI":"10.21236\/ADA164453"},{"key":"9_CR41","unstructured":"Yuan, F.N., Zhang, L., Shi, L., Xia, J.T., Li, X.: Theories and applications of auto-encoder neural networks: a literature survey. Chin. J. Comput. 42, 203\u2013230 (2019). https:\/\/doi.org\/10.11897\/SP.J.1016.2019.00203"},{"key":"9_CR42","doi-asserted-by":"crossref","unstructured":"Jiang, P., Maghrebi, M., Crosky, A., Saydam, S.: Unsupervised deep learning for data-driven reliability and risk analysis of engineered systems. In: Handbook of Neural Computation, pp. 417\u2013431 (2017)","DOI":"10.1016\/B978-0-12-811318-9.00023-5"},{"issue":"1","key":"9_CR43","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0893-6080(89)90014-2","volume":"2","author":"P Baldi","year":"1989","unstructured":"Baldi, P., Hornik, K.: Neural networks and principal component analysis: Learning from examples without local minima. Neural Netw. 2(1), 53\u201358 (1989)","journal-title":"Neural Netw."},{"issue":"3","key":"9_CR44","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1162\/089976600300015691","volume":"12","author":"N Japkowicz","year":"2000","unstructured":"Japkowicz, N., Hanson, S.J., Gluck, M.A.: Nonlinear autoassociation is not equivalent to pca. Neural Comput. 12(3), 531\u2013545 (2000)","journal-title":"Neural Comput."},{"key":"9_CR45","doi-asserted-by":"crossref","unstructured":"Deng, J., Zhang, Z., Marchi, E., Schuller, B.: Sparse autoencoder-based feature transfer learning for speech emotion recognition. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, pp. 511\u2013516 (2013)","DOI":"10.1109\/ACII.2013.90"},{"key":"9_CR46","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., Manzago, P.-A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning. ACM, pp. 1096\u20131103 (2008)","DOI":"10.1145\/1390156.1390294"},{"key":"9_CR47","unstructured":"Rifai, S., Vincent, P., Muller, X., Glorot, X., Bengio, Y.: Contractive auto-encoders: explicit invariance during feature extraction. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 833\u2013840 (2008)"},{"key":"9_CR48","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"9_CR49","doi-asserted-by":"crossref","unstructured":"Wang, W., Huang, Y., Wang, A., Wang, L.: Generalized autoencoder: a neural network framework for dimensionality reduction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 490\u2013497 (2014)","DOI":"10.1109\/CVPRW.2014.79"},{"key":"9_CR50","unstructured":"Wen, Y., Lie, X., Gonzalez, J.: Fast training of deep LSTM networks. Springer International Publishing, pp. 3\u201310 (2019) Isbn: 978-3-030-22796-8"},{"key":"9_CR51","doi-asserted-by":"crossref","unstructured":"Pang, B., Zha, K., Cao, H., Shi, C., Lu, C.: Deep RNN Framework for Visual Sequential Applications, pp. 423\u2013432 (2018)","DOI":"10.1109\/CVPR.2019.00051"},{"issue":"8","key":"9_CR52","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"9_CR53","doi-asserted-by":"publisher","unstructured":"Sja, F., Christian, I.: An Introduction to Restricted Boltzmann Machines, pp. 14\u201336 (2012). https:\/\/doi.org\/10.1007\/978-3-642-33275-3-2","DOI":"10.1007\/978-3-642-33275-3-2"},{"key":"9_CR54","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Aaron, C., Bengio, Y.: Generative Adversarial Nets. NIPS preceding (2014)"},{"issue":"1","key":"9_CR55","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35(1), 53\u201365 (2018)","journal-title":"IEEE Signal Process. Mag."},{"key":"9_CR56","unstructured":"Baldi, P.: Autoencoders, unsupervised learning, and deep architectures. In: JMLR: Workshop Workshop on Unsupervised and Transfer Learning, pp. 27\u201337 (2012)"},{"key":"9_CR57","unstructured":"Meng, Q., Catchpooley, D., Skillicornz, D., Kennedy, P.J.: Relational Autoencoder for Feature Extraction (2018). arXiv: 1802.03145v1 [cs.LG]"},{"key":"9_CR58","unstructured":"Pascanu, R., Gulcehre, C., Cho, K., Bengio, Y.: How to Construct Deep Recurrent Neural Networks (2014). arXiv:1312.6026v5 [cs.NE]"},{"key":"9_CR59","first-page":"190","volume":"26","author":"M Hermans","year":"2013","unstructured":"Hermans, M., Schrauwen, B.: Training and analyzing deep recurrent neural networks. Advances in Neural Information Processing Systems 26, 190\u2013198 (2013)","journal-title":"Advances in Neural Information Processing Systems"},{"key":"9_CR60","volume-title":"Long Short-Term Memory Recurrent Neural Network Architectures for Generating Music and Japanese Lyrics","author":"A Mikami","year":"2016","unstructured":"Mikami, A.: Long Short-Term Memory Recurrent Neural Network Architectures for Generating Music and Japanese Lyrics. Ph.D, Computer Science Department, Boston College (2016)"},{"key":"9_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-810408-8.00002-X","author":"H Suk","year":"2017","unstructured":"Suk, H.: Deep Learning for Medical Image Analysis (2017). https:\/\/doi.org\/10.1016\/B978-0-12-810408-8.00002-X","journal-title":"Deep Learning for Medical Image Analysis"},{"key":"9_CR62","unstructured":"Salakhutdinov, R., Hinton, G.: Deep Boltzmann Machines. In: Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS), Clearwater Beach, Florida, USA (2009). Volume 5 of JMLR: W-CP 5"},{"key":"9_CR63","unstructured":"Khan, A., Zameer, A., Jamal, T., Raza, A.: Deep Belief Networks Based Feature Generation and Regression for Predicting Wind Power (2018). arXiv:1807.11682"},{"key":"9_CR64","doi-asserted-by":"crossref","unstructured":"Karhunen, J., Raiko, T., Cho, K.: Deep learning: a short review. In: Advances in Independent Component Analysis and Learning Machines (2015). http:\/\/dx.doi.org\/10.1016\/B978-0-12-802806-3.00007-5","DOI":"10.1016\/B978-0-12-802806-3.00007-5"},{"key":"9_CR65","doi-asserted-by":"publisher","unstructured":"Punn, N., Sonbhadra, S., Agarwal, S.: COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms. Display the preprint in perpetuity. medRxiv preprint. https:\/\/doi.org\/10.1101\/2020.04.08.20057679. This version posted 11 Apr 2020","DOI":"10.1101\/2020.04.08.20057679"},{"key":"9_CR66","unstructured":"Alom, M., Rahman, M., Nasrin, M., Taha, M., Asari, K.: COVID-MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches (2020)"},{"key":"9_CR67","doi-asserted-by":"publisher","unstructured":"Ozturka, T., Talob, M., Yildirimc, E., Baloglud, U., Yildirime, O., Acharyaf, U.: Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103792","DOI":"10.1016\/j.compbiomed.2020.103792"},{"issue":"3","key":"9_CR68","doi-asserted-by":"publisher","first-page":"W87","DOI":"10.1097\/RTI.0000000000000512","volume":"35","author":"B Hurt","year":"2020","unstructured":"Hurt, B., Kligerman, S., Hsiao, A.: Deep learning localization of pneumonia: 2019 coronavirus (COVID-19) outbreak. J. Thorac. Imaging 35(3), W87\u2013W89 (2020)","journal-title":"J. Thorac. Imaging"},{"key":"9_CR69","doi-asserted-by":"publisher","unstructured":"Shadab, S., Alam Khan, T., Afrin Neezi, N., Adilina, S., Shatabda, S.: DeepDBP: Deep Neural Networks for Identification of DNA-binding Proteins. https:\/\/doi.org\/10.1101\/829432","DOI":"10.1101\/829432"},{"key":"9_CR70","doi-asserted-by":"publisher","DOI":"10.1101\/2020.01.29.925354","author":"JM Bartoszewicz","year":"2020","unstructured":"Bartoszewicz, J.M., Seidel, A., Renard, B.Y.: Interpretable Detection of Novel Human Viruses from Genome Sequencing Data (2020). https:\/\/doi.org\/10.1101\/2020.01.29.925354","journal-title":"Interpretable Detection of Novel Human Viruses from Genome Sequencing Data"},{"issue":"2","key":"9_CR71","doi-asserted-by":"publisher","first-page":"e18828","DOI":"10.2196\/18828","volume":"6","author":"SM Ayyoubzadeh","year":"2020","unstructured":"Ayyoubzadeh, S.M., Ayyoubzadeh, S.M., Zahedi, H., Ahmadi, M., Kalhori, S.R.: Predicting COVID-19 incidence through analysis of google trends data in iran: data mining and deep learning pilot study. JMIR Public Health Surveill. 6(2), e18828 (2020). https:\/\/doi.org\/10.2196\/18828","journal-title":"JMIR Public Health Surveill."},{"key":"9_CR72","doi-asserted-by":"publisher","unstructured":"Lin, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J.: Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology, p. 200905 (2020). https:\/\/doi.org\/10.1148\/radiol.2020200905","DOI":"10.1148\/radiol.2020200905"},{"key":"9_CR73","unstructured":"Linda, W., Wong, A.: COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images (2020). arXiv preprint arXiv:2003.09871"},{"key":"9_CR74","doi-asserted-by":"crossref","unstructured":"Chuansheng, Z., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., Wang, X.: Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label. medRxiv (2020)","DOI":"10.1101\/2020.03.12.20027185"},{"key":"9_CR75","unstructured":"Xiaowei, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L.: Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia (2020). arXiv preprint . arXiv:2002.09334"},{"key":"9_CR76","doi-asserted-by":"crossref","unstructured":"Sethy, P.K., Behera, S.K.: Detection of Coronavirus Disease (COVID-19) Based on Deep Features (2020)","DOI":"10.20944\/preprints202003.0300.v1"},{"key":"9_CR77","doi-asserted-by":"publisher","unstructured":"Zhang, H., Saravanan, K.M., Yang, Y., Hossain, M.T., Li, J., Ren, X., Wei, Y.: Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov (2020). Preprints 2020, 2020020061. https:\/\/doi.org\/10.20944\/preprints202002.0061.v1","DOI":"10.20944\/preprints202002.0061.v1"},{"key":"9_CR78","unstructured":"Computational predictions of protein structures associated with COVID-19. https:\/\/deepmind.com\/research\/open-source\/computational-predictions-of-protein-structures-associated-with-COVID-19"}],"container-title":["Studies in Systems, Decision and Control","Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-63307-3_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T13:35:01Z","timestamp":1619271301000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-63307-3_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030633066","9783030633073"],"references-count":78,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-63307-3_9","relation":{},"ISSN":["2198-4182","2198-4190"],"issn-type":[{"type":"print","value":"2198-4182"},{"type":"electronic","value":"2198-4190"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"11 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}