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Artificial Intelligence in Health                            Federated learning health stack against pandemics



            37.  Wang L, Lin  ℤQ, Wong A. COVID-Net: A  tailored   In:  IEEE Conference on Computer Vision and Pattern
               deep  convolutional  neural  network  design  for detection   Recognition. United States: IEEE; 2016. p. 2921-2929.
               of  COVID-19  cases  from  chest  X-ray  images.  Sci      doi: 10.1109/CVPR.2016.319
               Rep. 2020;10(1):19549.
                                                               48.  Scharfman J. Decentralized autonomous organization (DAO)
               doi: 10.1038/s41598-020-76550-z
                                                                  fraud, hacks, and controversies. In: The Cryptocurrency and
            38.  Wang S, Kang B, Ma J, et al. A deep learning algorithm using   Digital Asset Fraud Casebook  DeFi, NFTs, DAOs, Meme
               CT images to screen for corona virus disease (COVID-19).   Coins, and Other Digital Asset Hacks. Vol. 2. Berlin: Springer;
               Eur Radiol. 2021;31:6096-6104.                     2024. p. 65-106.
               doi: 10.1007/s00330-021-07715-1                    doi: 10.1007/978-3-031-60836-0_3
            39.  Joaquin AS. Using Deep Learning to Detect NCOV-19 from   49.  Destefanis G, Marchesi M, Ortu M, Tonelli R, Bracciali A,
               X-Ray Images. Medium; 2020. Available from: https://  Hierons R. Smart contracts vulnerabilities: A  call for
               towardsdatascience.com/using/deep/learning/to/detect/  blockchain software engineering? In: International Workshop
               ncov/19/from/x/ray/images/1a89701d1acd [Last accessed   on Blockchain Oriented Software Engineering (IWBOSE).
               on 2024 Dec 18].                                   United States: IEEE; 2018. p. 19-25.
            40.  Gogineni AK, Kishore R, Raj P, Naik S, Sahu KK.      doi: 10.1109/IWBOSE.2018.8327567
               Unsupervised Clustering algorithm as region of interest
               proposals for cancer detection using CNN. In: Computational   50.  Gogineni AK, Swayamjyoti S, Sahoo D, Sahu KK, Kishore R.
               Vision and Bio-Inspired Computing. Coimbatore: ICCVBIC;   Multi-Class classification of vulnerabilities in Smart
               2019. p. 1386-1396.                                Contracts using AWD-LSTM, with pre-trained encoder
                                                                  inspired from natural  language processing.  IOP SciNotes.
               doi: 10.1007/978-3-030-37218-7_146                 2020;1(3):035002.
            41.  Kishore R, Gogineni AK, Nussinov  ℤ, Sahu KK.      doi: 10.1088/2633-1357/abcd29
               A  nature inspired  modularity function for  unsupervised
               learning involving spatially embedded networks.  Sci   51.  Merity S, Keskar NS, Socher R.  Regularizing and
               Rep. 2019;9(1):2631.                               Optimizing LSTM Language Models. United States: Cornell
                                                                  University; 2017.
               doi: 10.1038/s41598-019-39180-8
                                                                  doi: 10.48550/arXiv.1708.02182
            42.  Kishore R, Krishnan R, Satpathy M, Nussinov ℤ, Sahu KK.
               Abstraction of meso-scale network architecture in granular   52.  Howard J, Ruder S. Universal Language Model Fine-Tuning for
               ensembles using ‘big data analytics’ tools. J Phys Commun.   Text Classification. United States: Harvard University; 2018.
               2018;2(3):031004.                                  doi: 10.48550/arXiv.1801.06146
               doi: 10.1088/2399-6528/aab386                   53.  Collins L, Hassani H, Mokhtari A, Shakkottai S. Fedavg with
            43.  Uijlings JR, Van De Sande KE, Gevers T, Smeulders AW.   fine tuning: Local updates lead to representation learning.
               Selective search for object recognition. Int J Comput Vision.   In:  Advances in Neural Information Processing Systems.
               2013;104:154-171.                                  Vol. 35. United States: MIT Press; 2022. p. 10572-10586.
               doi: 10.1007/s11263-013-0620-5                     doi: 10.48550/arXiv.2205.13692
            44.  Van  Ginneken  B,  Jacobs  C.  LUNA16.  ℤenodo;  2019.   54.  Beutel DJ, Topal T, Mathur A,  et al.  Flower: A  Friendly
               Available from: https://luna16.grand-challenge.org/ [Last   Federated Learning Research Framework. United  States:
               accessed on 2024 May 20].                          Cornell University; 2020.
               doi: 10.5281/zenodo.2595813                        doi: 10.48550/arXiv.2007.14390
            45.  He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. In:   55.  Benaissa A, Retiat B, Cebere B, Belfedhal AE.  Tenseal:
               IEEE International Conference on Computer Vision (ICCV).   A  Library for Encrypted Tensor Operations Using
               United States: IEEE; 2017. p. 2980-2988.           Homomorphic  Encryption.  United  States:  Cornell
                                                                  University; 2021.
               doi: 10.48550/arXiv.1703.06870
                                                                  doi: 10.48550/arXiv.2104.03152
            46.  Rother C, Kolmogorov V, Blake A. GrabCut interactive
               foreground extraction using iterated graph cuts. ACM Trans   56.  Sathishkumar P, Pugalarasan K, Ponnparamaguru C,
               Graph (TOG). 2004;23(3):309-314.                   Vasanthkumar M. Improving healthcare data security using
                                                                  cheon-kim-kim-song (ckks) homomorphic encryption. In:
               doi: 10.1145/3596711.3596774
                                                                  2024 International Conference on Knowledge Engineering
            47.  ℤhou  B,  Khosla  A,  Lapedriza  A,  Oliva  A,  Torralba  A.   and  Communication  Systems. Vol.  1. United States: IEEE;
               Learning deep features for discriminative localization.   2024. p. 1-6.


            Volume 2 Issue 4 (2025)                         90                          doi: 10.36922/AIH025080013
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