![]() ![]() This version also introduced several new costumes for the existing playable characters. Revision 3 added a new second Fatality for every character, further fixed existing bugs left over in Revision 2, changed more character weapons and added the secret character Meat as an alternate skin to the selectable characters. ![]() The final version of MK4 to appear in arcades was Revision 3. Several characters receive new weapons, such as Sub-Zero's Ice Scepter. ![]() Revision 2 was released some time later, bringing back Johnny Cage and Jax while introducing Reiko, as well as editing Sub-Zero's appearance to include his scar and changing his 2p outfit to a look resembling his MK3 appearance. The game features no endings and no final boss. In this version, Noob Saibot was made unselectable, but Kai, Reptile, Jarek and Tanya were added to the selectable characters while retaining 3 "?" character slots. The first version of the game to officially enter arcades was Revision 1. Each character had a weapon and one Fatality. The selectable characters were comprised of Shinnok, Fujin, Scorpion, Raiden, Sonya, Liu Kang, Sub-Zero (masked but missing his scar), Quan Chi and Noob Saibot, along with 6 "?" character slots. The game was noticeably incomplete, featuring many bugs and a relatively small selection of playable characters with few or no special moves. The first incarnation of the game, the "Road Tour" incarnation, was toured around America to hype the game's official release. Springer, Cham (2020).Mortal Kombat 4 saw a handful of revisions in its arcade lifetime. In: Hassanien, A.-E., Azar, A.T., Gaber, T., Oliva, D., Tolba, F.M. Hamed, G., Marey, M.A.E.-R., Amin, S.E.-S., Tolba, M.F.: Deep learning in breast cancer detection and classification. Zalluhoglu, C., Ikizler-Cinbis, N.: Comparison of 2D and 3D attention mechanisms for human (collective) activity recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. IEEE (2021)įukui, H., Hirakawa, T., Yamashita, T., Fujiyoshi, H.: Attention branch network: learning of attention mechanism for visual explanation. In: 2021 International Conference of Women in Data Science at Taif University (WiDSTaif), pp. Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely Connected Convolutional Networks (2018)īerrimi, M., Hamdi, S., Cherif, R.Y., Moussaoui, A., Oussalah, M., Chabane, M.: COVID-19 detection from XRAY and CT scans using transfer learning. He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition (2015) ![]() Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition (2015) (eds.) Recent Trends and Advances in Artificial Intelligence and Internet of Things. In: Balas, V.E., Kumar, R., Srivastava, R. Ghosh, A., Sufian, A., Sultana, F., Chakrabarti, A., De, D.: Fundamental concepts of convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Pereira, R.M., Bertolini, D., Teixeira, L.O., Silla, C.N., Jr., Costa, Y.M.: COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. 40(4), 1391–1405 (2020)Ĭohen, J.P., Morrison, P., Dao, L., Roth, K., Duong, T.Q., Ghassemi, M.: COVID-19 image data collection: prospective predictions are the future. Jain, G., Mittal, D., Thakur, D., Mittal, M.K.: A deep learning approach to detect COVID-19 coronavirus with X-ray images. Hussain, E., Hasan, M., Rahman, M.A., Lee, I., Tamanna, T., Parvez, M.Z.: Corodet: a deep learning based classification for COVID-19 detection using chest X-ray images. 104, 107184 (2021)Īpostolopoulos, I.D., Mpesiana, T.A.: COVID-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Kedia, P., Katarya, R., et al.: CoVNet-19: A deep learning model for the detection and analysis of COVID-19 patients. Kassania, S.H., Kassanib, P.H., Wesolowskic, M.J., Schneidera, K.A., Detersa, R.: Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: a machine learning based approach. Maguolo, G., Nanni, L.: A critic evaluation of methods for COVID-19 automatic detection from X-ray images. Zu, Z.Y., et al.: Coronavirus disease 2019 (COVID-19): a perspective from china. Wang, C., Horby, P.W., Hayden, F.G., Gao, G.F.: A novel coronavirus outbreak of global health concern. ![]()
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