- Mortal kombat project 4.1 season 2 final additions movie#
- Mortal kombat project 4.1 season 2 final additions series#
- Mortal kombat project 4.1 season 2 final additions tv#
Early games in this series were also noted for their realistic digitized sprites and an extensive use of palette swapping to create new characters. Controversies surrounding Mortal Kombat, in part, led to the creation of the ESRB video game rating system. The series has a reputation for high levels of graphic violence, including, most notably, its Fatalities (finishing moves allowing the player to finish off their defeated opponent). Mortal Kombat has become the most successful fighting franchise in the history of video games and one of the highest-grossing media franchises of all time.
Mortal kombat project 4.1 season 2 final additions tv#
Lawrence also produced the second movie, animated TV series, live-action TV series films, the first one million platinum-selling album and a live-action tour.
Mortal kombat project 4.1 season 2 final additions movie#
Film producer Lawrence Kasanoff licensed the rights to the game in the early 1990s and produced the first hit movie ever made from a video game. The original game has spawned many sequels and spin-offs consisting of several action-adventure games, as well as a comic book series and a card game. Mortal Kombat was the first ever fighting game to introduce a secret fighter, reached if the player fulfilled a set of requirements. The development of the first game was originally based on an idea that Ed Boon and John Tobias had of making a video game starring Jean-Claude Van Damme, but as that idea fell through, a fantasy-themed fighting game was created instead, nonetheless paying homage to him with nut-cracking movie star character Johnny Cage, whose initials and personal style echo Van Damme's. Springer, Cham (2020).Mortal Kombat is an American media franchise centered on a series of video games, originally developed by Midway Games in 1992.
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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