Zobrazeno 1 - 10
of 358
pro vyhledávání: '"Zahran, Mohamed A"'
This work proposes an unsupervised learning framework for trajectory (sequence) outlier detection that combines ranking tests with user sequence models. The overall framework identifies sequence outliers at a desired false positive rate (FPR), in an
Externí odkaz:
http://arxiv.org/abs/2111.03808
Autor:
Zahran, Mohamed Aly, Manas-Ojeda, Aroa, Navarro-Sánchez, Mónica, Castillo-Gómez, Esther, Olucha-Bordonau, Francisco E.
Publikováno v:
In Heliyon 15 September 2024 10(17)
Publikováno v:
Review of Economics and Political Science, 2019, Vol. 8, Issue 6, pp. 520-539.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/REPS-01-2019-0009
Since its invention, Generative adversarial networks (GANs) have shown outstanding results in many applications. Generative Adversarial Networks are powerful yet, resource-hungry deep-learning models. Their main difference from ordinary deep learning
Externí odkaz:
http://arxiv.org/abs/2108.06626
Autor:
Harraz, Ahmed M., Elkarta, Ahmed, Zahran, Mohamed H., Mosbah, Ahmed, Shaaban, Atallah A., Abol-Enein, Hassan
Publikováno v:
In Asian Journal of Urology April 2024 11(2):294-303
Autor:
Samir, Ahmed, Abd Elhamid, Abd Elhamid M., Eliwa, Aref, Aboul Zahab, Essam El Din, B. Zahran, Mohamed, Sayed, Mahmoud M.
Publikováno v:
In Journal of Alloys and Compounds 15 January 2024 971
Autor:
Weston, Kevin, Jafanza, Vahid, Kansal, Arnav, Taur, Abhishek, Zahran, Mohamed, Muzahid, Abdullah
Computer applications are continuously evolving. However, significant knowledge can be harvested from a set of applications and applied in the context of unknown applications. In this paper, we propose to use the harvested knowledge to tune hardware
Externí odkaz:
http://arxiv.org/abs/2004.13074
Publikováno v:
Published as a conference paper at the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)
We consider the task of learning a parametric Continuous Time Markov Chain (CTMC) sequence model without examples of sequences, where the training data consists entirely of aggregate steady-state statistics. Making the problem harder, we assume that
Externí odkaz:
http://arxiv.org/abs/2002.04186
Data scarcity is a bottleneck to machine learning-based perception modules, usually tackled by augmenting real data with synthetic data from simulators. Realistic models of the vehicle perception sensors are hard to formulate in closed form, and at t
Externí odkaz:
http://arxiv.org/abs/1911.10575
Advanced sensors are a key to enable self-driving cars technology. Laser scanner sensors (LiDAR, Light Detection And Ranging) became a fundamental choice due to its long-range and robustness to low light driving conditions. The problem of designing a
Externí odkaz:
http://arxiv.org/abs/1907.07748