Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Mohamed H. Essa"'
Autor:
Mohamed H. Essa
Publikováno v:
Journal of General Union of Arab Archaeologists, Vol 25, Iss 2, Pp 15-42 (2024)
الملخص : لا تزال برديات سرقة المقابر الملكية بحاجة لمزيد من الدراسات المعمقة؛ لأنها تعد أفضل مثال حي لممارسة القانون الجنائي في مصر القد
Externí odkaz:
https://doaj.org/article/75bdbbed62a1484892c2ea8896dd8c79
Autor:
Mohamed H. Essa
Publikováno v:
Journal of General Union of Arab Archaeologists, Vol 23, Iss 1, Pp 241-266 (2022)
المرأة في قوانين الميراث في مصر القديمة تميزت المرأة في مصر القديمة عن أقرانها في الحضارات القديمة الأخرى أنها كانت ندا قويا للرجل أمام
Externí odkaz:
https://doaj.org/article/742083259d624ae5b34890cf4980f1c3
Autor:
Mohamed A. Mohamed, Hassan A. Hassan, Mohamed H. Essai, Hamada Esmaiel, Ahmed S. Mubarak, Osama A. Omer
Publikováno v:
EURASIP Journal on Wireless Communications and Networking, Vol 2023, Iss 1, Pp 1-26 (2023)
Abstract The most important function of the deep learning (DL) channel equalization and symbol detection systems is the ability to predict the user’s original transmitted data. Generally, the behavior and performance of the deep artificial neural n
Externí odkaz:
https://doaj.org/article/0df9b6200be04736b6248b7077029c39
Autor:
Hassan A. Hassan, Mohamed A. Mohamed, Mohamed H. Essai, Hamada Esmaiel, Ahmed S. Mubarak, Osama A. Omer
Publikováno v:
JES: Journal of Engineering Sciences, Vol 51, Iss 6, Pp 32-48 (2023)
Orthogonal frequency division multiplexing (OFDM) wireless systems rely heavily on channel state estimation (CSE) to mitigate the effects of multipath channel fading. Achieving a high data rate with OFDM technology requires efficient CSE and accurate
Externí odkaz:
https://doaj.org/article/1d2921df311148769afe13c7a04dea26
Autor:
Mohamad Abou Houran, Mohamed H. Essai Ali, Adel B. Abdel-Raman, Eman A. Badry, Alaaeldien Hassan, Hany A. Atallah
Publikováno v:
IEEE Access, Vol 11, Pp 49863-49873 (2023)
In this paper, we suggest improving the performance of developed activation function-based Deep Learning Long Short-Term Memory (DLLSTM) structures by employing robust loss functions like Mean Absolute Error $(MAE)$ and Sum Squared Error $(SSE)$ to c
Externí odkaz:
https://doaj.org/article/b6e383cfa5144783a30e85c3adb3d8c3
Publikováno v:
IEEE Access, Vol 10, Pp 97259-97275 (2022)
This study proposes novel Long Short-Term Memory (LSTM)-based classifiers through developing the internal structure of LSTM neural networks using 26 state activation functions as alternatives to the traditional hyperbolic tangent (tanh) activation fu
Externí odkaz:
https://doaj.org/article/a9d50359ffb740a2bdf8a29be5578942
Publikováno v:
Water, Air, and Soil Pollution. 109:429-442
The efficiency of nitrite removal in an electrochemical cell was investigated in this study using stainless steel electrodes. The experiments were designed to study the effects of current input, volume of the solution, initial pH, and number of elect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b5ec03266130d63b9c4049874552d4e8
https://doi.org/10.1023/a:1005024012610
https://doi.org/10.1023/a:1005024012610
Publikováno v:
Applied Sciences, Vol 12, Iss 19, p 10030 (2022)
Artificial intelligence (AI) is capable of addressing the complexities and difficulties of fifth-generation (5G) mobile networks and beyond. In this paradigm, it is important to predict network metrics to meet future network requirements. Vehicle-to-
Externí odkaz:
https://doaj.org/article/24156af1da1a4182a362fc22458474b4