Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Omar Alfarisi"'
Autor:
Omar Alfarisi, Zeyar Aung
Can the machine judge autonomously and solve all challenges or objectives optimally? Has the machine an autonomous route to select the optimal algorithm and hardware for a particular application that achieves the utmost accuracy and efficiency? Despi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::40ac563df8e89e0a6aa666d37428da11
https://doi.org/10.31219/osf.io/ycbxs
https://doi.org/10.31219/osf.io/ycbxs
Autor:
Omar Alfarisi
The cloud service has emerged as one of the 21st-century novel Storage as a Service (SaaS) solutions that eased access to digital storage as needed, with zero maintenance and management efforts on the end users. However, multiple options are currentl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b2177a30ca81c2afd4ca763afc0698fb
https://doi.org/10.31219/osf.io/x7hft
https://doi.org/10.31219/osf.io/x7hft
Autor:
Omar Alfarisi, Aikifa Raza, Hongtao Zhang, Djamel Ozzane, Mohamed Sassi, Hongxia Li, TieJun Zhang
Permeability has a dominant influence on the flow behavior of a natural fluid, and without proper quantification, biological fluids (hydrocarbons) and water resources become waste. During the first decades of the 21st century, permeability quantifica
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::66b70349f49b9247bae4ebcaeece52a2
https://doi.org/10.21203/rs.3.rs-1727412/v1
https://doi.org/10.21203/rs.3.rs-1727412/v1
Autor:
Omar Alfarisi, Zeyar Aung, Qingfeng Huang, Ashraf Al-Khateeb, Hamed Alhashmi, Mohamed Abdelsalam, Salem Alzaabi, Haifa Alyazeedi, Anthony Tzes
Planetary exploration depends heavily on 3D image data to characterize the static and dynamic properties of the rock and environment. Analyzing 3D images requires many computations, causing efficiency to suffer lengthy processing time alongside large
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7756290e724691a56ba3dbb0bc98353a
https://doi.org/10.36227/techrxiv.19412546
https://doi.org/10.36227/techrxiv.19412546
Wettability is one of the critical physical-chemical properties controlling multiphase flow in porous media. Therefore, it is vital to identify the wettability for each rock type when building 3D geological models for predicting the fluid flow behavi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e13f328d3804fc1e7dadcd03b4f5c2a
https://ecsarxiv.org/xmz6e
https://ecsarxiv.org/xmz6e
Researchers have used NMR to measure multi-phase fluid saturation and distribution inside porousmedia of natural rock. However, the NMR signal amplitude suffers reduction with the increase of temperature. The main reason is the Transverse Overhauser
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::edb7239d31f151e09b6ef2b47aa9b780
For defining the optimal machine learning algorithm, the decision was not easy for which we shall choose. To help future researchers, we describe in this paper the optimal among the best of the algorithms. We built a synthetic data set and performed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::731c9ac4ee44311ddb46881af9873e80
https://doi.org/10.36227/techrxiv.17162147.v1
https://doi.org/10.36227/techrxiv.17162147.v1
Morphology Decoder to Predict Heterogeneous Rock Permeability with Machine Learning Guided 3D Vision
Autor:
TieJun Zhang, Hamdan Alhammadi, Hamed Alhashmi, Khalil Ibrahim, Hongxia Li, Hongtao Zhang, Mohamed Sassi, Djamel Ouzzane, Aikifa Raza, Omar Alfarisi
Permeability has a dominant influence on the flow behavior of a natural fluid, and without proper quantification, biological fluids (Hydrocarbons) and water resources become waste. During the first decades of the 21st century, permeability quantifica
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3270f3c0488b43802712b588ccee9436
https://doi.org/10.36227/techrxiv.17126567
https://doi.org/10.36227/techrxiv.17126567
Automated image processing algorithms can improve the quality, efficiency, and consistency of classifying the morphology of heterogeneous carbonate rock and can deal with a massive amount of data and images seamlessly. Geoscientists and petroleum eng
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f09e68d769f8a648c91e1d850ec291fa
https://doi.org/10.36227/techrxiv.16961551
https://doi.org/10.36227/techrxiv.16961551
For defining the optimal machine learning algorithm, the decision was not easy for which we shall choose. To help future researchers, we describe in this paper the optimal among the best of the algorithms. We built a synthetic data set and performed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a3a0c2e1bf1a93f1891c8b45b77d21d
http://arxiv.org/abs/2111.05558
http://arxiv.org/abs/2111.05558