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1دورية أكاديمية
المؤلفون: Mohamed, Mohab, Noaman , Ahmed, Nour, Islam, Abdel-Hady, Hesham
المصدر: Open Access Macedonian Journal of Medical Sciences; Vol. 11 No. B (2023): B - Clinical Sciences; 592-598 ; 1857-9655
مصطلحات موضوعية: CPAP, Preterm, Nasal injury, Quality improvement
وصف الملف: application/pdf
العلاقة: https://oamjms.eu/index.php/mjms/article/view/11685/8556Test; https://oamjms.eu/index.php/mjms/article/view/11685Test
الإتاحة: https://doi.org/10.3889/oamjms.2023.11685Test
https://oamjms.eu/index.php/mjms/article/view/11685Test -
2مؤتمر
المساهمون: DIOTASOFT, University of Montenegro (UCG), Institut Clément Ader (ICA), Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-IMT École nationale supérieure des Mines d'Albi-Carmaux (IMT Mines Albi), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)
المصدر: 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022), Lecture Notes in Networks and Systems. - ISBN 978-3-031-18049-1
SOCO 2022 - 17th International Conference on Soft Computing Models in Industrial and Environmental Applications
https://imt-mines-albi.hal.science/hal-03813066Test
SOCO 2022 - 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, Sep 2022, Salamanque, Spain. p. 340-349, ⟨10.1007/978-3-031-18050-7_33⟩مصطلحات موضوعية: [SPI]Engineering Sciences [physics]
جغرافية الموضوع: Salamanque, Spain
العلاقة: hal-03813066; https://imt-mines-albi.hal.science/hal-03813066Test; https://imt-mines-albi.hal.science/hal-03813066/documentTest; https://imt-mines-albi.hal.science/hal-03813066/file/Image-Classification-Applied-To-The-Problem-Of-Conformity-Check-In-Industry.pdfTest
الإتاحة: https://doi.org/10.1007/978-3-031-18050-7_33Test
https://imt-mines-albi.hal.science/hal-03813066Test
https://imt-mines-albi.hal.science/hal-03813066/documentTest
https://imt-mines-albi.hal.science/hal-03813066/file/Image-Classification-Applied-To-The-Problem-Of-Conformity-Check-In-Industry.pdfTest -
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4دورية أكاديمية
المؤلفون: Abubakr, Abdelrahman, Jovančević, Igor, Mokhtari, Nour Islam, Ben Abdallah, Hamdi, Orteu, Jean-José
المساهمون: DIOTA, University of Montenegro (UCG), Institut Clément Ader (ICA), Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-IMT École nationale supérieure des Mines d'Albi-Carmaux (IMT Mines Albi), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)
المصدر: ISSN: 1017-9909.
مصطلحات موضوعية: Deep learning, Domain adaptation, Domain randomization, Augmented autoencoders, Synthetic rendering, Industrial visual inspection, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
العلاقة: hal-03712960; https://imt-mines-albi.hal.science/hal-03712960Test; https://imt-mines-albi.hal.science/hal-03712960/documentTest; https://imt-mines-albi.hal.science/hal-03712960/file/Learning-deep-domain-agnostic-features-from-synthetic-renders-for-industrial-visual-inspection.pdfTest
الإتاحة: https://doi.org/10.1117/1.JEI.31.5.051604Test
https://imt-mines-albi.hal.science/hal-03712960Test
https://imt-mines-albi.hal.science/hal-03712960/documentTest
https://imt-mines-albi.hal.science/hal-03712960/file/Learning-deep-domain-agnostic-features-from-synthetic-renders-for-industrial-visual-inspection.pdfTest -
5دورية أكاديمية
المؤلفون: Alanazi, Fahad, Nour, Islam, Hanif, Atif, Al-Ashkar, Ibrahim, Aljowaie, Reem M., Eifan, Saleh
المساهمون: Kothe, Erika, Deanship of Scientific Research, King Saud University
المصدر: PLOS ONE ; volume 17, issue 8, page e0273343 ; ISSN 1932-6203
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6دورية أكاديمية
المؤلفون: Nagy, Mohammed, Nasef, Nehad, Gibreel, Ahmed, Sarhan, Mohamed, Aldomiaty, Hoda, Darwish, Mohammed, Nour, Islam
المصدر: Frontiers in Pediatrics ; volume 9 ; ISSN 2296-2360
مصطلحات موضوعية: Pediatrics, Perinatology and Child Health
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7مؤتمرOn learning deep domain-invariant features from 2D synthetic images for industrial visual inspection
المؤلفون: Abubakr, Abdelrahman G., Jovančević, Igor, Mokhtari, Nour Islam, Ben Abdallah, Hamdi, Orteu, Jean-José
المساهمون: DIOTA, Institut Clément Ader (ICA), Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-IMT École nationale supérieure des Mines d'Albi-Carmaux (IMT Mines Albi), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)
المصدر: Proc. SPIE 11794, Fifteenth International Conference on Quality Control by Artificial Vision, 1179418 (16 July 2021) ; QCAV’2021- 15th International Conference on Quality Control by Artificial Vision ; https://hal.science/hal-03230285Test ; QCAV’2021- 15th International Conference on Quality Control by Artificial Vision, May 2021, Tokushima (online), Japan. 9 p., ⟨10.1117/12.2589040⟩ ; http://www.tc-iaip.org/qcav/2021/callforpapers.htmlTest
مصطلحات موضوعية: Optical inspection, RGB color model, Solid modeling, Cameras, Computer aided design, Convolutional neural networks, Inspection, Mobile robots, [SPI]Engineering Sciences [physics], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
جغرافية الموضوع: Tokushima (online), Japan
العلاقة: hal-03230285; https://hal.science/hal-03230285Test; https://hal.science/hal-03230285/documentTest; https://hal.science/hal-03230285/file/On-learning-deep-domain-invariant-features.pdfTest
الإتاحة: https://doi.org/10.1117/12.2589040Test
https://hal.science/hal-03230285Test
https://hal.science/hal-03230285/documentTest
https://hal.science/hal-03230285/file/On-learning-deep-domain-invariant-features.pdfTest -
8دورية أكاديمية
المؤلفون: Nour, Islam1 (AUTHOR) islam.mohamed@usda.gov, Alvarez-Narvaez, Sonsiray1 (AUTHOR), Harrell, Telvin L.1 (AUTHOR), Conrad, Steven J.1 (AUTHOR), Mohanty, Sujit K.1 (AUTHOR) sujit.mohanty@usda.gov
المصدر: Viruses (1999-4915). Nov2023, Vol. 15 Issue 11, p2191. 23p.
مصطلحات موضوعية: *AMINO acid analysis, *AMINO acid sequence, *GENETIC variation, *CHICKEN industry, *BROILER chickens, *COMPARATIVE genomics
مصطلحات جغرافية: NORTH Carolina
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9دورية أكاديمية
المؤلفون: Nour, Islam, Hanif, Atif, Ryan, Martin Denis, Eifan, Saleh
المصدر: Nour , I , Hanif , A , Ryan , M D & Eifan , S 2021 , ' Insights into gastrointestinal virome : etiology and public exposure ' , Water , vol. 13 , no. 19 , 2794 . https://doi.org/10.3390/w13192794Test
مصطلحات موضوعية: Virome, Wastewater, Etiology, Viral gastroenteritis, Exposure
وصف الملف: application/pdf
العلاقة: https://research-portal.st-andrews.ac.uk/en/researchoutput/insights-into-gastrointestinal-viromeTest(6432ba2f-fe3f-4fec-8304-d0be9d0f769d).html
الإتاحة: https://doi.org/10.3390/w13192794Test
https://research-portal.st-andrews.ac.uk/en/researchoutput/insights-into-gastrointestinal-viromeTest(6432ba2f-fe3f-4fec-8304-d0be9d0f769d).html
https://research-repository.st-andrews.ac.uk/bitstream/10023/24100/1/Nour_2021_Insights_into_gastrointestinal_virome_Water_13_02794_CCBY.pdfTest -
10دورية أكاديمية
المؤلفون: Nour, Islam, Nasef, Nehad, Abdel‐Hady, Hesham
المصدر: Acta Paediatrica ; volume 111, issue 1, page 196-197 ; ISSN 0803-5253 1651-2227