| 000 | 01674nam a2200253 4500 | ||
|---|---|---|---|
| 003 | EG-NbEJU | ||
| 005 | 20241201113133.0 | ||
| 008 | 240627b2023 ua a | m||| 00| 0 eng d | ||
| 040 |
_aEG-NbEJU _beng _cEG-NbEJU _dEG-NbEJU |
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| 041 |
_aeng _bara |
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| 100 | 1 | _aMahmoud , Mahmoud Ashraf Mohamed | |
| 245 | 1 | 2 |
_aA Proposed Hybrid Deep Learning - Based Cognitive Digital Twin for Enhancing Supply Chain Resilience : _bA Thesis Submitted to the Graduate School of Innovative Design Engineering : Egypt - Japan University of Science and Technology (E - JUST) : In Partial Fulfilment of the Requirements for the Degree of Master of Science in Industrial Engineering and Systems Management / _cby Mahmoud Ashraf Mohamed Mahmoud |
| 246 | 1 | 5 |
_aمقترح لتوأم رقمي إدراكي قائم على التعلم العميق الهجين لتعزيز مرونة سلسلة الإمداد / _bمقدمة من محمود أشرف محمد محمود للحصول على درجة ماجستير العلوم في الهندسة الصناعية و إدارة النظم |
| 260 |
_aAlexandria : _bMahmoud Ashraf Mohamed Mahmoud _c2023 |
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| 300 |
_aleaves 87 : _b(illustrations (some color ; _c30 cm |
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| 500 | _aIncludes a title page in Arabic | ||
| 502 |
_aThesis (M.Sc.) _bMaster _cEgypt - Japan University of Science and Technology (E-JUST) - School of Innovative Design Engineering - Industrial Engineering and Systems Management Department _d2023 |
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| 520 | _a. | ||
| 530 | _aIssued also as a digital file (for more information please check our Digital Repository) | ||
| 590 |
_aIDE _bIME |
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| 901 | _aHaGeR | ||
| 902 | _aTH_02_ (271) | ||
| 942 |
_2lcc _n0 _cDISS |
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| 999 |
_c5797 _d5797 |
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