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| 008 | 150316s2014 ua k f m 000 0 eng d | ||
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_aEG-GiCUC _beng _cEG-GiCUC |
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| 041 | 0 | _aeng | |
| 049 | _aDeposite | ||
| 097 | _aPh.D | ||
| 099 | _aCai01.18.02.Ph.D.2014.Ah.N | ||
| 100 | 0 | _aAhmed Taisser Shawky | |
| 245 | 1 | 2 |
_aA novel Modular form of rough decision models / _cAhmed Taisser Shawky ; Supervised Ashraf H. Abdelwahab , Hesham A. Hefny |
| 246 | 1 | 5 | _aشكل وحداتى جديد لنماذج إتخاذ القرار التقريبية |
| 260 |
_aCairo : _bAhmed Taisser Shawky , _c2014 |
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_a192 Leaves : _bforms ; _c30cm |
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| 502 | _aThesis (Ph.D.) - Cairo University - Institute of Statistical Studies and Researches - Department of Computer and Information Sciences | ||
| 520 | _aMany real world applications need to deal with huge amount of data. Therefore, there is a need for new techniques which can manage the data with such magnitude. Also, the variety of decision makers and the variance of their visions can cause inconsistency in decisions. Modularity techniques are appropriate for dealing with complexity of data to support decision makers. The difference in visions of decision makers requires dealing with data in the framework of inaccuracy. Computational Intelligence (CI) techniques like genetic algorithms, neural networks, and fuzzy logic are effective for dealing with imprecise data to support decision makers. Now using rough sets is getting quite necessary to be used for its ability to mining such type of data | ||
| 530 | _aIssued also as CD | ||
| 653 | 4 | _aDecision models | |
| 653 | 4 | _aNovel Modular | |
| 653 | 4 | _aRough decision models | |
| 700 | 0 |
_aAshraf Hassan Abdelwahab , _eSupervisor |
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| 700 | 0 |
_aHesham Ahmed Hefny , _eSupervisor |
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| 856 | _uhttp://172.23.153.220/th.pdf | ||
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_aNazla _eRevisor |
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_aSoheir _eCataloger |
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_2ddc _cTH |
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_c49864 _d49864 |
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