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00000cam a2200000 a 4500 |
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ocn959031861 |
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OCoLC |
| 005 |
20240909213021.0 |
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m o d |
| 007 |
cr cnu---unuuu |
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160826s2016 enk o 000 0 eng d |
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|a YDX
|b eng
|e pn
|c YDX
|d N$T
|d DG1
|d OCLCQ
|d N$T
|d OCLCQ
|d OCLCF
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|d DG1
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|d DG1
|d OTZ
|d OCLCQ
|d KSU
|d TKN
|d U3W
|d OCLCQ
|d YDX
|d OCLCO
|d OCLCQ
|d OCLCO
|d OCLCL
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| 019 |
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|a 957156755
|a 957615739
|a 957680055
|a 959276221
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|a 9781119347569
|q (electronic bk.)
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|q (electronic bk.)
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|q (electronic bk.)
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|q (electronic bk.)
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|a 9781119347606
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| 020 |
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|a 1119347602
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|z 1848218060
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|z 1119347602
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| 020 |
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|z 9781848218062
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| 035 |
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|a (OCoLC)959031861
|z (OCoLC)957156755
|z (OCoLC)957615739
|z (OCoLC)957680055
|z (OCoLC)959276221
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| 037 |
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|a 949830
|b MIL
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| 050 |
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4 |
|a QA402.5
|b .C53 2016
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| 072 |
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7 |
|a MAT
|x 003000
|2 bisacsh
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| 072 |
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7 |
|a MAT
|x 029000
|2 bisacsh
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| 049 |
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|a HCDD
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| 100 |
1 |
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|a Clarisse Dhaenens,
|e author.
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| 245 |
1 |
0 |
|a Metaheuristics for big data /
|c Clarisse Dhaenens, Laetitia Jourdan.
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| 264 |
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1 |
|a London :
|b London, UK ;
|a Hoboken, NJ :
|b John Wiley & Sons, Inc.,
|c 2016.
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| 300 |
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|a 1 online resource (xvi, 188 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
0 |
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|a Computer engineering series. Metaheuristics set ;
|v volume 5
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| 588 |
0 |
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|a Print version record.
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| 505 |
0 |
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|a Optimization and Big Data -- Metaheuristics : A Short Introduction -- Metaheuristics and Parallel Optimization -- Metaheuristics and Clustering -- Metaheuristics and Association Rules -- Metaheuristics and (Supervised) Classification -- On the Use of Metaheuristics for Feature Selection in Classification -- Frameworks.
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| 520 |
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|a This book deals with the management and valuation of energy storage in electric power grids, highlighting the interest of storage systems in grid applications and developing management methodologies based on artificial intelligence tools. The authors highlight the importance of storing electrical energy, in the context of sustainable development, in "smart cities" and "smart transportation", and discuss multiple services that storing electrical energy can bring. Methodological tools are provided to build an energy management system storage following a generic approach. These tools are based on causal formalisms, artificial intelligence and explicit optimization techniques and are presented throughout the book in connection with concrete case studies.
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| 650 |
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|a Mathematical optimization.
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| 650 |
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|a Big data.
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| 650 |
|
7 |
|a COMPUTERS
|x Computer Engineering.
|2 bisacsh
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| 650 |
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7 |
|a MATHEMATICS
|x Applied.
|2 bisacsh
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| 650 |
|
7 |
|a MATHEMATICS
|x Probability & Statistics
|x General.
|2 bisacsh
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| 650 |
|
7 |
|a Big data
|2 fast
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| 650 |
|
7 |
|a Mathematical optimization
|2 fast
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| 758 |
|
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|i has work:
|a Metaheuristics for big data (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCH8gCcJx4jTvVxHggXT3FC
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a Clarisse Dhaenens.
|t Metaheuristics for big data.
|d London : London, UK ; Hoboken, NJ : John Wiley & Sons, Inc., 2016
|z 1848218060
|z 9781848218062
|w (OCoLC)956340041
|
| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=4648718
|y Click for online access
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| 903 |
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|a EBC-AC
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| 994 |
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|a 92
|b HCD
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