Predicting the Future : Big Data and Machine Learning

Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information,...

Full description

Saved in:
Bibliographic Details
Sonstige:
Year of Publication:2020
Language:English
Physical Description:1 electronic resource (148 p.)
Tags: Add Tag
No Tags, Be the first to tag this record!
id 993546003404498
ctrlnum (CKB)5400000000045348
(oapen)https://directory.doabooks.org/handle/20.500.12854/68909
(EXLCZ)995400000000045348
collection bib_alma
record_format marc
spelling Sánchez Lasheras, Fernando edt
Predicting the Future Big Data and Machine Learning
Predicting the Future
Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
1 electronic resource (148 p.)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings.
English
History of engineering & technology bicssc
3-03936-619-X
3-03936-620-3
Sánchez Lasheras, Fernando oth
language English
format eBook
author2 Sánchez Lasheras, Fernando
author_facet Sánchez Lasheras, Fernando
author2_variant l f s lf lfs
author2_role Sonstige
title Predicting the Future Big Data and Machine Learning
spellingShingle Predicting the Future Big Data and Machine Learning
title_sub Big Data and Machine Learning
title_full Predicting the Future Big Data and Machine Learning
title_fullStr Predicting the Future Big Data and Machine Learning
title_full_unstemmed Predicting the Future Big Data and Machine Learning
title_auth Predicting the Future Big Data and Machine Learning
title_alt Predicting the Future
title_new Predicting the Future
title_sort predicting the future big data and machine learning
publisher MDPI - Multidisciplinary Digital Publishing Institute
publishDate 2020
physical 1 electronic resource (148 p.)
isbn 3-03936-619-X
3-03936-620-3
illustrated Not Illustrated
work_keys_str_mv AT sanchezlasherasfernando predictingthefuturebigdataandmachinelearning
AT sanchezlasherasfernando predictingthefuture
status_str n
ids_txt_mv (CKB)5400000000045348
(oapen)https://directory.doabooks.org/handle/20.500.12854/68909
(EXLCZ)995400000000045348
carrierType_str_mv cr
is_hierarchy_title Predicting the Future Big Data and Machine Learning
author2_original_writing_str_mv noLinkedField
_version_ 1796648786790973441
fullrecord <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01921nam-a2200301z--4500</leader><controlfield tag="001">993546003404498</controlfield><controlfield tag="005">20231214133114.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr|mn|---annan</controlfield><controlfield tag="008">202105s2020 xx |||||o ||| 0|eng d</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(CKB)5400000000045348</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(oapen)https://directory.doabooks.org/handle/20.500.12854/68909</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EXLCZ)995400000000045348</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sánchez Lasheras, Fernando</subfield><subfield code="4">edt</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Predicting the Future</subfield><subfield code="b">Big Data and Machine Learning</subfield></datafield><datafield tag="246" ind1=" " ind2=" "><subfield code="a">Predicting the Future </subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="a">Basel, Switzerland</subfield><subfield code="b">MDPI - Multidisciplinary Digital Publishing Institute</subfield><subfield code="c">2020</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 electronic resource (148 p.)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">English</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">History of engineering &amp; technology</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">3-03936-619-X</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">3-03936-620-3</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sánchez Lasheras, Fernando</subfield><subfield code="4">oth</subfield></datafield><datafield tag="906" ind1=" " ind2=" "><subfield code="a">BOOK</subfield></datafield><datafield tag="ADM" ind1=" " ind2=" "><subfield code="b">2023-12-15 05:43:06 Europe/Vienna</subfield><subfield code="f">system</subfield><subfield code="c">marc21</subfield><subfield code="a">2022-04-04 09:22:53 Europe/Vienna</subfield><subfield code="g">false</subfield></datafield><datafield tag="AVE" ind1=" " ind2=" "><subfield code="i">DOAB Directory of Open Access Books</subfield><subfield code="P">DOAB Directory of Open Access Books</subfield><subfield code="x">https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&amp;portfolio_pid=5338063610004498&amp;Force_direct=true</subfield><subfield code="Z">5338063610004498</subfield><subfield code="b">Available</subfield><subfield code="8">5338063610004498</subfield></datafield></record></collection>