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,...
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Format: | Electronic Book Chapter |
Language: | English |
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Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2020
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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100 | 1 | |a Sánchez Lasheras, Fernando |4 edt | |
700 | 1 | |a Sánchez Lasheras, Fernando |4 oth | |
245 | 1 | 0 | |a Predicting the Future |b Big Data and Machine Learning |
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506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |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. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a History of engineering & technology |2 bicssc | |
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/2676 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/68909 |7 0 |z DOAB: description of the publication |