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008			210724s2022 sz | s |||| 0|eng d
020			_a9783030758479
024	7		_a10.1007/978-3-030-75847-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2022 EB
245	0	0	_aMachine Learning in Industry _cedited by Shubhabrata Datta, J. Paulo Davim
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing, _c2022
300			_a1 recurso en línea (X, 197 páginas) _b83 ilustraciones, 71 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aManagement and Industrial Engineering _x2365-0540
505	0		_aFundamentals of Machine learning -- Neural network model identification studies to predict residual stress of a steel plate based on a non-destructive Barkhausen noise measurement -- Data Driven Optimization of Blast Furnace Iron Making Process Using Evolutionary Deep Learning -- A brief appraisal of machine learning in industrial sensing probes -- Mining the genesis of sliver defects through Rough and Fuzzy Set Theories.
520			_aThis book covers different machine learning techniques such as artificial neural network, support vector machine, rough set theory and deep learning. It points out the difference between the techniques and their suitability for specific applications. This book also describes different applications of machine learning techniques for industrial problems. The book includes several case studies, helping researchers in academia and industries aspiring to use machine learning for solving practical industrial problems.
988			_aSpringer_Engineering_2022
650		7	_2embne _9166090 _aAprendizaje automático
776	0	8	_iPrinted edition: _z9783030758462
776	0	8	_iPrinted edition: _z9783030758486
776	0	8	_iPrinted edition: _z9783030758493
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-75847-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eh _zSI