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008			200102s2020 si a o |||| 0|eng d
020			_a9789811522376
024	7		_a10.1007/978-981-15-2237-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aSH156 _b2020 EB
100	1		_aMohd Razman, Mohd Azraai _eautor _9672510
245	1	0	_aMachine Learning in Aquaculture _bHunger Classification of Lates calcarifer _cby Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai.
250			_aFirst edition
264		1	_aSingapore _bSpringer International Publishing _c2020
300			_a1 recurso en línea (VI, 60 páginas)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aSpringerBriefs in Applied Sciences and Technology _x2191-530X
490	0		_aBiomedical and Life Sciences (Springer-11642)
505	0		_a1 Introduction -- 2 Monitoring and feeding integration of demand feeder systems -- 3 Image processing features extraction on fish behaviour -- 4 Time-series identification of fish feeding behaviour.
520	3		_aThis book highlights the fundamental association between aquaculture and engineering in classifying fish hunger behaviour by means of machine learning techniques. Understanding the underlying factors that affect fish growth is essential, since they have implications for higher productivity in fish farms. Computer vision and machine learning techniques make it possible to quantify the subjective perception of hunger behaviour and so allow food to be provided as necessary. The book analyses the conceptual framework of motion tracking, feeding schedule and prediction classifiers in order to classify the hunger state, and proposes a system comprising an automated feeder system, image-processing module, as well as machine learning classifiers. Furthermore, the system substitutes conventional, complex modelling techniques with a robust, artificial intelligence approach. The findings presented are of interest to researchers, fish farmers, and aquaculture technologist wanting to gain insights into the productivity of fish and fish behaviour.
988			_aPrimersemestre_2020_BiomedLife
650		7	_2embne _9672511 _aPeces _xAlimentación
700	1		_aP. P. Abdul Majeed, Anwar _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aMuazu Musa, Rabiu _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9671030
700	1		_aTaha, Zahari _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aSusto, Gian-Antonio _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aMukai, Yukinori _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9789811522369
776	0	8	_iPrinted edition: _z9789811522383
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-15-2237-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2020 _dz _ek _zSI