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000924239 020__ $$a9789811522376$$q(electronic book)
000924239 020__ $$a9811522375$$q(electronic book)
000924239 020__ $$z9789811522369
000924239 035__ $$aSP(OCoLC)on1135665294
000924239 035__ $$aSP(OCoLC)1135665294
000924239 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dLQU
000924239 049__ $$aISEA
000924239 050_4 $$aSH156
000924239 08204 $$a597$$223
000924239 1001_ $$aMohd Razman, Mohd Azraai.
000924239 24510 $$aMachine learning in aquaculture :$$bhunger classification of Lates Calcarifer /$$cMohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian-Antonio Susto, Yukinori Mukai.
000924239 260__ $$aSingapore :$$bSpringer,$$c2020.
000924239 300__ $$a1 online resource (64 pages).
000924239 336__ $$atext$$btxt$$2rdacontent
000924239 337__ $$acomputer$$bc$$2rdamedia
000924239 338__ $$aonline resource$$bcr$$2rdacarrier
000924239 4901_ $$aSpringerBriefs in Applied Sciences and Technology Ser.
000924239 504__ $$aIncludes bibliographical references.
000924239 506__ $$aAccess limited to authorized users.
000924239 520__ $$aThis book highlights the fundamental association between aquaculture and engineering in classifying fish hunger behaviour by means of machine learning techniques. Understanding the underlying factors that affect fish growth is essential, since they have implications for higher productivity in fish farms. Computer vision and machine learning techniques make it possible to quantify the subjective perception of hunger behaviour and so allow food to be provided as necessary. The book analyses the conceptual framework of motion tracking, feeding schedule and prediction classifiers in order to classify the hunger state, and proposes a system comprising an automated feeder system, image-processing module, as well as machine learning classifiers. Furthermore, the system substitutes conventional, complex modelling techniques with a robust, artificial intelligence approach. The findings presented are of interest to researchers, fish farmers, and aquaculture technologist wanting to gain insights into the productivity of fish and fish behaviour.
000924239 588__ $$aDescription based on print version record.
000924239 650_0 $$aFishes$$xFeeding and feeds$$xData processing.
000924239 650_0 $$aMachine learning.
000924239 7001_ $$aMajeed, Anwar P. P. Abdul.
000924239 7001_ $$aMuazu Musa, Rabiu.
000924239 7000_ $$aZahari Taha.
000924239 7001_ $$aSusto, Gian Antonio.
000924239 7001_ $$aMukai, Yukinori.
000924239 77608 $$iPrint version:$$aMohd Razman, Mohd Azraai$$tMachine Learning in Aquaculture : Hunger Classification of Lates Calcarifer$$dSingapore : Springer,c2020$$z9789811522369
000924239 830_0 $$aSpringerBriefs in applied sciences and technology.
000924239 852__ $$bebk
000924239 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-15-2237-6$$zOnline Access$$91397441.1
000924239 909CO $$ooai:library.usi.edu:924239$$pGLOBAL_SET
000924239 980__ $$aEBOOK
000924239 980__ $$aBIB
000924239 982__ $$aEbook
000924239 983__ $$aOnline
000924239 994__ $$a92$$bISE