Titlebook: Machine Learning in Aquaculture; Hunger Classificatio Mohd Azraai Mohd Razman,Anwar P. P. Abdul Majeed,Y Book 2020 The Author(s), under exc

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書目名稱Machine Learning in Aquaculture影響因子(影響力)




書目名稱Machine Learning in Aquaculture影響因子(影響力)學(xué)科排名




書目名稱Machine Learning in Aquaculture網(wǎng)絡(luò)公開度




書目名稱Machine Learning in Aquaculture網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning in Aquaculture被引頻次




書目名稱Machine Learning in Aquaculture被引頻次學(xué)科排名




書目名稱Machine Learning in Aquaculture年度引用




書目名稱Machine Learning in Aquaculture年度引用學(xué)科排名




書目名稱Machine Learning in Aquaculture讀者反饋




書目名稱Machine Learning in Aquaculture讀者反饋學(xué)科排名

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SpringerBriefs in Applied Sciences and Technologyhttp://image.papertrans.cn/m/image/620657.jpg

Moderate

Concluding Remarks,g any vital information, including the feeding time, the feeder cause between the hungry and the happy, and the most relevant, extracting the notable features of the fish movement behaviours across the test. The contribution and future work shall be drawn up in conjunction with the goals reached in this report.

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JOT

Machine Learning in Aquaculture978-981-15-2237-6Series ISSN 2191-530X Series E-ISSN 2191-5318

Orchiectomy

Image Processing Features Extraction on Fish Behaviour,ely the discriminant analysis (DA), support vector machine (SVM) and .-nearest neighbour (.-NN). The outcome in this chapter will validate the features from image processing as a tool for identifying the behavioural changes of the fish in school size.

FIR

Time-Series Identification on Fish Feeding Behaviour,max rotation. The details were then categorized using support vector machine (SVM), K-NN and random forest tree (RF) classifiers. The best identification accuracy was shown with eight described features in the varimax-based PCA. The forecast results based on the K-NN model built on selected data cha

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f needs and preferences prevents him from explaining cooperation as a voluntary and spontaneous social phenomenon. In conclusion, the chapter maintains that less formal approaches to needs and preferences are imperative in light of the climate and ecological crises and allow us to examine the pros a

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