Mohd Azraai Mohd Razman; Anwar P. P. Abdul Majeed; Rabiu Muazu Musa; Zahari Taha; Gian-Antonio Susto; Yukinori Mukai Springer (2020) Pehmeäkantinen kirja
Mohd Azraai Mohd Razman (ed.); Jessnor Arif Mat Jizat (ed.); Nafrizuan Mat Yahya (ed.); Hyun Myung (ed.); Ama Zainal Abidin Springer (2020) Kovakantinen kirja
Jessnor Arif Mat Jizat (ed.); Ismail Mohd Khairuddin (ed.); Mohd Azraai Mohd Razman (ed.); Ahmad Fakhri Ab. Nasir (ed.); Abd Springer (2021) Pehmeäkantinen kirja
Mohd Azraai Mohd Razman (ed.); Jessnor Arif Mat Jizat (ed.); Nafrizuan Mat Yahya (ed.); Hyun Myung (ed.); Ama Zainal Abidin Springer (2021) Pehmeäkantinen kirja
Muhammad Amirul Abdullah; Ismail Mohd. Khairuddin; Ahmad Fakhri Ab. Nasir; Wan Hasbullah Mohd. Isa; Mohd. Azra Mohd. Razman Springer Verlag, Singapore (2023) Kovakantinen kirja
Muhammad Amirul Abdullah; Ismail Mohd. Khairuddin; Ahmad Fakhri Ab. Nasir; Wan Hasbullah Mohd. Isa; Mohd. Azra Mohd. Razman Springer Verlag, Singapore (2024) Pehmeäkantinen kirja
Rabiu Muazu Musa; Anwar P. P. Abdul Majeed; Muhammad Zuhaili Suhaimi; Mohd Azraai Mohd Razman; Mohamad Razali Abdullah; Abu Springer (2021) Pehmeäkantinen kirja
Mohd Hafiz Arzmi; Anwar P. P. Abdul Majeed; Rabiu Muazu Musa; Mohd Azraai Mohd Razman; Hong-Seng Gan; Isma Mohd Khairuddin Springer Verlag, Singapore (2023) Pehmeäkantinen kirja
This 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.
