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Abdul Rahman Omar

affiliation not provided to SSRN

  • affiliation not provided to SSRN

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A Hybrid Chromaticity-Morphological Machine Learning Model to Overcome the Limit of Detecting Newcastle Disease in Chicken within 36 Hours

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A Hybrid Chromaticity-Morphological Machine Learning Model to Overcome the Limit of Detecting Newcastle Disease in Chicken within 36 Hours

Number of pages: 31 Posted: 14 Mar 2024
Mohd Anif Akhmal Abu Bakar, Pin Jern Ker, Shirley Gee Hoon Tang, Fatin Nursyaza Arman Shah, T.M.I. Mahlia, Mohd Zafri Baharuddin and Abdul Rahman Omar
Universiti Tenaga Nasional, Universiti Tenaga Nasional, Universiti Kebangsaan Malaysia, Universiti Putra Malaysia, University of Technology Sydney (UTS), Universiti Tenaga Nasional and affiliation not provided to SSRN
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Food Security, Food Safety, Machine Learning, Image Processing, Infected, Poultry, Energy

A Hybrid Chromaticity-Morphological Machine Learning Model to Overcome the Limit of Detecting Newcastle Disease in Chicken within 36 Hours

Number of pages: 48 Posted: 25 Apr 2024
Mohd Anif Akhmal Abu Bakar, Pin Jern Ker, Shirley Gee Hoon Tang, Fatin Nursyaza Arman Shah, T.M.I. Mahlia, Mohd Zafri Baharuddin and Abdul Rahman Omar
Universiti Tenaga Nasional, Universiti Tenaga Nasional, Universiti Kebangsaan Malaysia, Universiti Putra Malaysia, University of Technology Sydney (UTS), Universiti Tenaga Nasional and affiliation not provided to SSRN
Downloads 60 (996,491)
Citation 1

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Machine Learning, Image processing, Infected, Poultry, Energy