Don hango waɗannan rikice-rikice, zaku iya amfani da i samfurin tsinkaya amma sun dogara ne akan matakan haɗari waɗanda galibi ana jinkirta su, waɗanda ba su daɗe ko kuma ba su cika ba. Nazarin Jami'ar New York yayi ƙoƙarin fahimtar yadda ake amfani da algorithms tsinkaya ta hanya mafi kyau.
Binciken ya nuna cewa, ta hanyar tattara kasidu miliyan 11,2 kan kasashe masu fama da karancin abinci da aka buga tsakanin shekarar 1980 zuwa 2020, da kuma cin gajiyar ci gaban da aka samu a baya-bayan nan. deep learning: ana iya samun sakamako mai ta'aziyya. Sarrafa ya ba da izinin fitar da manyan abubuwan da ke haifar da rikice-rikicen abinci waɗanda duka biyun suke fassara da ingantattun alamun haɗari na gargajiya.
Algorithm deep learning Ya yi nuni da cewa, a tsakanin watan Yulin 2009 zuwa Yuli 2020, alamomin rikicin sun inganta hasashen a cikin kasashe 21 masu fama da karancin abinci, har zuwa watanni 12 da suka gabata fiye da tsarin asali wadanda ba su hada da bayanan rubutu ba.
Binciken ya mayar da hankali ne kan Hasashen Haɗin Kai Tsaye (IPC) na ƙarancin abinci da aka buga Hanyoyin Sadarwar Gargaɗi game da Fari (FEWS NET). Ana samun wannan rabe-rabe a matakin gundumomi a kasashe 37 masu fama da karancin abinci a Afirka, Asiya da Latin Amurka kuma an bayar da rahoton sau hudu a shekara tsakanin 2009 zuwa 2015 da sau uku a shekara bayan haka.
An rarraba ƙarancin abinci bisa ga ma'auni na yau da kullun wanda ya ƙunshi matakai biyar: ƙananan, damuwa, rikici, gaggawa da yunwa.
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