Emmanuel, OkewuAnanya, M.Misra, SanjayKoyuncu, MuratInformation Systems EngineeringComputer Engineering2024-07-052024-07-05202032071-105010.3390/su1224105242-s2.0-85098169103https://doi.org/10.3390/su122410524https://hdl.handle.net/20.500.14411/3190Koyuncu, Murat/0000-0003-1958-5945; Misra, Sanjay/0000-0002-3556-9331Research in sustainable development, program design and monitoring, and evaluation requires data analytics for the Sustainable Developments Goals (SDGs) not to suffer the same fate as the Millennium Development Goals (MDGs). The MDGs were poorly implemented, particularly in developing countries. In the SDGs dispensation, there is a huge amount of development-related data that needs to be harnessed using predictive analytics models such as deep neural networks for timely and unbiased information. The SDGs aim at improving the lives of citizens globally. However, the first six SDGs (SDGs 1-6) are more relevant to developing economies than developed economies. This is because low-resourced countries are still battling with extreme poverty and unacceptable levels of illiteracy occasioned by corruption and poor leadership. Inclusive innovation is a philosophy of SDGs as no one should be left behind in the global economy. The focus of this study is the implementation of SDGs 1-6 in less developed countries. Given their peculiar socio-economic challenges, we proposed a design for a low-budget deep neural network-based sustainable development goals 1-6 (DNNSDGs 1-6) system. The aim is to empower actors implementing SDGs in developing countries with data-based information for robust decision making.eninfo:eu-repo/semantics/openAccesssustainability development goalspredictive analytics modelsdeveloping economiesdeep neural networkArticleQ2Q21224WOS:000603223000001