# Deep Neural Network Model for Evaluating and Achieving the Sustainable Development Goal 16
## 2 Related Works
### Literature Review  
The past works on the predictive model (deep neural networks), sustainable development goals, and corruption are as follows. [PERSON] [4] postulated that the generic gradient descent optimization algorithm's objective when applied to neural network problems, is finding optimal parameter values that reduce the error between predicted and actual values using initial parameter values. In other words, the optimization algorithm called gradient descent searches for optimal weights that reduce prediction error. The steps for gradient-based optimization include initializing the weights with random values and calculating error; computing the gradient which ensures the change in the values of weights is in the direction in which error is minimized; updating weights using gradients; using new weights for prediction and for computing error; and repeating gradient computation and weight updating until adjustment to weights doesn't appreciably reduce the error. The work offers insight into stochastic optimization algorithms' workings, particularly as applicable in artificial neural networks and deep learning. However, it is silent on applying DNN as a prediction tool for detecting patterns in sustainable development data such as corruption survey data. Our present study applies the predictive model to corruption survey data intending to elicit useful patterns for improved anti-graft measures. This is in a bid to facilitate the implementation of the SDGs. [PERSON] [1] opined that neural network is useful for solving a wide range of problems. As long as data and problems exist in any field of human endeavor, machine learning can be applied. DNN apart, other deep learning tools useful for predictive modeling include deep random forests, neural processes, and deep Gaussian processes. The study revealed that DNN remains a force to reckon with among the lots. The author emphasized that in recent years, deep learning as a predictive tool has gained popularity because of its ability to handle complex problems. Our present study considered that tackling the impact of corruption on the attainment of sustainable development goals is a hydra-headed problem that a sophisticated predictive tool like DNN should be applied to resolve. In [26], the authors proposed a compendium of the statistical information needed for monitoring and evaluating progress in SDGs implementation in Nigeria. It emphasizes the essence of timely, disaggregated, reliable, and accessible data for measuring progress, informed decision-making, and ensuring that no one is left behind. Apart from attaining the SDGs targets, the government needed to be provided with reliable, adequate, and timely statistical information to implement its national plan christened Economic Recovery and Growth Plan (ERGP). The compendium is comprehensive and robust statistical information that takes into cognizance Nigeria's peculiarity. It is a product of the combined data collection efforts of several agencies. Despite emphasizing the significance of data in achieving the SDGs, the work does not show the link between corruption and the attainment of SDGs. This current study establishes a relationship between corruption and the attainment of SDGs. We also add value to existing data by applying data analytics (predictive modeling) to determine knowledge engineering for informed decision-making. [PERSON] [5] dwells on measuring progress towards attaining SDG 16, which focuses on strong institutions, peace, and justice. Among others, Goal 16 contains obligations to tackle corruption, scale up transparency, fight illegal financial flows and improve information access. The author emphasizes that Transparency International believes that achieving the other goals is nearly impossible in the absence of significant action to mitigate corruption.