# Deep Neural Network Model for Evaluating and Achieving the Sustainable Development Goal 16
## 1 Introduction  
Deep learning is a predictive model and a machine learning component. It uses algorithms and data to make classifications, predictions, and decisions. Explicit programming is not involved in deep learning [1] Predictive models like deep learning facilitate decision-making in sustainable development domains by eliciting knowledge from raw data hierarchically, using algorithms rather than leveraging the domain experts' outlined features. This is accomplished through many layers of non-linear processing units to make predictions or take actions that are in line with the defined target objective. Generally, a neural network is categorized as a deep model when it has more than one hidden layer. Apart from deep neural networks (DNNs), architectures like deep random forests [2], neural processes [3], and deep Gaussian processes [4] have multiple layers and these are also classified as deep learning models. Applying a predictive tool like deep learning to a national database such as the Nigerian national corruption survey database would help to detect hidden and useful patterns for understanding the corruption disposition behaviors of the citizenry. The unbiased information that is generated could be used to guide decisions that are bordering on designing value re-orientation programmes. Such value re-orientation aims to instill the core values of accountability, transparency, and probity in leaders and are important in attaining the Sustainable Development Goals (SDGs). Corruption corrodes away at the public resources, weakens institutions, promotes political instability, and makes the attainment of sustainable development impossible [5]. The presence of corruption in a country means the erosion of peace, justice, strong institutions, and partnerships, which are the ideals of SDG 16 [5, 6]. Essentially, the attainment of this goal (SDG 16) is fundamental to the achievement of the remaining SDGs [7, 8]. This implies that an effective and efficient evaluation framework for national development, suchas Nigeria's Economic Recovery and Growth Plan (ERGP) [9] should place a high premium on an anti-corruption drive. The ERGP is aimed at attaining all the SDGs by 2030 [10]. Hence, detecting patterns in national corruption databases for gauging the disposition of citizens to conduct corrupt practices for the purpose of taking correctional measures is germane. This study modeled the 2017 Nigerian national corruption survey dataset [11] using a DNN to understand the underlying corruption patterns. In developing economies like Nigeria, data are not readily available. Hence, the study has used only this dataset. Also, to underscore the strategic role of image data (e.g., biometrics) in the anti-corruption effort, a series of experiments were performed using standardized image data from the Modified National Institute of Standards and Technology (MNIST). Corruption in Nigeria is endemic [12; 13] and this threatens the actualization of the national economic plan (ERGP) as well as the SDG implementation plans [7; 9]. A DNN is used as the predictive model for extracting information from the dataset. [PERSON] (2016) observed that the inadequate use of data analytics impacted adversely on the actualization of previous global sustainable development agendas. The research question is: how useful is predictive modeling in the formulation of policies, the implementation of programs, and the management of projects for the purpose of curbing corruption in a bid to achieve the SDGs? The objectives of this work are:  
1. To evaluate the impact of corruption on sustainable development policies, programs, and projects. 2. To model corruption-related data with a view to understanding patterns for the improved anti-corruption drive. 3. To experimentally demonstrate the efficacy of deep neural networks as a predictive analytics tool. In a nutshell, our study links corruption to the inability of countries to attain the sustainable development goals.