Adoption of Adam optimizer for enhancement of deep learning model in political security threat prediction

Political security threats are the main challenges for governments and organizations around the globe and require the development of accurate predictive models for their proactive mitigation. Deep learning techniques have been successful in this area, but optimizing their performance is still a major challenge. Thus, this paper introduces a new way of strengthening deep learning frameworks for the prediction of political security threats by using the Adam optimizer. The Adam optimizer, famous for its efficiency in the optimization of deep neural networks, is used here to improve the predictive capabilities of the existing frameworks. Based on the findings of empirical studies on extensive datasets that cover different political circumstances and types of threats, we show that the proposed approach is effective in increasing the prediction accuracy and model convergence. Besides, the comparative studies with the traditional optimization methods confirm the superiority of the Adam optimizer in the improvement of the performance of deep learning frameworks for political security threat prediction tasks. This research is a step forward in the development of predictive analytics in the political security domains and it shows the imponance of the optimization techniques in the improvement of the deep learning models that are used in the real world.