A Review on Deep Learning Approaches and Optimization Techniques For Political Security Threat Prediction

Abstract

In an era of complex geopolitical dynamics and evolving security threats, the accurate prediction and proactive management of political security risks are imperative. This article provides a comprehensive review of the application of deep learning methodologies and optimization techniques to enhance political security threat prediction. Beginning with analyzing the dynamic landscape of political security threats, the paper emphasizes the necessity for adaptive, data-driven predictive tools. It then delves into the fundamentals of deep learning, elucidating core principles, notable architectural frameworks, and their diverse applications across domains. Expanding upon this foundation, the study evaluates the suitability of deep learning models for addressing the multifaceted challenges associated with political security threat prediction. To maximize the utility of these models, the article explores optimization techniques encompassing hyperparameter tuning, transfer learning, and ensemble strategies, assessing their effectiveness in fine-tuning predictions and bolstering the resilience of threat prediction systems. This review involved the utilization of four journal databases: IEEE, Science Direct, Association for Computing Machinery (ACM), and SpringerLink. We analyzed and examined 39 articles, paying close attention to the different patterns and techniques found within the chosen research framework. Through a critical synthesis of existing research, this review offers insights into the strengths, limitations, and future directions of deep learning-based political security threat prediction, contributing to the ongoing discourse on leveraging artificial intelligence for safeguarding global stability and security.