Analysis of Artificial Intelligence Based Random Forest Classification on Military Dataset

Abstract

This article aims to provide an overview of the potential uses of AI in the military and to emphasize the need to identify and define measurable indicators in order to evaluate the benefits of cutting-edge technologies and solutions that are expected to improve the quality and performance of operations. It focuses on crucial areas such as situational awareness and decision-making support, as well as logistical and operational planning and modelling and simulation (M&S). AI is becoming a crucial tool for intelligence and intelligence analysis of the enemy, and its role in military operations planning and support is growing. Autonomous vehicle and weapon systems are another area in which AI can be put to use. The use of AI is expected to have a greater impact on the military functions of human-machine interfaces (machine-learning, man-machine teaming). AI promises to overcome Big Data\'s \"3V challenge\" (volume, variety, and velocity), as well as the \"2V challenge\" (veracity, value) and render data processing at a controlled level of decision-making based on AI\'s knowledge. In this essay, we will talk about a number of AI applications in the military, their capabilities, opportunities, and the harm and destruction they could cause in times of instability. The seven AI patterns, the military\'s use of AI algorithms, object detection, military logistics, and robots, the global instability caused by AI use, and nuclear risk constituted the majority of the discussion.

Introduction

The text discusses the role of the Indian Army and the growing importance of Artificial Intelligence (AI) in modern military systems. The Indian Army, the land-based branch of India’s armed forces, is responsible for defending the nation against external aggression and internal threats, maintaining national unity, and conducting humanitarian and disaster relief operations. It has played a significant role in major wars, military operations, peacekeeping missions, and global conflicts such as the World Wars, contributing substantially to India’s defence strength alongside the Navy and Air Force.

Artificial Intelligence is defined as the development of machines capable of mimicking human intelligence through technologies such as machine learning, natural language processing, and robotics. AI has transformed multiple sectors and is increasingly influencing military operations by enhancing decision-making, surveillance, cybersecurity, logistics, training, and autonomous systems. While AI improves efficiency, accuracy, and data management, it also raises ethical concerns, particularly regarding autonomous weapons, transparency (black-box systems), job displacement, and human control in warfare.

The literature review highlights global research on AI’s military applications, including autonomous weapons, drone swarms, logistics, cyber warfare, and explainable AI. Scholars emphasize both AI’s strategic potential and the unresolved challenges related to ethics, accountability, human–machine collaboration, and geopolitical competition, especially among major powers like the USA, China, and Russia. For India, being a late entrant into AI-driven defence systems necessitates focused policy-making, skill development, and investment to remain competitive.

The existing system analysis points out limitations such as lack of transparency in AI models and the continued need for human involvement in military decision-making. Explainable AI, advanced machine learning techniques, and better data analytics are identified as crucial areas for future research. AI is projected to significantly boost India’s economy and reshape warfare by enabling technology-driven operations, reducing casualties, improving logistics, and providing strategic insights.

The proposed system explores the use of machine learning algorithms, particularly Linear Regression and Random Forest, to improve predictive performance in defence-related applications. Linear regression is valued for simplicity and interpretability, while Random Forest offers higher accuracy, robustness, and scalability for large datasets. Performance evaluation using metrics such as survival probability, accuracy, and precision demonstrates that the proposed approach outperforms existing methods in efficiency, scalability, runtime, and memory usage.

Conclusion

To this end, the most dataset-associated well-known additives, including virtual demonstration and visual representation, were evaluated in this paper to be expecting the military idea into the machine learning process. The overall performance of machine learning, including linear regression, random forest, and KNN, was evaluated. Results indicated that the carried out fashions have suitable performance for military field work, however, the excellent performance was related to the Random forest. Furthermore, a contrast of the performance of carried out fashions indicated that the outcomes of Random forest models have been extra reliable in comparison with the KNN and Linear regression. In this case, using a prescriptive evaluation based totally on projected values would bring about future abilities to help decision and coverage makers.

References

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Copyright

Copyright © 2026 Dr. N. Menaga, Dr. P. Tamilselvan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.