Synergizing Theoretical Foundations and Intelligent Systems: A Unified Approach Through Machine Learning and Artificial Intelligence

• Author(s): Sharmin Akter ; Muntaha Islam ; Jannatul Ferdous ; Md Mehedi Hassan ; Mohammad Majharul Islam Jabed
• Paper ID: 1708445
• Page: 466-477
• Published Date: 31-03-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 9 March-2023

Abstract

In an age filled with the pace of technological change and changing geopolitical threats, the introduction of Artificial Intelligence (AI) and Machine Learning (ML) into national defense systems has gone from being optional to being mandatory. This article presents an integrated framework that combines theoretical defense paradigms and intelligent systems to enhance decision-making processes, predictability, and operational effectiveness in military regimes. Conventional defense packages have in the past hinged on human intuition and static protocols, which are becoming increasingly inadequate in the context of modern times: asymmetrical and cyber-centred warfare. This study discusses how this integration can change the game on threat detection, mission planning, and real-time situation awareness by integrating AI and ML into the basic defense systems. The research combines a mixed-method approach characterised by qualitative theoretical exploration with quantitative modeling and simulations. Several ML algorithms (supervised, unsupervised, and reinforcement learning) are examined in terms of their effectiveness for dynamic defenses. The results show that supervised learning models such as Random Forest and Support Vector Machine offer the highest classification accuracy in identifying potential threats. In contrast, reinforcement learning can bring adaptability into autonomous response systems. The research also presents a conceptual model to ensure that AI decision architectures match military strategy frameworks. Dataare used to compare performance, error ratio, response time, etc., and essential m to compare performance, error ratio, response time, and essential metricsetrics. These visualizations indicate the proposed intelligent defense architecture's practical possibility. The concluding part of the article discusses possible challenges, including ethical issues, security issues, algorithmic bias, and suggests future investigation. By connecting the logical roots with the intelligent computational models this research contributes to the comprehensive understanding of modernizing defense systems up to the level of the holistic approach for the realization of the defense technologies which can be seen perfectly talented and effective weapons systems in the future wise for policymakers, defense engineers, and AI researchers who want to create reliable, future-proof defense technologies.

Keywords

Artificial Intelligence, Machine Learning, Smart Defense, Threat Detection, Military Strategy, Intelligent Systems, Predictive Modeling

Citations

IRE Journals:
Sharmin Akter , Muntaha Islam , Jannatul Ferdous , Md Mehedi Hassan , Mohammad Majharul Islam Jabed "Synergizing Theoretical Foundations and Intelligent Systems: A Unified Approach Through Machine Learning and Artificial Intelligence" Iconic Research And Engineering Journals Volume 6 Issue 9 2023 Page 466-477

IEEE:
Sharmin Akter , Muntaha Islam , Jannatul Ferdous , Md Mehedi Hassan , Mohammad Majharul Islam Jabed "Synergizing Theoretical Foundations and Intelligent Systems: A Unified Approach Through Machine Learning and Artificial Intelligence" Iconic Research And Engineering Journals, 6(9)