Machine Learning Algorithm for Intelligent Prediction for Military Logistics and Planning

Ajakwe, Simeon Okechukwu; Nwakanma, Cosmas Ifeanyi; Lee, Jae-Min; Kim, Dong-Seong

doi:10.1109/ictc49870.2020.9289286

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Another example to note is the dependence on oil by military equipment and machinery. This is also where ML comes in, as military logistics must be intelligently based on informed deductions [4]; thus, we see how ML is integrated into the military world.…”

Section: Introductionmentioning

confidence: 95%

Military Applications of Machine Learning: A Bibliometric Perspective

2022

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The military environment generates a large amount of data of great importance, which makes necessary the use of machine learning for its processing. Its ability to learn and predict possible scenarios by analyzing the huge volume of information generated provides automatic learning and decision support. This paper aims to present a model of a machine learning architecture applied to a military organization, carried out and supported by a bibliometric study applied to an architecture model of a nonmilitary organization. For this purpose, a bibliometric analysis up to the year 2021 was carried out, making a strategic diagram and interpreting the results. The information used has been extracted from one of the main databases widely accepted by the scientific community, ISI WoS. No direct military sources were used. This work is divided into five parts: the study of previous research related to machine learning in the military world; the explanation of our research methodology using the SciMat, Excel and VosViewer tools; the use of this methodology based on data mining, preprocessing, cluster normalization, a strategic diagram and the analysis of its results to investigate machine learning in the military context; based on these results, a conceptual architecture of the practical use of ML in the military context is drawn up; and, finally, we present the conclusions, where we will see the most important areas and the latest advances in machine learning applied, in this case, to a military environment, to analyze a large set of data, providing utility, machine learning and decision support.

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Section: Introductionmentioning

confidence: 95%

Military Applications of Machine Learning: A Bibliometric Perspective

2022

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“…Also, there is insufficient awareness of government regulations regarding safety practices for airspace usage when it comes to drone-based logistics and priority-based deliveries. Finally, it is worth pointing out that the promulgation of stiffer laws and sanctions for incriminating airspace, cyberspace, and AI-space offenders that engage in drone hijacking, drone identity theft, droneillance-or drone surveillance (Dini et al, 2022)-for cyberwarfare under the guise of drone-based logistics (Ajakwe et al, 2020), and unprovoked drone warfare on civil targets is highly needed to actualize secured smart aerial mobility via drones. Most cyber-physical attacks on drones (for instance, spoofing and signal jamming), as well as authentication issues (for example, identity theft), arise from loopholes in the existing regulatory framework.…”

Section: Corporate Airspace Security Intelligence In Dds Designmentioning

confidence: 99%

Radicalization of Airspace Security: Prospects and Botheration of Drone Defense System Technology

Ajakwe

Kim

Lee

2023

JICW

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The development of a comprehensive and decisive drone defense integrated control system that can provide maximum security is crucial for maintaining territorial integrity and accelerating smart aerial mobility to sustain the emerging drone transportation system (DTS) for priority-based logistics and mobile communication. This study explores recent developments in the design of robust drone defense control systems that can observe and respond not only to drone attacks inside and outside a facility but also to equipment data such as CCTV security control on the ground and security sensors in the facility at a glance. Also, it considered DDS strategies, schema, and innovative security setups in different regions. Finally, open research issues in DDs designs are discussed, and useful recommendations are provided. Effective means for drone source authentication, delivery package verification, operator authorization, and dynamic scenario-specific engagement are solicited for comprehensive DDS design for maximum security Received: 2023-03-07 Revised: 2023-04-27

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“…The idea of the reinforcement learning was presented in Figure 8. Another approach to supply chain management was presented in [65]. Authors compared artificial neural network (ANN) and machine learning algorithms like k-Nearest Neighbors, Logistic Regression, Random Forest and Naive Bayes in solving the problem of prediction of availability and possible reorder level of military logistics in an example of ensuring the availability of petroleum products.…”

Section: Ai Applications For Military Logisticsmentioning

confidence: 99%

“…Authors compared artificial neural network (ANN) and machine learning algorithms like k-Nearest Neighbors, Logistic Regression, Random Forest and Naive Bayes in solving the problem of prediction of availability and possible reorder level of military logistics in an example of ensuring the availability of petroleum products. Another approach to supply chain management was presented in [65]. Authors compared artificial neural network (ANN) and machine learning algorithms like k-Nearest Neighbors, Logistic Regression, Random Forest and Naive Bayes in solving the problem of prediction of availability and possible reorder level of military logistics in an example of ensuring the availability of petroleum products.…”

Section: Ai Applications For Military Logisticsmentioning

confidence: 99%

Artificial Intelligence Applications in Military Systems and Their Influence on Sense of Security of Citizens

Bistroń

Piotrowski

2021

Electronics

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The paper presents an overview of current and expected prospects for the development of artificial intelligence algorithms, especially in military applications, and conducted research regarding applications in the area of civilian life. Attention was paid mainly to the use of AI algorithms in cybersecurity, object detection, military logistics and robotics. It discusses the problems connected with the present solutions and how artificial intelligence can help solve them. It briefly presents also mathematical structures and descriptions for ART, CNN and SVM networks as well as Expectation–Maximization and Gaussian Mixture Model algorithms that are used in solving of discussed problems. The third chapter discusses the attitude of society towards the use of neural network algorithms in military applications. The basic problems related to ethics in the application of artificial intelligence and issues of responsibility for errors made by autonomous systems are discussed.

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Machine Learning Algorithm for Intelligent Prediction for Military Logistics and Planning

Cited by 5 publications

References 15 publications

Military Applications of Machine Learning: A Bibliometric Perspective

Military Applications of Machine Learning: A Bibliometric Perspective

Radicalization of Airspace Security: Prospects and Botheration of Drone Defense System Technology

Artificial Intelligence Applications in Military Systems and Their Influence on Sense of Security of Citizens

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