Mahdi Afshar
Hamed Garoosi
Abbas Rahdan

This paper presents a prophetic model using the Random Forest algorithm to read thecoffers demanded by the service. Directly estimating the needed coffers is pivotal for effectivedecision- making and planning in military operations. The proposed model utilizes a datasetcomprising colorful factors similar as the number of military labor force, available vehicles,aircraft, nonmilitary vessels, security stashes, and medical inventories. The Random Forestalgorithm is chosen due to its capability to handle complex connections and give robustprognostications. The model is trained on a sample dataset, and the weights and bias terms aredetermined automatically during the training process. The dataset includes compliances withknown resource conditions, allowing the model to learn patterns and make accurateprognostications. Results from the model indicate promising performance in prognosticating theposition of coffers demanded, distributed as low, medium, or high. The delicacy of the model isestimated using applicable criteria, and the results demonstrate its effectiveness in soothsayingresource conditions for military operations. This exploration contributes to the field of militaryresource operation by furnishing a data- driven approach for prognosticating resourcerequirements. The proposed model can help military itineraries in making informed opinions,optimizing resource allocation, and icing functional readiness. unborn work can involveincorporating fresh variables and enriching the model to enhance its prophetic capabilities.

Random Forest, machine learning, military