Full Autonomous Artificial Intelligence in Attack or Defense Decisions Making in Military Drones Box: The NeuronDrone‐Box

Manuscript submitted January 15, 2025; accepted May 6, 2025; published June 17, 2025

Abstract—This research paper introduces the full autonomous artificial intelligence in attack or defense decisions making in military drones’ system box (The NeuronDrone-Box): the hardware, algorithm, and a new special military Drone or Unmanned Aerial Vehicle (UAV). The first section presents the full autonomous artificial intelligence in attack or defense decisions making in military drones’ system box (The NeuronDrone-Box) to adapt to any drone to the main control system of any drone. Second section is the algorithm is using chaos theory and Econographicology. third section we present the groundbreaking prototype known as the “Black Nightmare V.7”. The Black Nightmare V.7 drone bombardier boasts a range of distinctive features and applications, which are detailed in this technical report. Firstly, we advocate for the implementation of the full autonomous artificial intelligence in attack or defense decisions making in military drones’ system box (The NeuronDrone-Box) to control the Multiple Ailerons System (MAS) and Multi-Missiles System (MM-System) connected to the full autonomous artificial intelligence in attack or defense decisions making in military drones’ system box (The NeuronDrone-Box).

keywords—Unmanned Aerial Vehicle (UAV), drones, black nightmare V.7 drone bombardier, aerospace, aviation systems, the full autonomous artificial intelligence in attack or defense decisions making in military drones’ system box (The NeuronDrone-Box)

Cite: Mario Arturo Ruiz Estrada,"Full Autonomous Artificial Intelligence in Attack or Defense Decisions Making in Military Drones Box: The NeuronDrone‐Box," Journal of Advances in Artificial Intelligence, vol. 3, no. 2, pp. 169-179, 2025. doi: 10.18178/JAAI.2025.3.2.169-179

Copyright © 2025 by the authors. 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 (CC BY 4.0).