HYBRID MODELS AND ALGORITHMS FOR SELECTING OPTIMAL FREQUENCIES BASED ON ARTIFICIAL INTELLIGENCE

Authors

  • Khurramov Jamshid Akhrorovich Uzbekistan Republican University of Military Security and Defense Military Aviation Institute Department of Aviation Engineers Head of the Radioelectronic Equipment Cycle Lieutenant Colonel, dotsen

Keywords:

artificial intelligence, hybrid algorithms, optimal frequency, neural networks, machine learning, genetic algorithm, telecommunications, signal transmission, radio communication, digital networks.

Abstract

This article analyzes the theoretical and practical aspects of hybrid models and algorithms based on artificial intelligence for selecting optimal frequencies. The study examines the integration of neural networks, genetic algorithms, and machine learning methods to improve signal transmission quality and network efficiency. Particular attention is paid to effective frequency management in radio communication systems, reduction of interference levels, and optimization
of data transmission speed. The use of hybrid algorithms makes it possible to enhance the stability and energy efficiency of telecommunication systems. The research findings are of significant scientific and practical importance for the development
of mobile communications, digital networks, and advanced telecommunication technologies.

References

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Published

2026-05-14

How to Cite

Khurramov Jamshid Akhrorovich. (2026). HYBRID MODELS AND ALGORITHMS FOR SELECTING OPTIMAL FREQUENCIES BASED ON ARTIFICIAL INTELLIGENCE. Ethiopian International Journal of Multidisciplinary Research, 13(5), 929–933. Retrieved from https://eijmr.org/index.php/eijmr/article/view/6766

Issue

Vol. 13 No. 5 (2026): Ethiopian International Journal of Multidisciplinary Research

Section

Articles