Application of artificial intelligence to preserve the reindeer population in the arctic regions of the planet (case study of the republic of Sakha (Yakutia))

E3S Web of Conferences, Journal Year: 2023, Volume and Issue: 460, P. 04015 - 04015

Published: Jan. 1, 2023

This article examines the approaches and prospects for use of artificial intelligence to preserve reindeer population in Arctic regions planet (on example Republic Sakha (Yakutia)). Preserving fauna based on modern technologies, including intelligence, is one promising solutions environmental problems Arctic. The study terms analyzing changes migration routes. More accurate prompt data routes will contribute improving quality decisions made field conservation. results obtained may be interest software developers can also used development programs aimed at preserving regions, research this area.

Language: Английский

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Development of ecotourism in the arctic regions based on the use of modern IT-technologies

E3S Web of Conferences, Journal Year: 2023, Volume and Issue: 460, P. 09025 - 09025

Published: Jan. 1, 2023

This article examines the impact of use modern IT-technologies on development ecotourism in Arctic regions. A survey was conducted among potential users tourism services regarding their choice between traditional and ecological types tourism. The results confirm increase attractiveness regions with introduction IT technologies. These studies can be used programs for regions, as well conducting research field planet.

Language: Английский

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Composite Ensemble Learning Framework for Passive Drone Radio Frequency Fingerprinting in Sixth-Generation Networks

Sensors, Journal Year: 2024, Volume and Issue: 24(17), P. 5618 - 5618

Published: Aug. 29, 2024

The rapid evolution of drone technology has introduced unprecedented challenges in security, particularly concerning the threat unconventional and swarm attacks. In order to deal with threats, drones need be classified by intercepting their Radio Frequency (RF) signals. With arrival Sixth Generation (6G) networks, it is required develop sophisticated methods properly categorize signals achieve optimal resource sharing, high-security levels, mobility management. However, deep ensemble learning not been investigated case 6G. It anticipated that will incorporate drone-based BTS cellular networks that, one way or another, may subjected jamming, intentional interferences, other dangers from unauthorized UAVs. Thus, this study conducted based on Fingerprinting (RFF) identified detect ones so proper actions can taken protect network’s security integrity. This paper proposes a novel method—a Composite Ensemble Learning (CEL)-based neural network—for signal classification. proposed method integrates wavelet-based denoising combines automatic manual feature extraction techniques foster diversity, robustness, performance enhancement. Through extensive experiments open-source benchmark datasets drones, our approach demonstrates superior classification accuracies compared recent across various Signal-to-Noise Ratios (SNRs). holds promise for enhancing communication efficiency, safety 6G amidst proliferation applications.

Language: Английский

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A Survey of Intelligent Reflecting Surfaces: Performance Analysis, Extensions, Potential Challenges, and Open Research Issues

Vehicular Communications, Journal Year: 2024, Volume and Issue: unknown, P. 100859 - 100859

Published: Nov. 1, 2024

Language: Английский

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Secure, Robust, and Energy-Efficient Authenticated Data Sharing in Drone to Vehicles Communications

Published: July 8, 2024

Language: Английский

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