Golden Eagle Research Articles

Fauna and landscapes of the Southern Minusinsk basin (southern Siberia) at the end of the late pleistocene

The paleolandscapes of the first half of MIS 2 or the Last Glacial Maximum (LGM) were reconstructed based on the study of the first mammoth fauna locality in the Abakan River valley and comparison with other localities in the South Minusinsk Basin. The sediments in the geological section of Uytag are represented by interlayers of silts, underlain and overlain by sandstone rock slack fragment, interpreted as weakly eroded deluvium. The study of the Uytag locality made it possible to clarify the time and range of distribution in southern Siberia of some mammal species Ovis ammon, Equus ferus, Marmota baibacina, Sicista subtilis, Lagurus lagurus and Microtus gregalis, as well as Pleistocene bird Aquila chrysaetos. The age of the locality is confirmed by radiocarbon dating. The species composition of the fauna is similar to other localities in the region of this age. All known localities and single finds of faunal remains indicate a wide development of open steppe landscapes in the South Minusinsk Basin during the LGM.

Effect of Viscosity in the Medium for Sperm Cryopreservation of Golden Eagle (Aquila Chrysaetos)

BACKGROUND: The development of assisted reproduction techniques for birds is useful for ex situ conservation but is limited. For the golden eagle ( Aquila chrysaetos ), only artificial insemination procedures using extenders developed over 50 years ago have been described. OBJECTIVE: To evaluate the effect of the viscosity of the cryopreservation medium on the acrosomal reaction ability of A. chrysaetos sperm. MATERIALS AND METHODS: Viscosity was determined in 45 ejaculates. A design using the Lake medium, 6% dimethylacetamide (DMA), and Ficoll (10%, 35%, and 45%) was developed to create conditions of viscosity that was lower, similar, and higher than those determined in fresh semen. RESULTS: The viscosity of fresh semen was 3.2320 mPa/s. In aliquots of the Lake medium supplemented with 6% DMA and 10%, 35%, and 45% Ficoll, the viscosities were 2.1698 mPa/s, 3.5393 mPa/s, and 6.1123 mPa/s, respectively. Post-thaw, in the aliquot with 10% Ficoll, 74% of sperm were alive, with 28% mobility, and 21% exhibited an acrosomal reaction with percentages that were significantly lower (P<0.05) than those observed in sperm frozen with 30% and 45% Ficoll. CONCLUSION: Viscosity has a positive influence on the viability of cryopreserved semen.

Performance analysis of DC-DC Buck converter with innovative multi-stage PIDn(1+PD) controller using GEO algorithm

Power electronic converters are widely used in various fields of electrical equipment. Due to their fast dynamics and non-linear nature, controlling them requires dealing with various complexities. Therefore, having a well-designed, high-speed, and robust controller is critical to ensure the effective operation of these devices. In a DC-DC converter, steady-state performance with minimum error and fast dynamic response relies on controller design. This paper presents the design of a multi-stage PID controller with an N-filter combined with a one plus proportional derivative (1+PD) controller. This controller illustrates fast tracking reference voltage; additionally, it shows incredible results when the DC-DC converter operates in different modes. The parameters of the proposed controller are effectively determined using the golden eagle optimization (GEO) algorithm. Furthermore, a comprehensive comparison between the proposed controller, proportional–integral–derivative (PID), and fractional order PID (FOPID) controllers, as well as different metaheuristic optimization methods in various conditions, has been conducted to demonstrate the effectiveness of the proposed controller. The behavior of the closed-loop system under different conditions has been thoroughly investigated. The superior time and frequency domain characteristics of the closed-loop system with the PIDn(1+PD) controller highlight its superiority over other controllers. The demonstrated enhancements in settling time, voltage regulation accuracy, and transient response emphasize the potential applicability of the proposed control strategy in real-world power electronics systems, particularly in scenarios requiring high efficiency, stability, and dynamic performance.

Linear infrastructure and associated wildlife accidents create an ecological trap for an apex predator and scavenger

Animals may fall into an ‘ecological trap’ when they select seemingly attractive habitats at the expense of their fitness. This maladaptive behavior is often the result of rapid, human-induced changes in their natal environment, such as the construction of energy and transportation infrastructure. We tested the ecological trap hypothesis regarding human-created linear infrastructure on a widely distributed apex predator and scavenger—the Golden Eagle (Aquila chrysaetos), whose range spans the entire Northern Hemisphere. Roads and railways offer novel and attractive feeding opportunities through traffic-induced mortality of other species, while powerline areas provide perching or nesting sites and scavenging opportunities from electrocuted or collision-killed birds. These conditions may have negative demographic consequences for eagles if these apparent opportunities turn into traps. Using step selection functions, we analyzed habitat selection of 74 GPS-tracked Golden Eagles (37 adults and 37 immatures) during eleven years in Fennoscandia. To assess habitat attractiveness, we used wildlife traffic accident statistics for dominant wild species, and to evaluate demographic consequences, we used mortality data from the GPS-tagged eagles. Our analysis revealed that eagles selected linear features such as roads, railways and powerlines at both the population and individual levels. Both adult and immature eagles consistently selected these features, and the strength of selection for linear features increased with age in immature eagles. The linear features however had 5.5 times higher mortality risk for eagles than other selected habitats indicating the presence of an ecological trap. We discuss the implications of these findings for the conservation and population ecology of apex predators and scavengers, as well as their potential demographic consequences. To mitigate this issue, we urgently recommend the removal of carcasses from roads and tracks to prevent ecological traps for raptors and scavenger species worldwide. Additionally, we advocate for the development of methods and strategies to reduce wildlife traffic accidents.

Security-enabled optimal placement of drone-assisted intelligent transportation systems in mission-critical zones

A massive increase in highly dynamic vehicular nodes has resulted in network instability. Owing to the heterogeneous vehicular environment requires a multi-objective solution using a meta-heuristic optimization algorithm in the event of mission-critical zones with poor signal and secured quick decision-making system. Developed a security-enabled optimal placement of Drones or unmanned aerial vehicles (UAV) in mission-critical zones aims to achieve two primary objectives: 1) Maximizing the effectiveness of the intelligent transportation system (ITS) for traffic management and ubiquitous connectivity in mission-critical zones. 2) Ensuring robust security measures to protect sensitive data and infrastructure. This approach represents a cutting-edge solution for optimizing transportation systems in high-risk environments while safeguarding against potential security threats. The pre-deployment of drones and vehicles (VOBU) parameter occurs during the registration phase, and then the mission-critical zone (MCZ) is identified and stored. The optimal position for drones in MCZs is determined by mathematically modeling a golden eagle optimization (GEO), which is inspired by varying the speed at different stages along their spiral trajectory for cruising and hunting. Furthermore, the robustness of the sensitive data and the real identity is ensured by using a biometric-based AKA algorithm utilizing the prevalent real-or-random (ROR) model and the formal security analysis. Based on a comparison of the simulation results, the proposed SDV-GEOAKA scheme outperforms the existing system- STPTC-A2 G, IoDAV, and IMOC with 99.36 % of PDR approximately, whereas, SDV-GEOAKA has maintained a load balancing factor with 0.01 to 0.1 when the transmission range between 0 and 60. When it comes to network coverage, proposed work outperforms with 99.95 % during the transmission range of 50 mW means it uses a minimum number of drones with maximum connectivity within the coverage range and also has significantly reduced the computation overhead and an increase in anomaly detection rate.

High Prevalence and Broad Distribution of Trichomonas gypaetinii in Bald Eagles (Haliaeetus leucocephalus) in the USA.

Trichomonas gypaetinii was detected in 117 (88%) of 133 Bald Eagles (Haliaeetus leucocephalus) and in 0/7 Golden Eagles (Aquila chrysaetos) in the USA, with no sex or age prevalence difference. All eagles lacked associated lesions. This study indicated that T. gypaetinii is common and widespread in Bald Eagles, but rarely associated with disease.

A non‐invasive genetic approach for estimating the apparent survival rate of a long‐lived territorial raptor: a case study on the Golden Eagle Aquila chrysaetos

For long‐lived species, adult survival is the parameter that theoretically has the strongest impact on population dynamics; this makes its estimate crucial for conservation management. The use of non‐invasive genetic approaches coupled with capture–recapture methods is being used increasingly to estimate adult survival. Despite being regularly employed in mammals, it has been little‐used in birds and notably in long‐lived territorial species. This study aimed to determine if feathers obtained from eagle nests could be used to estimate the adult apparent survival rate and the costs associated with such an approach. Feather samples (n = 180) were collected in nests (n = 78) and directly from individual Golden Eagles (n = 36) Aquila chrysaetos in France and were genotyped using 17 microsatellite markers. The genotyping error rates obtained were low, with 3% amplification failures, 3.1% dropouts and 1.7% false alleles. The genetic variability of the markers was high, with a probability of identity between siblings of 6.8 × 10−6, allowing reliable individual identification. Of the feathers collected in nests, 90% were from breeding females; this allowed apparent survival to be estimated for adult females at a reasonable cost. The genotyping of three feathers collected from a nest ensured individual identification of the breeding female with near certainty. Our simulations showed that monitoring of at least 20 pairs over 5 years, or 10 pairs over 10 years is necessary to detect a 10% absolute decrease in adult apparent survival. Estimating the adult survival rate of long‐lived birds using feathers collected in nests is possible. A substantial budget, albeit one that is likely to be lower than that for a common capture–recapture survey, would be needed to obtain precise survival estimates.

Design and implementation of Golden Eagle optimized cascaded PI and LQR controller for PFC SEPIC converter in EV charging

This research article focuses on designing and implementing a single ended primary inductor converter (SEPIC) for an AC-DC converter, followed by an electric vehicle (EV) charger. It improves the high-power factor at the AC supply with minimum harmonic distortion. The Golden Eagle optimization techniques are adapted to optimize the tuning of Proportional-Integral (PI) and Linear Quadratic Regulator (LQR) controller parameters for better converter performance. The optimization is designed based on the eagle knowledge of hunting methods at various angles of spiral trajectories in capturing the animal. The SEPIC converter is designed and derived from the state space model by state space averaging, and the reduced model is obtained through the moment matching method to reduce computational complexity. The Golden Eagle Optimize the parameters of KP and KI of the PI controller and weighing matrix Q of the linear quadratic controller. The fitness function of the proposed optimization is the sum of the Integral Absolute error (IAE) and Integral Square error (ISE). The proposed optimization is implemented using MATLAB/SIMULINK software, and the simulation outcomes demonstrate an improved settling time, fast recovery against input and output variations, Total Harmonic Distortion (THD) of 1.75 %, and enhanced stability.

Revolutionizing PV grid integration: Metaheuristic optimization of fractional PI controllers in T-type neutral point piloted inverters for enhanced performance

This study investigates the efficacy of FOPI regulators as a substitute for conventional proportional integral (PI) controllers in grid-connected PV inverters. The research's objective is to improve the dynamic efficiency of these systems by integrating intelligent optimization techniques that utilize both PI and FOPI controllers and employing different metaheuristic optimization procedures. The study introduces four new optimization algorithms: the Tyrannosaurus Optimization Algorithm (TROA), the Nutcracker Optimization Algorithm (NOA), the Golden Eagle Optimizer (GEO), and the Jellyfish Search Optimizer (JSO). These are made to meet the needs of multi-objective optimization. The proposal suggests using two PI/FOPI regulators to regulate both voltage and amperage provided by the multilayer inverter. The proposal employs a T-type three-level inverter, known for its superior conversion efficiency over conventional inverters. Matlab-Simulink simulations demonstrate that the Nutcracker optimization algorithm (NOA) outperforms other metaheuristic techniques for optimizing dynamic behavior, such as overshoot, settling time, execution, and rising time. Comparisons with existing methods, such as the Manta Rays Foraging Optimization (MRFO) and Grey Wolf Optimizer (GWO), show that all four new algorithms consistently outperform these existing algorithms. The data suggest that using NOA improves stability, efficiency, and power factor while reducing the inverter's THD.

Dispersal Ecology of Golden Eagles (Aquila chrysaetos) in Northern Greece: Onset, Ranging, Temporary and Territorial Settlement

Dispersal Ecology of Golden Eagles (Aquila chrysaetos) in Northern Greece: Onset, Ranging, Temporary and Territorial Settlement

Efficacy of non‐lead ammunition distribution programs to offset fatalities of golden eagles in southeast Wyoming

AbstractGolden eagles (Aquila chrysaetos) face many anthropogenic risks including illegal shooting, electrocution, collision with wind turbines and vehicles, and lead poisoning. Minimizing or offsetting eagle deaths resulting from human‐caused sources is often viewed as an important management objective. Despite understanding the leading anthropogenic sources of eagle fatalities, existing scientific research supports few practical solutions to mitigate these causes of death. We implemented a non‐lead ammunition distribution program in southeast Wyoming, USA, and evaluated its effectiveness as a compensatory mitigation action to offset incidental take (i.e., fatalities) of golden eagles at wind energy facilities. In 2020 and 2022, we distributed non‐lead ammunition to 699 hunters with big‐game tags specific to our >400,000‐ha study area. These hunters harvested 296 pronghorn (Antilocapra americana), 14 deer (Odocoileus spp.), and 33 elk (Cervus canadensis) in the study area, which accounted for 6.9% and 6.5% of the harvest in these hunt units in 2020 and 2022, respectively. We used road surveys in 2020 to estimate a density of 0.036 (95% CI = 0.018–0.058) golden eagles/km2 during the big game hunting season in our study area. Model output suggests that our non‐lead ammunition distribution program offset the fatality of 3.84 (95% CI = 1.06–23.72) eagles over the course of these 2 hunting seasons. Our work illustrates the potential usefulness of non‐lead ammunition distribution programs as an action to mitigate eagle fatalities caused by wind facilities or other anthropogenic causes of death.

Developmental stage shapes the realized energy landscape for a flight specialist.

The heterogeneity of the physical environment determines the cost of transport for animals, shaping their energy landscape. Animals respond to this energy landscape by adjusting their distribution and movement to maximize gains and reduce costs. Much of our knowledge about energy landscape dynamics focuses on factors external to the animal, particularly the spatio-temporal variations of the environment. However, an animal's internal state can significantly impact its ability to perceive and utilize available energy, creating a distinction between the 'fundamental' and the 'realized' energy landscapes. Here, we show that the realized energy landscape varies along the ontogenetic axis. Locomotor and cognitive capabilities of individuals change over time, especially during the early life stages. We investigate the development of the realized energy landscape in the Central European Alpine population of the golden eagle Aquila chrysaetos, a large predator that requires negotiating the atmospheric environment to achieve energy-efficient soaring flight. We quantified weekly energy landscapes using environmental features for 55 juvenile golden eagles, demonstrating that energetic costs of traversing the landscape decreased with age. Consequently, the potentially flyable area within the Alpine region increased 2170-fold during their first three years of independence. Our work contributes to a predictive understanding of animal movement by presenting ontogeny as a mechanism shaping the realized energy landscape.

Golden eagle optimized fractional-order PI controller design for a PFC SEPIC converter in EV charging

The power factor correction converter is the function of the front-end converter, followed by the DC–DC converter of the electric vehicle charger. It improves the power factor and regulates the output voltage and current. This research article proposes the Golden Eagle optimization for fractional order PI (FOPI) controller for Single Ended Primary Inductor Converter (SEPIC) power factor correction. The Golden eagle optimization is based on its knowledge of hunting tactics at various degrees of spiral trajectories to catch the prey. The FOPI controller has a broad range of controller parameters that provide better control and performance of the converter. The tuning of the parameters of the FOPI controller is optimized in Golden Eagle Optimization, and the Integral Absolute error with Integral Square error is used for the objective function. The optimized parameters of FOPI compare with the conventional PI controller performance. The SEPIC converter is designed and derived from the state space model by state space averaging, and the reduced model is obtained through the moment matching method. This system is tested under MATLAB/SIMULINK, and simulation results show improved settling time, fast dynamic response, reduction of inrush current, less harmonic distortion, and stability.

The Characteristics and Variation of the Golden Eagle Aquila chrysaetos Home Range

Satellite tracking allows for novel investigations into golden eagle home range characteristics. Understanding home range characteristics is important for conservation and for assessing the potential impact of landscape changes from forest planting, wind farms, etc. Small sample sizes, inconsistent definitions and methods restricted several previous studies. Our study involved 69 resident tagged eagles with over one year of data across five Scottish regions. Home range size was estimated from 95% isopleth contours extracted from Utilisation Distributions. Above a small threshold, estimated range size was not affected by the number of records but at least one year of data is required, largely because of the breeding and non-breeding seasonal differences. There were no significant range size differences between birds tagged as range holders and those previously tagged as nestlings. Across four regions, with considerable intra-regional variation, planar 95% isopleths did not differ (medians, km2): Argyll 58.9, Northwest Highlands 61.7, Northeast Highlands 89.3, South of Scotland 91.9. Ranges in the isolated Outer Hebrides region were exceptionally small, at 24.0 km2. Estimated range area was usually reduced to 70–80% of the planar area when restricted to usable habitat, as estimated by the Golden Eagle Topography (GET) model. Applying measures of known unsuitable habitat (closed-canopy commercial forest and wind turbines) further reduced usable open land. Loss of otherwise suitable habitat was substantially due to commercial forest. Larger ranges had larger extents of suitable habitat (according to GET), with no apparent optimum of preferred GET habitat. Range size was not different across a year between the sexes. Breeding ranges were smaller, and females’ breeding ranges were much smaller than those of males, but larger than males’ ranges in the non-breeding season. Breeding attempt duration was probably also influential. Our study provides novel insights into golden eagle home range characteristics and can guide further research and practical applications.

Coherent Feature Extraction with Swarm Intelligence Based Hybrid Adaboost Weighted ELM Classification for Snoring Sound Classification.

For patients suffering from obstructive sleep apnea and sleep-related breathing disorders, snoring is quite common, and it greatly interferes with the quality of life for them and for the people surrounding them. For diagnosing obstructive sleep apnea, snoring is used as a screening parameter, so the exact detection and classification of snoring sounds are quite important. Therefore, automated and very high precision snoring analysis and classification algorithms are required. In this work, initially the features are extracted from six different domains, such as time domain, frequency domain, Discrete Wavelet Transform (DWT) domain, sparse domain, eigen value domain, and cepstral domain. The extracted features are then selected using three efficient feature selection techniques, such as Golden Eagle Optimization (GEO), Salp Swarm Algorithm (SSA), and Refined SSA. The selected features are finally classified with the help of eight traditional machine learning classifiers and two proposed classifiers, such as the Firefly Algorithm-Weighted Extreme Learning Machine hybrid with Adaboost model (FA-WELM-Adaboost) and the Capuchin Search Algorithm-Weighted Extreme Learning Machine hybrid with Adaboost model (CSA-WELM-Adaboost). The analysis is performed on the MPSSC Interspeech dataset, and the best results are obtained when the DWT features with the refined SSA feature selection technique and FA-WELM-Adaboost hybrid classifier are utilized, reporting an Unweighted Average Recall (UAR) of 74.23%. The second-best results are obtained when DWT features are selected with the GEO feature selection technique and a CSA-WELM-Adaboost hybrid classifier is utilized, reporting an UAR of 73.86%.

A Modified Levy Flight Firefly-Based Approach to Optimize Turnaround Time in Fog Computing Environments

The escalation of Internet of Things (IoT) devices has led to increased data generation at the network edge that has burdened the cloud infrastructure in terms of handling and processing of data. This has led to the rapid adoption of fog computing because of its ability to bring computation and storage closer to the edge and support for real-time applications and services by reducing latency. One of the foremost challenges in the fog computing arena is minimizing turnaround time. This research paper proposes a Modified Levy Flight Firefly Algorithm (MLFFA) to optimize task scheduling for fog computing environments. Specifically, the objective is to minimize the turnaround time of tasks. Moreover, genetic operators like crossover and mutation are also employed to achieve an optimal balance between exploration and exploitation. Experimental observations undertaken show that the proposed method improves the average turnaround time by 55%, 22%, and 13%, average waiting time by 59%, 45%, and 37%, average energy consumption by 19%, 7%, and 4%, and average failure rate by 50%, 28%, and 7% compared to the existing studies, namely Load Balancing and Optimization Strategy (LBOS), Technique for Resource Allocation and Management (TRAM), and Fuzzy Golden Eagle Load Balancing (FGELB), respectively.

Vulnerability to lead toxicosis and bioindicator utility of deer scavengers in New York

AbstractLead (Pb) is a heavy metal with no clear biological benefit that is toxic to wildlife and humans. Although Pb has been banned from several consumer products, it is not regulated in ammunition for big‐game hunting (e.g., cervids) in New York, USA. Wildlife species that scavenge remains of game harvested with Pb ammunition can be exposed to bullet fragments through ingestion and potentially develop toxicosis. To make evidence‐based decisions for reducing risk from bullet‐derived Pb, we first empirically identified avian and mammalian scavengers of white‐tailed deer (Odocoileus virginianus) remains in New York through an online survey during July‐August 2023, where community members submitted images of scavengers from game cameras. We conducted species vulnerability and utility assessments using published evidence and pre‐existing frameworks. Despite inherent limitations, we documented an extensive array of avian (n = 17) and mammalian (n = 14) scavengers. We estimated bald eagles (Haliaeetus leucocephalus) and golden eagles (Aquila chrysaetos) as most vulnerable to Pb toxicosis, followed collectively by American goshawks (Accipiter atricapillus), American crows (Corvus brachyrhynchos), and common ravens (C. corax). We also determined bald eagles to be best‐suited as bioindicators of bullet‐derived Pb, followed by American goshawks. Red‐tailed hawks (Buteo jamaicensis) and great horned owls (Bubo virginianus) equally ranked third. Integrating vulnerability and utility assessments point to bald eagles as the most vulnerable scavengers to Pb toxicosis and best‐suited bioindicators of bullet‐derived Pb. Because regulation of Pb ammunition for big‐game hunting is an ongoing socio‐political issue, wildlife managers and conservationists can address health risks through monitoring species vulnerable to toxicosis and instituting surveillance programs using the most effective bioindicators.

Reduced trophic niche breadth is associated with higher productivity in a recovering apex predator population

AbstractUnderstanding the trophic and demographic dynamics of apex predators is of paramount importance for ecosystem conservation. Apex predators are usually free from predation, and thus, the main driver of population dynamics is the availability of trophic resources, which affects breeding performance. Albeit food habits have been described in many apex species, the effects of changes in the trophic niche on long‐term demographic dynamics are poorly known. We describe the long‐term (N = 25 years) changes in trophic niche breadth of a recovering golden eagle population in a Mediterranean landscape (northern Spain) and search for potential correlates with their productivity (i.e., number of fledged eaglets per controlled pair) as a proxy for their breeding performance (N = 290 pair‐years). We identified a total of 3475 prey items from 77 species; rabbits, hares, and red‐legged partridges were the most frequent prey consumed (overall 51%), followed by roe deer (10%), red‐billed chough, red foxes, and woodpigeons, all of them with >5%. Prey diversity in eagles' diet decreased during the study period. Consumption of rabbits and roe deer increased, while that of hares and partridges decreased; no significant trends were observed in the consumption of foxes, choughs, and woodpigeons. Prey diversity and red‐legged partridge consumption were negatively correlated with productivity at the territory level, while rabbits, corvids, and pigeons showed a strong positive relationship with productivity. The size of the territory showed the strongest positive relationship with productivity, while roughness was negatively correlated. Rabbits and ungulates showed negative and positive correlations with roughness, respectively, while predator consumption exhibited a negative relationship with the size of the territory. Our findings give insights into the potential trophic mechanisms driving the dynamics of recovering apex predator populations; a reduction in the trophic niche breadth toward specific groups of prey, which could be due to a higher availability and individual specificity toward these prey species, might enhance productivity at the territory level and, ultimately, influence population dynamics facilitating the recovery process. The remarkable increase in the consumption of wild ungulates, predators, and other apex consumers suggests that the current population recovery and reduced trophic niche breadth may facilitate this eagle population to exhibit its apex role and contribute to ecosystem functionality.

Bidirectional Power Sharing Control with Current-Fed Dual Active Bridge Converter: Hybrid Technique

This paper proposes a hybrid method for controlling bidirectional power sharing among grids and dc microgrids. The interlinking converter (IC) and current-fed dual active bridge (CFDAB) converters connect the grid and dc microgrid. The proposed approach is the joint implementation of golden eagle optimization and radial basis function neural network, hence, named the GEO-RBFNN approach. The bidirectional power distribution control signal analysis significantly diminishes power distribution deviation and circulating current. This power-sharing control system neglects the need for battery backup since the required amount of power is brought from or handled by an AC utility. The dc microgrid incorporates the dc to dc converter, from which boost converters use sources and buckconverters use supply loads. The control of bidirectional power sharing among the microgrids and the grid is attained with the aid of the GEO-RBFNN algorithm. It delivers certain aids, like higher predictability, highly reliable results, high speed, and less complexity. The outcome is investigated on the MATLAB platform. According to the comparison outcome, the proposed method delivers higher performance than the existing system. The proposed method gives higher comparison outcomes than the current methods, which can conquer the associated problems. The proposed method-based settling time is 3.356 ms, and the rise time is 1.555 ms. The efficiency based on the proposed method is 97.54%, which is higher than the existing methods.

Energy Efficient Routing Mechanism Based on Similarity Guided Graph Neural Network and Decision Tree with Golden Eagle Optimization in VSN

Energy Efficient Routing Mechanism Based on Similarity Guided Graph Neural Network and Decision Tree with Golden Eagle Optimization in VSN