Pelican Optimization Algorithm with Federated Learning Driven Attack Detection model in Internet of Things environment

Al‐Wesabi, Fahd N.; Mengash, Hanan Abdullah; Marzouk, Radwa; Alruwais, Nuha; Allafi, Randa; Alabdan, Rana; Alharbi, Meshal; Gupta, Deepak

doi:10.1016/j.future.2023.05.029

Cited by 11 publications

(4 citation statements)

References 18 publications

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“…Once all individuals in the population have been updated, the subsequent iteration commences, and the series of steps outlined by Eq. ( 5) to ( 8) are reiterated until the entire execution process is completed [42]. The POA flowchart, which is displayed in Fig.…”

Section: Pelican Optimization Algorithm (Poa)mentioning

confidence: 99%

Student Performance Estimation Through Innovative Classification Techniques in Education

Fan,

Zhu,

Zhan

2024

IJACSA

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In the current era of intense educational competition, institutions must effectively classify individuals based on their abilities, proactively forecast student performance, and work towards enhancing their forthcoming examination outcomes. Providing early guidance to students is crucial in helping them focus their efforts on specific areas to boost their academic achievements. This analytical approach supports educational institutions in mitigating failure rates by utilizing students' previous performance in relevant courses to predict their outcomes in a specific program. Data mining encompasses a variety of techniques used to reveal hidden patterns within vast datasets. In the context of educational data mining, these methods are applied within the educational sphere, with a specific emphasis on analyzing data from both students and educators. These patterns can offer significant value for predictive and analytical objectives. In this study, Gaussian Process Classification (GPC) was employed for the prediction of student performance. To improve the model's accuracy, two cutting-edge optimizers, namely the Golden Eagle Optimizer (GEO) and the Pelican Optimization Algorithm (POA), were incorporated. When assessing the model's performance, four widely used metrics were utilized: Accuracy, Precision, Recall, and F1-score. The results of this study underscore the effectiveness of both the POA and GEO optimizers in enhancing GPC performance. Specifically, GPC+GEO demonstrated remarkable effectiveness in the Poor grade, while GPC+POA excelled in the Acceptable and Excellent category. This highlights the positive impact of these optimization techniques on the model's predictive capabilities.

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Section: Pelican Optimization Algorithm (Poa)mentioning

confidence: 99%

Student Performance Estimation Through Innovative Classification Techniques in Education

Fan,

Zhu,

Zhan

2024

IJACSA

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“…Here, z i,j indicates the amount of the jth variable determined by the ith candidate solution; N and m illustrate the number of swarm members and variables in the problem; rand is an accidental amount between (0, 1); and u j and l j define the jth higher and lower bound of the variables of the problem, respectively [9].…”

Section: Mathematical Expression Of the Offered Pomentioning

confidence: 99%

“…Today, PEMFCs are widely developed in electrical appliances, naval vessels, spacecraft, a large variety of lightweight utilizations, hybrid power, and heat systems. Each proton-exchange membrane FC has three major parts: the electrolyte, anode, and cathode [9]. These parts are usually connected via bipolar plates.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Optimal Design of the Proton-Exchange Membrane Fuel Cell Connected to the Network Utilizing an Improved Version of the Metaheuristic Algorithm

Guo,

Ghadimi

2023

Sustainability

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Fuel cells are a newly developed source for generating electric energy. These cells produce electricity through a chemical reaction between oxygen and hydrogen, which releases electrons. In recent years, extensive research has been conducted in this field, leading to the emergence of high-power batteries. This study introduces a novel technique to enhance the power quality of grid-connected proton-exchange membrane (PEM) fuel cells. The proposed approach uses an inverter following a buck converter that reduces voltage. A modified pelican optimization (MPO) algorithm optimizes the controller firing. A comparison is made between the controller’s performance, based on the recommended MPO algorithm and various other recent approaches, demonstrating the superior efficiency of the MPO algorithm. The study’s findings indicate that the current–voltage relationship in proton-exchange membrane fuel cells (PEMFCs) follows a logarithmic pattern, but becomes linear in the presence of ohmic overvoltage. Furthermore, the PEMFC operates at an impressive efficiency of 60.43% when running at 8 A, and it can deliver a significant power output under specific operating conditions. The MPO algorithm surpasses other strategies in terms of efficiency and reduction in voltage deviation, highlighting its effectiveness in managing the voltage stability, and improving the overall performance. Even during a 0.2 sagging event, the MPO-based controller successfully maintains the fuel cell voltage near its rated value, showcasing the robustness of the optimized regulators. The suggested MPO algorithm also achieves a superior accuracy in maintaining the voltage stability across various operating conditions.

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“…As a result, researchers have successfully applied the POA to various engineering problems, including network attack detection models [1], image issues [13][14], and asynchronous motor fault diagnosis [3]. However, when dealing with more complex engineering problems such as robot path planning, the algorithm still faces challenges such as falling into local optima, low accuracy, and slow convergence speed.…”

Section: Introductionmentioning

confidence: 99%

Multi-strategy Improved Pelican Optimization Algorithm for Mobile Robot Path Planning

Li,

Jiang,

Huang

2024

ITC

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In response to the problems of easily falling into local optima, low path planning accuracy, and slow convergence speed when applying the traditional pelican optimization algorithm to the mobile robot path planning problem, a multi-strategy improved pelican optimization algorithm (MPOA) is proposed. In the initialization stage, chaotic mapping is used to increase the diversity of the pelican population individuals. In the exploration stage, an adaptive feedback adjustment factor is proposed to adjust the local optima of pelican individuals’ positions and balance the algorithm’s local development capability. In the development stage, the Lévy flight strategy is introduced to adjust the domain radius of the pelican population individuals, and the Gaussian mutation mechanism is used to disturb individuals that have fallen into local optima. Simulation experimental results show that the improved algorithm has significantly improved and effectively shortened the length of the planned path.

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Pelican Optimization Algorithm with Federated Learning Driven Attack Detection model in Internet of Things environment

Cited by 11 publications

References 18 publications

Student Performance Estimation Through Innovative Classification Techniques in Education

Student Performance Estimation Through Innovative Classification Techniques in Education

Optimal Design of the Proton-Exchange Membrane Fuel Cell Connected to the Network Utilizing an Improved Version of the Metaheuristic Algorithm

Multi-strategy Improved Pelican Optimization Algorithm for Mobile Robot Path Planning

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