Improving Parameter Estimation of Fuel Cell Using Honey Badger Optimization Algorithm

Almodfer, Rolla; Elaziz, Mohamed Abd; Alshathri, Samah; Abualigah, Laith; Mudhsh, Mohammed; Shahzad, Khurram; Issa, Mohamed

doi:10.3389/fenrg.2022.875332

Cited by 18 publications

(5 citation statements)

References 43 publications

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“…In the honey step, the honey badgers follow honey birds for determining the beehive. In the digging step, the prey is determined based on the smelling ability of honey badgers [ 38 , 39 ]. First, we initialized the agents, as shown in Equation (8).…”

Section: Methodsmentioning

confidence: 99%

“…The extracted 4463 vectors were fed to the HBOA for feature optimization. HBOA follows the honey badger's behavior to catch the prey, and this optimization a rithm has two main steps (digging and honey) for resolving the optimization probl In the honey step, the honey badgers follow honey birds for determining the beehiv the digging step, the prey is determined based on the smelling ability of honey bad [38,39]. First, we initialized the agents, as shown in Equation (8).…”

Section: Feature Optimizationmentioning

confidence: 99%

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fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network

Chelladurai,

Narayan,

Divakarachari

et al. 2023

Brain Sciences

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In the present scenario, Alzheimer’s Disease (AD) is one of the incurable neuro-degenerative disorders, which accounts for nearly 60% to 70% of dementia cases. Currently, several machine-learning approaches and neuroimaging modalities are utilized for diagnosing AD. Among the available neuroimaging modalities, functional Magnetic Resonance Imaging (fMRI) is extensively utilized for studying brain activities related to AD. However, analyzing complex brain structures in fMRI is a time-consuming and complex task; so, a novel automated model was proposed in this manuscript for early diagnosis of AD using fMRI images. Initially, the fMRI images are acquired from an online dataset: Alzheimer’s Disease Neuroimaging Initiative (ADNI). Further, the quality of the acquired fMRI images was improved by implementing a normalization technique. Then, the Segmentation by Aggregating Superpixels (SAS) method was implemented for segmenting the brain regions (AD, Normal Controls (NC), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Significant Memory Concern (SMC)) from the denoised fMRI images. From the segmented brain regions, feature vectors were extracted by employing Gabor and Gray Level Co-Occurrence Matrix (GLCM) techniques. The obtained feature vectors were dimensionally reduced by implementing Honey Badger Optimization Algorithm (HBOA) and fed to the Multi-Layer Perceptron (MLP) model for classifying the fMRI images as AD, NC, MCI, EMCI, LMCI, and SMC. The extensive investigation indicated that the presented model attained 99.44% of classification accuracy, 88.90% of Dice Similarity Coefficient (DSC), 90.82% of Jaccard Coefficient (JC), and 88.43% of Hausdorff Distance (HD). The attained results are better compared with the conventional segmentation and classification models.

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Section: Methodsmentioning

confidence: 99%

Section: Feature Optimizationmentioning

confidence: 99%

fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network

Chelladurai,

Narayan,

Divakarachari

et al. 2023

Brain Sciences

View full text Add to dashboard Cite

show abstract

“…To extract the ECM applicable to FCs, electrochemical impedance spectroscopy has been applied under different working conditions [72], allowing the impedance data of the FC to be determined at various inlet pressures, operating temperatures, air stoichiometry conditions, and air humidity conditions. Reference [73] addressed the Randles circuit modelling of a PEMFC by identifying the coefficients related to the output voltage using the honey badger optimization algorithm.…”

Section: Fuel Cell Dynamic Modelmentioning

confidence: 99%

Optimal Sizing and Dynamic Energy Source Characteristics of Hybrid Electric Vehicles: A Comprehensive Review and Future Directions

Prasanthi,

Shareef,

Errouissi

et al. 2024

IEEE Access

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The automobile industry is regarded as one of the primary producers of greenhouse gas emissions. Hence, one major approach for promoting global sustainability is vehicle electrification. Energy source hybridization is imperative in vehicle electrification because no alternative clean energy source can match the performance of vehicles with internal combustion engines. For a compact energy source system with minimal source degradation and low operational costs, it is crucial to determine the optimal sizing of multiple sources in a hybrid electric vehicle (HEV). The objective of this article is to conduct a thorough assessment of the optimal energy source sizing methodology for HEV alongside an analysis of energy management systems. The importance of dynamic energy source characteristics in the optimal source design is explained and the dynamic modelling of these sources is investigated in detail. Moreover, this paper discusses the dynamic source degradation models used in the literature. This analysis on HEV component sizing provides a comprehensive picture of the current state-of-the-art and highlights several shortcomings, difficulties, and knowledge gaps. The findings indicate that the establishment of an energy management system and energy source sizing are interdependent activities that can prolong the usable life of the energy sources and reduce energy consumption. The aging, temperature, depth of discharge, and charging state are found to be essential characteristics of vehicle energy sources, yet these dynamic aspects have largely been neglected in previous papers. Additionally, if the source deterioration and associated factors are considered throughout the design phase, the HEV system can be built with long-life characteristics. The assessment conclusions highlight the significance of continued analysis to overcome obstacles and improve the hybrid automobile sector.

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“…The successes of the models must be calculated sensitively and the model success rates must be higher. In these situations, some complex structures are proposed by researchers to improve parameter estimations in the models [30], [31]. Last but not least, there are many applications and proposals to improve parameter estimations in the applications for some specific research fields.…”

Section: Literature Reviewmentioning

confidence: 99%

A Method to Improve Parameter Estimation Success

Unozkan

2023

IEEE Access

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This paper introduces a method for improving parameter estimation in statistical models. Parameter estimation is a popular area of study in statistics, and recent years have seen the introduction of new distributions with more parameters to enhance modelling success. While finding a suitable model for a dataset is crucial, accurately estimating parameters is equally important. In some cases, classical parameter estimation methods fail to provide a closed form of estimation for parameters. As a result, researchers commonly resort to numerical methods and software programs for parameter estimation in models. The success rates of models have gained significance with the rising popularity of novel techniques like machine learning algorithms and artificial neural networks. Robust and reliable models are built on the strong theoretical foundations of statistical distributions. Specific distributions are used in various research fields to model datasets, and the assumptions associated with these distributions provide valuable insights into observations. Additionally, parameter estimation results sometimes lead researchers to direct conclusions. This paper presents an improvement method that relies on the estimation of parameters from other statistical distributions. This novel approach aims to make parameter estimation easier and more successful in certain situations. In the applications in this paper, the proposed methodology improves the success rate by up to 10% which provides an additional 6% success in the models.

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Improving Parameter Estimation of Fuel Cell Using Honey Badger Optimization Algorithm

Cited by 18 publications

References 43 publications

fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network

fMRI-Based Alzheimer’s Disease Detection Using the SAS Method with Multi-Layer Perceptron Network

Optimal Sizing and Dynamic Energy Source Characteristics of Hybrid Electric Vehicles: A Comprehensive Review and Future Directions

A Method to Improve Parameter Estimation Success

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