Application of hybrid forecast engine based intelligent algorithm and feature selection for wind signal prediction

Mir, Mahdi; Shafieezadeh, Mahdi; Heidari, Mohammad; Ghadimi, Noradin

doi:10.1007/s12530-019-09271-y

Cited by 180 publications

(35 citation statements)

References 21 publications

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“…The FRP features here is obtained through coupon test as. [27] Two nondimensional elements were inputted in the optimized AlexNet-ELM-ARFO as presented in the following equations…”

Section: The Methods Formulation and Parametric Analysismentioning

confidence: 99%

“…A general dataset, containing glass fiber‐reinforced polymers and carbon fiber‐reinforced polymers used for the continuous concrete circular column is completely described in. [ 27 ] This analyzed dataset contains 306 samples with carbon fiber‐reinforced polymers and 159 with glass fiber‐reinforced polymers, therefore, 465 samples have contained the datasets and were used in this investigation.…”

Section: The Methods Formulation and Parametric Analysismentioning

confidence: 99%

See 1 more Smart Citation

The Compressive Strength Prediction for FRP‐Confined Concrete in Circular Columns by Applying the Normalized AlexNet‐ELM and the Advanced Red Fox Optimization Algorithm

Cui

Zhao

et al. 2022

Advcd Theory and Sims

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Fiber Reinforced Polymers (FRP) can be widely utilized in civil engineering because of their helpful features like high corrosion resistance for aggressive surroundings and high strength to weight ratio. By providing a lateral confining pressure, the concrete compressive strength is increased. Giving an analytic model that can forecast the FRP strength is the purpose of this study. This model is according to the normalized AlexNet Extreme Learning Machine and the Advanced Red Fox Optimization Algorithm (AlexNet‐ELM‐ARFO). The AlexNet‐ELM‐ARFO algorithm's innovation includes a formulation of an analytic relation, which doesn't attend to the established effectiveness parameter that is in the models introduced in the literature. An extended empirical dataset is utilized for defining the suggested formulation's variables. The suggested model is applied to circular columns including continuous FRP. The predictions’ validation is shown by parametric research and the precision is examined by an empirical against theoretic comparison. A supplementary comparison is demonstrated with consideration of the theoretic prediction gained from the given method and the formulations’ results using significant design codes. Outcomes prove that the used method is adapted to the FRP‐confined concrete design and ensures an improved precision in comparison with respect to the available competitors.

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“…The FRP features here is obtained through coupon test as. [27] Two nondimensional elements were inputted in the optimized AlexNet-ELM-ARFO as presented in the following equations…”

Section: The Methods Formulation and Parametric Analysismentioning

confidence: 99%

Section: The Methods Formulation and Parametric Analysismentioning

confidence: 99%

The Compressive Strength Prediction for FRP‐Confined Concrete in Circular Columns by Applying the Normalized AlexNet‐ELM and the Advanced Red Fox Optimization Algorithm

Cui

Zhao

et al. 2022

Advcd Theory and Sims

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show abstract

“…Recently, several optimization problems have been introduced which are unable to be solved accurately in a reasonable time, or even solving them is impossible. Some of these problems which are mainly optimization problems are called NP‐Hard problems . This large family of problems has many interesting properties that have led to many approximate methods to solve each of them over time.…”

Section: Icab Algorithmmentioning

confidence: 99%

“…Some of these problems which are mainly optimization problems are called NP-Hard problems. 48,49 This large family of problems has many interesting properties that have led to many approximate methods to solve each of them over time. Meta-heuristic algorithms are one of these types of methods.…”

Section: Modeling Of the Systemmentioning

confidence: 99%

Application of the meta‐heuristics for optimizing exergy of a HT‐PEMFC

Han

Khodaei

2020

Int J Energy Res

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Summary The aim of this study is to analyze the exergy efficiency of a high‐temperature proton exchange membrane fuel cell. To do this purpose, meta‐heuristic technique has been used. First, the model of a membrane fuel cell is simulated and the polarization diagram shows a potent agreement with empirical data. Then, a new improved version of collective animal behavior algorithm is utilized for evaluating and optimizing the thermodynamic irreversibility, exergy efficiency, and work of the fuel cell. The algorithm uses opposition‐based learning and Lévy flight for improving the algorithm's premature convergence shortcoming. The result of this study shows that by comparison with standard collective animal behavior algorithm, genetic algorithm, and empirical results, the proposed algorithm has better achievements for both terms of optimal value finding and convergence strength.

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“…The incidence of breast cancer in men is less than 1%. 2 Breast cancer is most often seen in people above 50 years old and it is in those less than 35 is rare. Especially symptoms such as a breast cancer history in first or second degree relatives of the mother (mother, aunt, grandmother, and daughter), early menstruation and late menopause, 1 hormone replacement therapy, never getting pregnant or late pregnancy, not breastfeeding, having cancer in one of a woman's breasts in the past doubles the risk of cancer.…”

Section: Introductionmentioning

confidence: 99%

Breast cancer diagnosis based on guided Water Strider Algorithm

Liu

Youssefi

et al. 2021

Proc Inst Mech Eng H

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Breast cancer is one of the main cancers that effect of the women’s health. This cancer is one of the most important health issues in the world and because of that, diagnosis in the beginning and appropriate cure is very effective in the recovery and survival of patients, so image processing as a decision-making tool can assist physicians in the early diagnosis of cancer. Image processing mechanisms are simple and non-invasive methods for identifying cancer cells that accelerate early detection and ultimately increase the chances of cancer patients surviving. In this study, a pipeline methodology is proposed for optimal diagnosis of the breast cancer area in the mammography images. Based on the proposed method, after image preprocessing and filtering for noise reduction, a simple and fast tumors mass segmentation based on Otsu threshold segmentation and mathematical morphology is proposed. Afterward, for simplifying the final diagnosis, a feature extraction based on 22 structural features is utilized. To reduce and pruning the useless features, an optimized feature selection based on a new developed design of Water Strider Algorithm (WSA), called Guided WSA (GWSA). Finally, the features injected to an optimized SVM classifier based on GWSA for optimal cancer diagnosis. Simulations of the suggested method are applied to the DDSM database. A comparison of the results with several latest approaches are performed to indicate the method higher effectiveness.

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Application of hybrid forecast engine based intelligent algorithm and feature selection for wind signal prediction

Cited by 180 publications

References 21 publications

The Compressive Strength Prediction for FRP‐Confined Concrete in Circular Columns by Applying the Normalized AlexNet‐ELM and the Advanced Red Fox Optimization Algorithm

The Compressive Strength Prediction for FRP‐Confined Concrete in Circular Columns by Applying the Normalized AlexNet‐ELM and the Advanced Red Fox Optimization Algorithm

Application of the meta‐heuristics for optimizing exergy of a HT‐PEMFC

Breast cancer diagnosis based on guided Water Strider Algorithm

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