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Experimental crack identification of API X70 steel pipeline using improved Artificial Neural Networks based on Whale Optimization Algorithm
Experimental crack identification of API X70 steel pipeline using improved Artificial Neural Networks based on Whale Optimization Algorithm
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Experimental crack identification of API X70 steel pipeline using improved Artificial Neural Networks based on Whale Optimization Algorithm

Ouladbrahim, A. ; Belaidi, I. ; Khatir, S. ; Magagnini, E. ; Capozucca, R. ; Abdel Wahab, M.

Mechanics of materials, 2022-03, Vol.166, p.104200, Article 104200 [Periódico revisado por pares]

Elsevier Ltd

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2
Prediction of resisting force and tensile load reduction in GFRP composite materials using Artificial Neural Network-Enhanced Jaya Algorithm
Prediction of resisting force and tensile load reduction in GFRP composite materials using Artificial Neural Network-Enhanced Jaya Algorithm
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Prediction of resisting force and tensile load reduction in GFRP composite materials using Artificial Neural Network-Enhanced Jaya Algorithm

Fahem, Noureddine ; Belaidi, Idir ; Oulad Brahim, Abdelmoumin ; Noori, Mohammad ; Khatir, Samir ; Abdel Wahab, Magd

Composite structures, 2023-01, Vol.304, p.116326, Article 116326 [Periódico revisado por pares]

Elsevier Ltd

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3
Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: A state of the art review and case study of a residential HVAC system
Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: A state of the art review and case study of a residential HVAC system
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Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: A state of the art review and case study of a residential HVAC system

Afram, Abdul ; Janabi-Sharifi, Farrokh ; Fung, Alan S. ; Raahemifar, Kaamran

Energy and buildings, 2017-04, Vol.141, p.96-113 [Periódico revisado por pares]

Lausanne: Elsevier B.V

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4
Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA
Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA
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Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA

Shukur, Osamah Basheer ; Lee, Muhammad Hisyam

Renewable energy, 2015-04, Vol.76, p.637-647 [Periódico revisado por pares]

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5
An improved Artificial Neural Network using Arithmetic Optimization Algorithm for damage assessment in FGM composite plates
An improved Artificial Neural Network using Arithmetic Optimization Algorithm for damage assessment in FGM composite plates
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An improved Artificial Neural Network using Arithmetic Optimization Algorithm for damage assessment in FGM composite plates

Khatir, Samir ; Tiachacht, Samir ; Le Thanh, Cuong ; Ghandourah, Emad ; Mirjalili, Seyedali ; Abdel Wahab, Magd

Composite structures, 2021-10, Vol.273, p.114287, Article 114287 [Periódico revisado por pares]

Elsevier Ltd

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6
Prediction of the peak load and crack initiation energy of dynamic brittle fracture in X70 steel pipes using an improved artificial neural network and extended Finite Element Method
Prediction of the peak load and crack initiation energy of dynamic brittle fracture in X70 steel pipes using an improved artificial neural network and extended Finite Element Method
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Prediction of the peak load and crack initiation energy of dynamic brittle fracture in X70 steel pipes using an improved artificial neural network and extended Finite Element Method

Oulad Brahim, Abdelmoumin ; Belaidi, Idir ; Fahem, Noureddine ; Khatir, Samir ; Mirjalili, Seyedali ; Abdel Wahab, Magd

Theoretical and applied fracture mechanics, 2022-12, Vol.122, p.103627, Article 103627 [Periódico revisado por pares]

Elsevier Ltd

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7
Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition
Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition
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Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition

Büyükşahin, Ümit Çavuş ; Ertekin, Şeyda

Neurocomputing (Amsterdam), 2019-10, Vol.361, p.151-163 [Periódico revisado por pares]

Elsevier B.V

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8
Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete
Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete
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Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete

Shariati, Mahdi ; Mafipour, Mohammad Saeed ; Mehrabi, Peyman ; Bahadori, Alireza ; Zandi, Yousef ; Salih, Musab N A ; Nguyen, Hoang ; Dou, Jie ; Song, Xuan ; Poi-Ngian, Shek

Applied sciences, 2019-12, Vol.9 (24), p.5534 [Periódico revisado por pares]

Basel: MDPI AG

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9
Modification of landslide susceptibility mapping using optimized PSO-ANN technique
Modification of landslide susceptibility mapping using optimized PSO-ANN technique
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Modification of landslide susceptibility mapping using optimized PSO-ANN technique

Moayedi, Hossein ; Mehrabi, Mohammad ; Mosallanezhad, Mansour ; Rashid, Ahmad Safuan A. ; Pradhan, Biswajeet

Engineering with computers, 2019-07, Vol.35 (3), p.967-984 [Periódico revisado por pares]

London: Springer London

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10
Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals
Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals
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Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals

Ben Ali, Jaouher ; Fnaiech, Nader ; Saidi, Lotfi ; Chebel-Morello, Brigitte ; Fnaiech, Farhat

Applied acoustics, 2015-03, Vol.89, p.16-27 [Periódico revisado por pares]

Elsevier Ltd

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