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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
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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Progress of artificial neural networks applications in hydrogen production
Progress of artificial neural networks applications in hydrogen production
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Progress of artificial neural networks applications in hydrogen production

Abdelkareem, Mohammad A. ; Soudan, Bassel ; Mahmoud, Mohamed S. ; Sayed, Enas T. ; AlMallahi, Maryam N. ; Inayat, Abrar ; Radi, Muaz Al ; Olabi, Abdul G.

Chemical engineering research & design, 2022-06, Vol.182, p.66-86 [Periódico revisado por pares]

Rugby: Elsevier Ltd

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Airblast prediction through a hybrid genetic algorithm-ANN model
Airblast prediction through a hybrid genetic algorithm-ANN model
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Airblast prediction through a hybrid genetic algorithm-ANN model

Jahed Armaghani, Danial ; Hasanipanah, Mahdi ; Mahdiyar, Amir ; Abd Majid, Muhd Zaimi ; Bakhshandeh Amnieh, Hassan ; Tahir, Mahmood M. D.

Neural computing & applications, 2018-05, Vol.29 (9), p.619-629 [Periódico revisado por pares]

London: Springer London

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An optimized ANN model based on genetic algorithm for predicting ripping production
An optimized ANN model based on genetic algorithm for predicting ripping production
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An optimized ANN model based on genetic algorithm for predicting ripping production

Mohamad, Edy Tonnizam ; Faradonbeh, Roohollah Shirani ; Armaghani, Danial Jahed ; Monjezi, Masoud ; Majid, Muhd Zaimi Abd

Neural computing & applications, 2017-12, Vol.28 (Suppl 1), p.393-406 [Periódico revisado por pares]

London: Springer London

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Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models
Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models
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Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models

Chen, Hui ; Asteris, Panagiotis G. ; Jahed Armaghani, Danial ; Gordan, Behrouz ; Pham, Binh Thai

Applied sciences, 2019, Vol.9 (6), p.1042 [Periódico revisado por pares]

Basel: MDPI AG

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Prediction of ultimate bearing capacity through various novel evolutionary and neural network models
Prediction of ultimate bearing capacity through various novel evolutionary and neural network models
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Prediction of ultimate bearing capacity through various novel evolutionary and neural network models

Moayedi, Hossein ; Moatamediyan, Arash ; Nguyen, Hoang ; Bui, Xuan-Nam ; Bui, Dieu Tien ; Rashid, Ahmad Safuan A.

Engineering with computers, 2020-04, Vol.36 (2), p.671-687 [Periódico revisado por pares]

London: Springer London

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Benchmarking Artificial Neural Network Architectures for High-Performance Spiking Neural Networks
Benchmarking Artificial Neural Network Architectures for High-Performance Spiking Neural Networks
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Benchmarking Artificial Neural Network Architectures for High-Performance Spiking Neural Networks

Islam, Riadul ; Majurski, Patrick ; Kwon, Jun ; Sharma, Anurag ; Tummala, Sri Ranga Sai Krishna

Sensors (Basel, Switzerland), 2024-02, Vol.24 (4), p.1329 [Periódico revisado por pares]

Switzerland: MDPI AG

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A CNN–LSTM model for gold price time-series forecasting
A CNN–LSTM model for gold price time-series forecasting
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A CNN–LSTM model for gold price time-series forecasting

Livieris, Ioannis E. ; Pintelas, Emmanuel ; Pintelas, Panagiotis

Neural computing & applications, 2020-12, Vol.32 (23), p.17351-17360 [Periódico revisado por pares]

London: Springer London

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An Adaptive Network-Based Reinforcement Learning Method for MPPT Control of PMSG Wind Energy Conversion Systems
An Adaptive Network-Based Reinforcement Learning Method for MPPT Control of PMSG Wind Energy Conversion Systems
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An Adaptive Network-Based Reinforcement Learning Method for MPPT Control of PMSG Wind Energy Conversion Systems

Wei, Chun ; Zhang, Zhe ; Qiao, Wei ; Qu, Liyan

IEEE transactions on power electronics, 2016-11, Vol.31 (11), p.7837-7848 [Periódico revisado por pares]

New York: IEEE

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Performance & emission analysis of HHO enriched dual-fuelled diesel engine with artificial neural network prediction approaches
Performance & emission analysis of HHO enriched dual-fuelled diesel engine with artificial neural network prediction approaches
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Performance & emission analysis of HHO enriched dual-fuelled diesel engine with artificial neural network prediction approaches

Kenanoğlu, Raif ; Baltacıoğlu, Mustafa Kaan ; Demir, Mehmet Hakan ; Erkınay Özdemir, Merve

International journal of hydrogen energy, 2020-10, Vol.45 (49), p.26357-26369 [Periódico revisado por pares]

Elsevier Ltd

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