M
Maryam Safa
Researcher at Duy Tan University
Publications - 30
Citations - 1713
Maryam Safa is an academic researcher from Duy Tan University. The author has contributed to research in topics: Boundary value problem & Artificial neural network. The author has an hindex of 18, co-authored 24 publications receiving 1022 citations. Previous affiliations of Maryam Safa include University of Malaya.
Papers
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Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam's shear strength
Maryam Safa,Mahdi Shariati,Zainah Ibrahim,Ali Toghroli,Shahrizan Baharom,Norazman Mohamad Nor,Dalibor Petković +6 more
TL;DR: In this paper, the shear strength of steel-concrete composite beams was analyzed based on the respective variable parameters and the methodology used by ANFIS (Adaptive Neuro Fuzzy Inference System) was adopted for this purpose.
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Extremely large oscillation and nonlinear frequency of a multi-scale hybrid disk resting on nonlinear elastic foundation
Ali Shariati,Aria Ghabussi,Mostafa Habibi,Hamed Safarpour,Mehran Safarpour,Abdelouahed Tounsi,Maryam Safa +6 more
TL;DR: In this paper, a fundamental study on the nonlinear vibrations considering large amplitude in multi-sized hybrid nano-composites (MHC) disk (MHCD) relying on nonlinear elastic media and located in an environment with gradually changed temperature feature is presented.
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Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors
E. Sadeghipour Chahnasir,Yousef Zandi,Mahdi Shariati,E. Dehghani,Ali Toghroli,E. Tonnizam Mohamad,Ali Shariati,Maryam Safa,Karzan Wakil,Majid Khorami +9 more
TL;DR: SVM-FFA could be performed as a novel model with predictive strategy in the shear capacity estimation of angle shear connectors and produce a generalized performance and be learnt faster than the conventional learning algorithms.
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Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam
Ali Toghroli,Meldi Suhatril,Zainah Ibrahim,Maryam Safa,Mahdi Shariati,Shahaboddin Shamshirband +5 more
TL;DR: Estimation and prediction results of the ELM models were compared with genetic programming (GP) and artificial neural networks (ANNs) models and indicate that on the whole, the newflanged algorithm creates good generalization presentation.
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Development of neuro-fuzzy and neuro-bee predictive models for prediction of the safety factor of eco-protection slopes
Maryam Safa,Puteri Azura Sari,Mahdi Shariati,Meldi Suhatril,Nguyen Thoi Trung,Karzan Wakil,Majid Khorami +6 more
TL;DR: The neuro-fuzzy can provide a new applicable model to effectively predict the FOS of the slopes due to the fact that it is able to combine the advantages of the ANN and fuzzy inference system to indicate a high prediction capacity in solving problem of slope stability.