Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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COAR Access Rights: search.filters.coarAccessRights.metadata only accessSubject: search.filters.subject.Artificial Neural Networks

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  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Data Driven Modeling and Design of Cellulose Acetate-Polysulfone Blend Ultrafiltration Membranes Based on Artificial Neural Networks
    (Elsevier Ltd, 2025) Gungormus, E.
    This study aimed to develop and validate an Artificial Neural Networks (ANNs) model for the design and optimization of cellulose acetate-polysulfone blend ultrafiltration membranes, produced via the Non-Solvent Induced Phase Separation method. After some data science applications on a comprehensive dataset obtained from literature studies, the ultimate ANNs model exhibited superior predictive capabilities and effectively captured complex nonlinear relationships in the data. The optimum model configuration with a single hidden layer containing six neurons provided reliable predictions by avoiding overfitting and underfitting risks and significantly reducing error metrics. The model analyzed the effects of input variables on outputs, revealing that different stages of the membrane preparation process had varying impacts on performance metrics. This finding emphasized the importance of systematically optimizing the preparation process to enhance overall membrane performance. The model's predictions showed strong agreement with experimental data, further validating its accuracy. The optimum production conditions identified by the model offered significant improvements in membrane performance. Moreover, the model accelerated the membrane development process by reducing the required number of experimental trials and promoting efficient resource utilization. This approach contributed to both economic and environmental sustainability by reducing production costs and energy consumption. This study highlighted the significant potential of machine learning techniques for future innovations and advancements in this field by enabling precise, efficient, and sustainable membrane design and synthesis. © 2025 Elsevier Ltd.
  • Conference Object
    Investigation of the Effect of Artificial Neural Network Performance Parameters and Training Dataset on the Probability Estimate Capacity in Structural Reliability Problems
    (Springer international Publishing Ag, 2024) Koroglu, F. B.; Maguire, M.; Akta, E.
    This study highlights two of the important details of the implementation of artificial neural networks to the structural reliability problems by pointing out the effect of training dataset, and the relationship between the performance parameters (coefficient of determination of train, validation, and test sets) of a network and its probability estimation capacity when it is used as a surrogate model in structural reliability problems. Four numerical examples are covered regarding these key aspects including one that is derived from a real-life reinforced concrete structure. Results have shown that the dataset can affect the probability estimation capacity for complex problems. Furthermore, it is also observed that having a neural network with good performance parameters does not mean that the network always has good probability estimation capacity. However, in order to have a network that can be used for probability estimate purposes, its performance parameters must be at a satisfactory level.
  • Conference Object
    Düşük Sükroz Derişimlerinin Görünür Bölge Spektroskopisi ve Yapay Sinir Aǧları ile Kestirimi
    (IEEE, 2017) Mezgil, Bahadir; Erdogan, Duygu; Alduran, Yesim; Yildiz, Umit Hakan; Yildiz, Ahu Arslan; Bastanlar, Yahn
    Low sucrose concentrations in solutions is estimated by means of localized surface plasmon resonance of immobilized gold nanoparticles. The ultraviolet-visible spectra (UV-Vis) of samples with different sucrose concentrations were prepared and used to train artificial neural networks. In our study, MATLAB Neural Networks Toolbox was used and effect of different input sizes and network structures on the estimation accuracy is investigated. It is observed that using complete spectrum instead of peak point results in higher accuracy.