Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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

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Publisher: search.filters.publisher.ElsevierSubject: search.filters.subject.Artificial neural network

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  • Article
    Citation - Scopus: 3
    Development of Chrono-Spectral Gold Nanoparticle Growth Based Plasmonic Biosensor Platform
    (Elsevier, 2024) Sözmen, Alper Baran; Elveren, Beste; Erdoğan, Duygu; Mezgil, Bahadır; Baştanlar, Yalın; Yıldız, Ümit Hakan; Arslan Yıldız, Ahu
    Plasmonic sensor platforms are designed for rapid, label-free, and real-time detection and they excel as the next generation biosensors. However, current methods such as Surface Plasmon Resonance require expertise and well-equipped laboratory facilities. Simpler methods such as Localized Surface Plasmon Resonance (LSPR) overcome those limitations, though they lack sensitivity. Hence, sensitivity enhancement plays a crucial role in the future of plasmonic sensor platforms. Herein, a refractive index (RI) sensitivity enhancement methodology is reported utilizing growth of gold nanoparticles (GNPs) on solid support and it is backed up with artificial neural network (ANN) analysis. Sensor platform fabrication was initiated with GNP immobilization onto solid support; immobilized GNPs were then used as seeds for chrono-spectral growth, which was carried out using NH2OH at varied incubation times. The response to RI change of the platform was investigated with varied concentrations of sucrose and ethanol. The detection of bacteria E.coli BL21 was carried out for validation as a model microorganism and results showed that detection was possible at 102 CFU/ml. The data acquired by spectrophotometric measurements were analyzed by ANN and bacteria classification with percentage error rates near 0% was achieved. The proposed LSPR-based, label-free sensor application proved that the developed methodology promises utile sensitivity enhancement potential for similar sensor platforms. © 2024 The Author(s)
  • Book Part
    Citation - Scopus: 3
    Real-Time Flood Hydrograph Predictions Using Rating Curve and Soft Computing Methods (ga, Ann)
    (Elsevier, 2022) Tayfur, Gökmen
    This chapter introduces hydraulic and hydrologic flood routing methods in natural channels. It details hydrological flood routing methods of the Rating Curve and Muskingum. Based on the rating curve method (RCM), it presents real-time flood hydrograph predictions using the genetic algorithm (GA-based RCM) model. In addition, it presents how to make real-time flood hydrograph predictions using the artificial neural network (ANN). The chapter briefly introduces the basics of GA and details how to calibrate and validate the GA-based RCM model using measured real-time flood hydrographs. Similarly, after giving the basics of ANN, it shows how to train and test the ANN model using measured hydrographs. Real hydrograph simulations by the RCM, GA-based RCM, and ANN are presented, and merits of each model are discussed. © 2023 Elsevier Inc. All rights reserved.
  • Article
    Citation - WoS: 39
    Citation - Scopus: 42
    Prediction of Char Production From Slow Pyrolysis of Lignocellulosic Biomass Using Multiple Nonlinear Regression and Artificial Neural Network
    (Elsevier, 2021) Li, Ting Yan; Xiang, Huan; Yang, Yang; Wang, Jiawei; Yıldız, Güray
    Char produced from lignocellulosic biomass via slow pyrolysis have become one of the most feasible alternatives that can partially replace the utilisation of fossil fuels for energy production. In this study, the relationship between compositions of lignocellulosic biomass, operating conditions of slow pyrolysis, and characteristics of produced char have been analysed by using multiple nonlinear regression (MnLR) and artificial neural networks (ANN). Six input variables (temperature, solid residence time, production capacity, particle size, and fixed carbon and ash content) and five responses (char yield, and fixed carbon, volatile matter, ash content, HHV of produced char) were selected. A total of 57 literature references with 393-422 datasets were used to determine the correlation and coefficient of determination (R-2) between the input variables and responses. High correlation results (>0.5) existed between pyrolysis temperature and char yield (-0.502) and volatile matter of produced char (-0.619), ash content of feedstock and fixed carbon (-0.685), ash content (0.871) and HHV (-0.571) of produced char. Whilst the quadratic model was selected for the regression model, then the model was further optimised by eliminating any terms with p-values greater than 0.05. The optimised MnLR model results showed a reasonable prediction ability of char yield (R-2 = 0.5579), fixed carbon (R-2 = 0.7763), volatile matter (R-2 = 0.5709), ash (R-2 = 0.8613), and HHV (R-2 = 0.5728). ANN model optimisation was carried out as the results showed trainbr training algorithm, 10 neurons in the hidden layer, and tansig and purelin transfer function in hidden and output layers, respectively. The optimised ANN models had higher accuracy than MnLR models with the R-2 greater than 0.75, including 0.785 for char yield, 0.855 for fixed carbon, 0.752 for volatile matter, 0.951 for ash and 0.784 for HHV, respectively. The trained models can be used to predict and optimise the char production from slow pyrolysis of biomass without expensive experiments.