WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653
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Article System-Level Prediction and Optimization of Cyclone Separator Performance Using a Hybrid CFD-DEM-ANN Approach(MDPI, 2026) Kocak, EyupIn this study, the separation performance of cyclone separators with different geometric configurations was investigated using a hybrid approach that combines Computational Fluid Dynamics, the Discrete Element Method, and Artificial Neural Networks. In the first stage, the flow field was solved using the Reynolds-Averaged Navier-Stokes equations together with the Reynolds Stress Model turbulence closure, and particle motion was evaluated in detail through DEM. To examine the effect of geometric parameters, the inlet aspect ratio, vortex finder diameter, and cylinder height were systematically assessed. The results revealed the formation of a pronounced Rankine-type vortex structure inside the cyclone and showed that secondary flow regions intensified as the vortex finder diameter and cylinder height increased, thereby reducing the separation efficiency. In the inlet section, an optimal aspect ratio was identified. In the second stage, an ANN model was developed to expand the limited dataset obtained from the CFD-DEM analyses. By optimizing the activation function and the number of neurons, the best performance was achieved with a ReLU-based neural network containing a single hidden neuron, reaching a test-set accuracy of approximately R2 approximate to 0.991 and an overall fit of R2 approximate to 0.895. The ANN model also captured interaction trends between flow velocity and geometry that could not be observed with the limited CFD dataset. This hybrid approach provides an effective and low-cost method for performance prediction and optimization in cyclone separator design.Article A Meta-Heuristic Stochastic Algorithm for the Numerical Treatment of Cancer Model through the Chemotherapy and Stem Cells(Elsevier, 2026) Baleanu, Dumitru; Defterli, Ozlem; Sabir, Zulqurnain; Abdelkawy, M. A.Objective: The aim of current research is to present the numerical performances of the cancer treatment model based on chemotherapy and stem cells using one of the heuristic computing neural network procedures. The cancer treatment model through chemotherapy and stem cells is categorized into stem cells, affected cells, tumor cells, and chemotherapy-based concentration drug. Method: A process of artificial neural network is applied using the hybrid optimization of global and local search schemes, which are taken as genetic algorithm (GA) and an active set (AS). An error-based fitness function is designed by using the differential model and then optimized by the hybridization of both global and local search schemes. GA is applied to exploit the global result and give a primary guess to the AS that further improves the results locally. AS is rooted in the GA, where GA produces new populaces and AS optimizes the fitness function for every individual. The hybridization of these two schemes is used iteratively for purifying the results. Ten numbers of neurons and log-sigmoid activation functions has been used to solve the cancer treatment model based on chemotherapy and stem cells. Results: For the correctness of the stochastic solver, the obtained numerical results have been compared with any traditional scheme. Moreover, the reliability and capability of the scheme are performed through the absolute error around 10-05 to 10-07 along with different statistical approaches for solving the mathematical model. Novelty: The proposed artificial neural network structure along with the hybrid optimization of global and local search schemes has never been implemented before to solve the cancer treatment model based on chemotherapy and stem cells.Article Comprehensive Analysis of Data Augmentation Methods in Classification for an Imbalanced Epilepsy Dataset(Institute of Electrical and Electronics Engineers Inc., 2026) Calis, A.G.; Ergezer, H.Imbalanced class distribution reduces the generalizability of classifiers in EEG-based epilepsy detection. This study examines the impact of the synthetic minority oversampling technique (SMOTE) and its variants on imbalanced electroencephalography (EEG) data, utilizing an end-to-end data processing pipeline. Band-limited filtering is applied as pre-processing, and then the training data is gradually oversampled by 20% increments in four scenes. Experiments are conducted on coarse-k-nearest neighbor (Coarse-KNN), bagged trees, and artificial neural network (ANN) classifiers, and evaluation is performed using accuracy, precision, recall, F1 score, and Matthew’s correlation coefficient (MCC) metrics. In Scene #4, where the inter-class imbalance is eliminated, Borderline-SMOTE yielded the highest and most consistent results (F1 Score = 0.903–0.937, MCC = 0.830–0.894). Safe level-SMOTE (SL-SMOTE) and SMOTE/Geometric-SMOTE(G-SMOTE) produced second-ranked results. The findings demonstrate that appropriate variant selection provides consistent gains even across classifiers, making Borderline-SMOTE the recommended approach for imbalanced EEG classification. Furthermore, in the detailed analysis of ensemble sampling limits, SMOTE-based combined approaches (e.g., SL + G SMOTE) also produced consistent results. Basic descriptive statistics (mode, median, variance, and kurtosis) of the synthetic samples were found to be comparable to those of the real data, providing additional evidence of distributional consistency. © 2013 IEEE.Conference Object Citation - WoS: 1Citation - Scopus: 1Dengesiz Epilepsi Veri Seti İçin Sınıflandırmada Farklı SMOTE Yöntemlerinin Etkileri(Institute of Electrical and Electronics Engineers Inc., 2025) Calis, Ahmet Gokay; Ergezer, HalitIn this study, the effects of different SMOTE methods on machine learning algorithms for the imbalanced epilepsy dataset were investigated. After filtering, the imbalanced dataset was balanced with 5 different SMOTE methods and classified with various machine learning algorithms. Coarse-K-Nearest Neighbor, Bagged Trees, and Artificial Neural Networks models were evaluated in epilepsy detection. The performance of these different models was compared with Matthews Correlation Coefficient (MCC) and F1 Score metrics. The results showed that the Borderline-SMOTE algorithm had the highest F1 Score and MCC values among all machine learning algorithms. © 2025 Elsevier B.V., All rights reserved.Article Citation - WoS: 1Citation - Scopus: 1Determination of Chemisorption Probabilities of Hydrogen Molecules on a Nickel Surface by Artificial Neural Network(Croatian Chemical Soc, 2008) Güvenç, Ziya Burhanettin; Boeyuekata, Mustafa; Kocyigit, Yuecel; Guevenc, Ziya B.; Böyükata, Mustafa; Bilgisayar MühendisliğiDissociative chemisorption probabilities for H-2(v, j) + Ni(100) collision systems have been estimated by using Artificial Neural Network (ANN). For training, previously determined probability values via molecular dynamics simulations have been used. Performance of the ANN, for predicting any quantities in the molecule-surface interaction, has been investigated. Effects of the surface sites and the rovibrational states of the molecule on the process are analyzed. The results are in good agreement with the related previous studies.Article Citation - WoS: 2Citation - Scopus: 3An Application of Principal Component Analysis - Artificial Neural Network for the Simultaneous Quantitative Analysis of a Binary Mixture System(Chiminform Data S A, 2009) Dinc, Erdal; Baleanu, Dumitru; Sen Koktas, Nigar; Köktaş, Nigar; Baleanu, Dumitru; Kökias, Nigar Şen; MatematikArtificial neural networks (ANNs) based on the use of principal components and the original absorbance data were proposed for the simultaneous quantitative analysis of amlodipine (AML) and atorvastatin (ATO) in tablets. A concentration set of mixtures containing ATO and AML in different concentration composition between 0.0-20.0 mu g/mL was prepared in methanol. The measured absorbance data matrix for the concentration data set was obtained and the principal components were extracted. In the next step five principal components were selected as an input data for the artificial neural network. This combined approach was named principal components-artificial neural network (PCA-ANN). The same problem was solved by using the application of the artificial neural network to the original absorbance data matrix. This approach was denoted as ANN. The classical ANN approach was used as a comparison method. Both PCA-ANN and ANN methods were tested by analyzing various synthetic mixtures corresponding to the validation set of AML and ATO compounds. The proposed methods were successfully applied to the quantitative analysis of the commercial tablets and a coincidence was reported between the proposed methods.Conference Object Citation - Scopus: 13Predicting Flight Delays With Artificial Neural Networks: Case Study of an Airport(Ieee, 2017) Demir, Engin; Demir, Vahap BurhanAir transportation has an important place among transportation systems and it is indispensable for the flights to perform their voyages in scheduled time in order to ensure the comfort of passengers and controllability of operational costs. There are several reasons for flight delays like weather conditions, excessive intensity in air traffic, accidents or closed airfields, conditions that will lead to an increase in distances between planes and operational delays in ground services. In this study, using the data collected from the sensors located in the airport and the information about the flight, the goal is develop a machine learning model to estimate departure delays of flights using artificial neural networks.Conference Object Citation - WoS: 2Citation - Scopus: 4Estimation of Cross Sections for Molecule-Cluster Interactions by Using Artificial Neural Networks(Springer, 2006) Boyukata, Mustafa; Kocyigit, Yucel; Guvenc, Ziya B.The cross sections Of D-2 (v,j) + Ni-n (T), n = 19 and 20, collision systems have been estimated by using Artificial Neural Networks (ANNs). For training, previously determined cross section values via molecular dynamics simulation have been used. The performance of the ANNs for predicting any quantities in molecule-cluster interaction has been investigated. Effects of the temperature of the clusters and the rovibrational states of the molecule are analyzed. The results are in good agreement with previous studies.Article Citation - WoS: 1Citation - Scopus: 1Design and Experimental Verification of a Posture Correction System: Development of an Artificial Neural Network To Predict the Effectiveness of the Developed System To Correct Poor Posture(Taylor & Francis inc, 2024) Yildiz, Eren; Das, MemikThis research aims to address designing an experiment to evaluate the impact of a developed posture correction system. Also, the correct posture learning habits of users can be estimated with an artificial neural network (ANN) structure that predicts the poor posture count (PPC) in the last session of the experiment using the information received from the users and the developed system. The developed system aims to collect data from different individuals about their sitting posture information. An ANN analysis tool is developed to predict the individuals' habits of learning the correct posture. This setup is based on a flex sensor and has the capability of collecting posture information data and warning the user when the posture is not correct. A three-session experiment was conducted on 12 healthy participants to investigate his/her posture habits. The data was analyzed to determine the average PPC value. It was observed that PPC decreased by 56.27% from session one to session three, and the average improvement evaluation (IE) value after each session was found to be positive. In addition to experimental analysis, the collected posture data was used to train and validate an ANN architecture capable of predicting PPC values. The developed device is effective in improving posture habits and has the potential to predict PPC values with the ANN architecture.Article Citation - WoS: 8Citation - Scopus: 9Numerical Computational Heuristic Through Morlet Wavelet Neural Network for Solving the Dynamics of Nonlinear Sitr Covid-19(Tech Science Press, 2022) Alnahdi, Abeer S.; Jeelani, Mdi Begum; Abdelkawy, Mohamed A.; Raja, Muhammad Asif Zahoor; Baleanu, Dumitru; Hussain, Muhammad Mubashar; Sabir, ZulqurnainThe present investigations are associated with designing Morlet wavelet neural network (MWNN) for solving a class of susceptible, infected, treatment and recovered (SITR) fractal systems of COVID-19 propagation and control. The structure of an error function is accessible using the SITR differential form and its initial conditions. The optimization is performed using the MWNN together with the global as well as local search heuristics of genetic algorithm (GA) and active-set algorithm (ASA), i.e., MWNN-GA-ASA. The detail of each class of the SITR nonlinear COVID-19 system is also discussed. The obtained outcomes of the SITR system are compared with the Runge-Kutta results to check the perfection of the designed method. The statistical analysis is performed using different measures for 30 independent runs as well as 15 variables to authenticate the consistency of the proposed method. The plots of the absolute error, convergence analysis, histogram, performance measures, and boxplots are also provided to find the exactness, dependability and stability of the MWNN-GA-ASA.