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MakaleAçık Erişim2026/r/bayburt/14286
Improvement of machine learning-based diabetes diagnosis via resampling techniques
Yapici, Irem Senyer; Arslan, Rukiye Uzun; Engin, Mustafa Alptekin
The objective of this study is to enhance the accuracy of diabetes diagnosis through the utilisation of machine learning techniques and resampling methods. The imbalanced nature of diabetes datasets presents a significant challenge for traditional classification algorithms, which often struggle to accurately predict results. In order to enhance the efficacy of the model, a comparative analysis was conducted to assess the performance of a range of over-sampling and under-sampling techniques, including SMOTE, ADASYN, Borderline SMOTE, SVM SMOTE, Random Under Sampler, Near Miss, One Sided Selection, Neighbourhood Cleaning Rule, Edited Nearest Neighbours, Instance Hardness Threshold, AllKNN and Tomek Links. The aforementioned techniques were then applied to the Decision Tree, Random Forest, K-Nearest Neighbours, AdaBoost, Extra Tree Classifier, and machine learning classifiers, and their performance was evaluated using the accuracy, recall, precision, F-Score, and AUC-ROC performance metrics. The SVMSMOTE resampling technique was identified as the most successful method, achieving 99.06% accuracy when used in combination with the decision tree classifier. The findings demonstrate that the incorporation of resampling techniques markedly enhances diagnostic proficiency and yields more dependable forecasts. This research makes a significant contribution to the field of medical informatics, providing a robust framework for diabetes diagnosis and offering valuable insights into the application of machine learning healthcare.
İncele
MakaleAçık Erişim2026Scopus SJRQ2/r/bayburt/11555
Machine Learning-Based Material Thickness and Type Classification via Scattering Parameters
Engin, Mustafa Alptekin; Cakir, Mehmet
The precise determination of material type and thickness is of major significance in non-destructive testing, quality assurance, and materials science, as it influences the functionality, reliability, and performance of materials in engineering applications. This study proposes a methodology for the classification of material thickness and type through the analysis of scattering parameters within the 8–12 GHz frequency range. A database was created, encompassing real, imaginary, and dB values of reflection and transmission parameters for nine real-world materials with thicknesses ranging from 1 to 10 mm. This study addressed two main classification tasks, namely material thickness and material type. A variety of training–testing splits were employed in conjunction with 10-fold cross-validation to facilitate a comparison of classifier performance. In the material type classification, incorporating multiple thickness levels of each material enabled the model to distinguish materials with similar reflection characteristics more accurately, thereby enhancing discrimination performance. The results showed that Fine KNN consistently achieved 100% accuracy in thickness classification, while Quadratic SVM achieved 100% accuracy in material type classification, even when utilising only three thickness levels. Furthermore, the 90% training–10% testing split yielded the highest performance in thickness classification, whereas the optimal data split for material type classification differed across classifiers. Overall, this study demonstrates that the combination of scattering parameters and machine learning serves as a reliable and efficient approach for non-destructive material characterization.
AtıfScopus1WoS1OpenAlex1
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MakaleYalnız künye2026Scopus SJRQ2/r/bayburt/11378
Leakage-safe benchmarking of conventional chemometric and deep learning models for food NIR spectroscopy regression
Akpolat, Hacer; Akpolat, Mehmet Zahid
Near-infrared (NIR) spectroscopy is widely used in food systems for rapid, non-destructive estimation of compositional and quality attributes. Although machine-learning and deep-learning models are increasingly reported for spectral regression, practical model selection is often hindered by heterogeneous validation setups and the risk of information leakage during preprocessing and tuning. In this study, we provide a leakage-safe benchmark across nine food-related regression tasks spanning different sample sizes and spectral dimensionalities. Conventional chemometric and regression baselines (partial least squares regression, ridge regression, and support vector regression) are compared with lightweight deep-learning baselines (a shallow artificial neural network and a one-dimensional convolutional neural network) under a unified leakage-safe evaluation framework combining nested cross-validation with acomplementary fixed held-out test-set assessment, fold-wise preprocessing, and repeated training for stochastic models. Performance is summarized primarily using prediction error (RMSE in nested cross-validation and RMSEP on the held-out test set), while the coefficient of determination is retained as a secondary descriptive measure, complemented by cross-validation and fixed held-out-test learning-curve analyses together with model-complexity analysis. Across both validation analyses, ridge regression achieved the largest number of numerical task-level wins, while partial least squares regression and support vector regression remained competitive across multiple tasks. Paired randomization tests following van der Voet further showed that some small numerical differences between models were not statistically distinguishable. Artificial neural network and one-dimensional convolutional neural network baselines did not provide a reliable generalization advantage and showed greater variability in some data-limited settings. From a food-measurement perspective, these findings indicate that well-validated conventional models remain a strong default for routine NIR calibration workflows and non-destructive quality assessment. Deep learning appears most justified when calibration settings provide larger datasets, stronger experimental control, or spectroscopy-specific inductive bias that can translate added model complexity into practical measurement gains. Overall, this study serves as a controlled benchmark and reference for model selection under realistic food-spectroscopy data regimes.
AtıfOpenAlex1
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MakaleAçık Erişim2026Scopus SJRQ1/r/bayburt/11323
A Single-Source Pilot Study of Machine Learning-Assisted Microwave S-Parameter Screening for Glucose Syrup and Water Adulteration in Grape Molasses
Engin, Mustafa Alptekin; Cakir, Mehmet; Cakil, Turan
Grape molasses, known as pekmez in Turkish, is a traditional concentrated fruit product that may be adulterated with cheaper sweeteners or water. This study evaluated broadband microwave S-parameter measurements as a rapid, non-destructive screening approach for detecting glucose syrup substitution and water dilution in grape molasses. Nine physical mixture groups were prepared, including pure grape molasses, glucose syrup–substituted mixtures at 5–30%, pure glucose syrup as an endpoint reference, and water-diluted mixtures at 10–30%. For each group, five consecutive technical measurements were recorded using a Libre vector network analyzer connected to a WR-229 waveguide-based two-port setup over 3.30–4.90 GHz. All mixtures were prepared from a single commercial grape molasses source and a single glucose syrup source; the results should therefore be interpreted as pilot-scale, single-source evidence rather than a generalizable screening method. Glucose syrup substitution produced a systematic upward shift in S11 resonance frequency. In the practical 0–30% range, S11 resonance frequency showed a strong linear relationship with glucose syrup content on the calibration data (R2 = 0.9955; inverse prediction error = 0.72 percentage points), though this reflects calibration fit rather than independently validated prediction accuracy. Water dilution produced stronger S21 attenuation. The detection principle is based on the complementary use of S11 resonance behavior for glucose syrup substitution and S21 transmission loss for water dilution. In group-blocked point-wise classification, the Ensemble Bagged Trees classifier achieved 88.79% accuracy and a macro-F1 score of 0.874. These results indicate that S11 and S21 provide complementary proof-of-concept indicators for controlled-mixture screening of grape molasses adulteration.
İncele
MakaleAçık Erişim2026Scopus SJRQ1/r/bayburt/11273
The 20-Year Evolution of Distance Education: A Bibliometric and LDA-Based Topic Model Analysis
Yavuz, Mehmet; Balat, Şener; Kayalı, Bünyami; Gülen, Emirhan
Although research on distance education has increased substantially in recent years, comprehensive analyses capturing the overall thematic development of the field remain limited. Modeling studies are necessary to address this gap and to uncover long-term research trends. This study aimed to reveal the trends of research conducted in the field of distance education in the last two decades by using machine learning methodology and bibliometric analysis. The unique aspect of the study was its use of the Latent Dirichlet Allocation (LDA) algorithm, a machine learning method for analysing large-scale data. Within the scope of the study, 54,444 articles in the Web of Science database were analysed. Bibliometric analysis revealed that distance education had gained significant momentum, especially in the post-pandemic period, with a wide range of applications in different disciplines such as education, management, and health. As a result of LDA analysis, 19 thematic topics were identified. Among these, digitalization, artificial intelligence, Web-based learning, and social interaction and collaboration stood out. Time series analysis showed an increasing trend for topics such as artificial intelligence and emergency distance education over the years, with less interest shown in topics such as Web-based learning and program design. The study emphasized that distance education is in a technology-driven transformation process and pointed to areas that will offer new opportunities for researchers. Furthermore, recommendations for decision-makers, such as investments in digital infrastructure, integration of artificial intelligence-based systems, and the establishment of quality standards, also emerged.
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MakaleAçık Erişim2026Scopus SJRQ2/r/bayburt/11266
Experimental Electromagnetic Shielding Analysis of a Square-Resonator-Integrated Double-Concrete Structure Using Explainable Machine Learning
Cakir, Mehmet
Electromagnetic shielding has become a practical concern in buildings and structures exposed to persistent interference. This paper reports experimental measurements of the frequency-dependent shielding properties of a square-resonator-integrated double-concrete structure, using a free-space S-parameter setup built around WR229 waveguide adaptors and horn antennas. Three variables were tested: concrete thickness D, relative permittivity εr, and relative magnetic permeability μr. Both εr and μr were characterized experimentally from carbon-fibre- and copper-slag-modified concrete rather than taken from standard tables. The novelty of the study lies in combining experimentally characterized concrete electromagnetic properties, an embedded square-resonator geometry, and explainability-driven machine learning analysis within a single experimental framework for cement-based EMI shielding design. A total of 96 parameter combinations were evaluated using calibrated S11 and reference-corrected S21 responses across 3.3–4.9 GHz. Thickness and electromagnetic material properties interacted—neither governed shielding performance on its own. The strongest transmission attenuation occurred at D = 5, εr = 7, and μr = 1.2, where minimum S21 reached approximately −62.98 dB at 3.6392 GHz. S11 varied considerably less than S21 across the tested combinations, suggesting transmission suppression is the dominant mechanism rather than reflection enhancement. A machine learning analysis confirmed that nonlinear ensemble models outperformed the linear baseline and identified thickness as the most influential predictor of minimum S21.
İncele
MakaleAçık Erişim2026Scopus SJRQ1/r/bayburt/11215
Waveguide-Based Microwave S-Parameter Measurements for Machine Learning-Assisted Classification of Paprika Powder Adulterants
Alptekin Engin, Mustafa
Detecting food adulteration in powdered spices through visual inspection is difficult, and paprika powder is particularly susceptible to adulteration due to its fine particulate structure and intense color. This study proposes a waveguide-based microwave S-parameter measurement approach combined with machine learning for the classification of organic and mineral adulterants in paprika powder. The experimental dataset was created by intentionally adulterating a commercial paprika brand with five organic and five mineral materials in varying proportions. The S11 and S21 scattering parameters, measured in the 3.3-4.9 GHz frequency range, of these samples, comprising a total of 11 different classes including pure paprika, were used as input in the machine learning-based classification process. The proposed framework directly uses frequency and S11 and S21 responses to distinguish pure paprika from ten adulterant types without complex feature extraction. S-parameter measurements were performed iteratively for each condition using a vector network analyzer, and the average values were used in the analyses. The frequency-point-level performance of the model was examined with different training/test data splitting ratios during the classification process. Furthermore, 10-fold cross-validation was performed during the training phase, and the performance of different classifiers was evaluated comparatively using overall and balanced validation and test accuracies. Under random point-wise partitioning, Subspace KNN achieved the highest numerical performance, with a mean frequency-point-level test accuracy of 99.86±0.06% across 10 independent runs. The findings demonstrate the frequency-point-level separability of microwave S-parameter responses under the investigated experimental conditions.
İncele
MakaleAçık Erişim2026Scopus SJRQ2/r/bayburt/11148
The Effect of Data Types’ on the Performance of Machine Learning Algorithms for Cryptocurrency Prediction
Tanrikulu, Hulusi Mehmet; Pabuccu, Hakan
Forecasting cryptocurrencies as a financial issue is crucial as it provides investors with possible financial benefits. A slight improvement in forecasting performance can lead to increased profitability; Therefore, obtaining a realistic forecast is very important for investors. Bitcoin, frequently mentioned in recent due to its volatility and chaotic behavior, has become an investment tool, especially during and after the COVID-19 pandemic. In this study, selected ML techniques were investigated for predicting cryptocurrency movements by using technical indicator-based data sets and measuring the applicability of the techniques to cryptocurrencies that do not have sufficient historical data. In order to measure the effect of data size, Bitcoin’s last 1 year and 7 years of data were used. Following the related literature, Google trends and the number of tweets were used as input features, in addition to the most commonly used twelve technical indicators. Random Forest, K-Nearest Neighbors, Extreme Gradient Boosting (XGBoost-XGB), Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Networks (ANN), and Long-Short-Term Memory (LSTM) network were optimized for best results. Accuracy, F1, and area under the ROC curve values were used to compare the model performance. For continuous data, ANN and SVM performed the best with the highest accuracy and outperformed the other ML models for complete and reduced sets. LSTM reached the best accuracy for trend data, but SVM, NB, and XGB models showed similar performance. The research shows that some indicators significantly affect prediction performance, and the data discretization process also improved the model’s accuracy. While the number of samples affects the results of many ML models, correctly optimized and fine-tuned models may also give excellent results even with less data.
AtıfScopus1OpenAlex2
İncele
MakaleAçık Erişim2026Scopus SJRQ2/r/bayburt/10926
Enhancing salt stress tolerance in sugar beet via TiO2 nanoparticles: a machine learning approach
Engin, Mustafa Alptekin; Sefaoglu, Firat; Gul, Volkan; Aras, Selim; Kaynar, Dilara
In this study, the biostimulant potential of titanium dioxide (TiO2) nanoparticles was investigated to enhance sugar beet (Beta vulgaris L.) seed response to salt stress. Controlled applications were conducted at different NaCl (0–200 mM) and TiO2 (0–1,800 ppm) levels, and eight key morphological and physiological parameters were evaluated. In the first stage, the individual and interactive effects of the factors were examined using a two-way analysis of variance. Then, various machine learning-based regression models, primarily the Gradient Boosting algorithm, were used to numerically model plant responses. Based on the high-accuracy models, optimal application combinations were determined for each parameter and visualized using graphical surface analyses. Additionally, the most suitable overall TiO2 dosage for each salt level was calculated using a multi-criteria scoring system that assigned equal weight to all parameters. The results revealed that TiO2 exerts a regulatory and protective effect on plants against salt stress, with this effect varying with both dosage level and environmental stress severity. The study demonstrates that nanotechnological initiatives can be integrated into data-driven agricultural strategies to optimize stress management.
AtıfScopus1WoS1OpenAlex1
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MakaleAçık Erişim2025Scopus SJRQ2/r/bayburt/12660
Development of a Clinical Decision Support System Using Artificial Intelligence Methods for Liver Transplant Centers
Yağanoğlu, Mete; Öztürk, Gürkan; Bozkurt, Ferhat; Bilen, Zeynep; Yetiş Demir, Zühal; Kul, Sinan; Şimşek, Emrah; Kara, Salih; Eygu, Hakan; Altundaş, Necip
The objective of this study is to utilize artificial intelligence techniques for the diagnosis of complications and diseases that may arise after liver transplantation, as well as for the identification of patients in need of transplantation. To achieve this, an interface was developed to collect patient information from Atatürk University Research Hospital, specifically focusing on individuals who have undergone liver transplantation. The collected data were subsequently entered into a comprehensive database. Additionally, relevant patient information was obtained through the hospital’s information processing system, which was used to create a data pool. The classification of data was based on four dependent variables, namely, the presence or absence of death (“exitus”), recurrence location, tumor recurrence, and cause of death. Techniques such as Principal Component Analysis and Linear Discriminant Analysis (LDA) were employed to enhance the performance of the models. Among the various methods employed, the LDA method consistently yielded superior results in terms of accuracy during k-fold cross-validation. Following k-fold cross-validation, the model achieved the highest accuracy of 98% for the dependent variable “exitus”. For the dependent variable “recurrence location”, the highest accuracy obtained after k-fold cross-validation was 91%. Furthermore, the highest accuracy of 99% was achieved for both the dependent variables “tumor recurrence” and “cause of death” after k-fold cross-validation.
AtıfScopus2WoS2OpenAlex2
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MakaleAçık Erişim2024Scopus SJRQ3/r/bayburt/11643
Machine learning driven optimization and parameter selection of multi-surface HTS Maglev
Ozkat, Erkan Caner; Abdioglu, Murat; Ozturk, U. Kemal
This research aims to tackle the challenges posed by precise force measurement for high temperature superconducting (HTS) Maglev systems, including mechanical constraints, step motor limitations, and sensor resolutions. For this aim, six machine learning (ML) models namely Support Vector Machine (SVM), Gaussian Process Regression (GPR), Extreme Gradient Boosting (XGB), Long Short-Term Memory (LSTM), Extreme Machine Learning (EML), and Convolutional Neural Network (CNN) were developed to predict levitation force (Fz) and lateral force (Fx) based on process parameters including permanent magnet width (PMW), field cooling height (FCH), the movement in the z-axis (vertical distance), and the movement in the x-axis (lateral distance). Among six ML models, CNN emerged as the most accurate model, demonstrating smaller root mean square deviation (RMSD) without compromising correlation coefficients. Furthermore, an innovative process window approach was introduced to select process parameters that simultaneously meet the minimum value of Fz and maximum value of Fx, named β1 and β2, set at 90 N and 0 N, respectively. Within this window, PMW of 30 mm and z values less than 10 mm were found to be consistent for all FCH and x values. The novelty of this study is to formulate the optimisation problem in HTS Maglev using the developed ML model by addressing two specific objectives one of which focuses on maximizing Fz while ensuring Fx remains within a defined tolerance (β3), representing the minimum allowable ratio of the levitation force to the total force, and the second problem aims to maximize Fz while obtaining zero Fx. The optimum PMW, FCH, x, and z values were obtained at 30 mm, 30 mm, 4 mm and 5 mm, corresponding to Fz and Fx values of 224.2 N and -53.8 N for option 1. As for option 2, the process parameters were obtained as 28.6 mm, 25.9 mm, 0 mm, and 5 mm, corresponding to Fz and Fx values of 194.2 N and 0 N. It was obtained both experimentally and by the optimization that Fz reaches close its maximum as the Fx gains attractive character. Hence, it is expected that the outcomes of this study will significantly benefit the design of HTS Maglev systems and find valuable applications across various transportation engineering projects.
AtıfScopus21WoS18
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MakaleAçık Erişim2024Scopus SJRQ1/r/bayburt/11491
Feature selection with annealing for forecasting financial time series
Pabuccu, Hakan; Barbu, Adrian
Stock market and cryptocurrency forecasting is very important to investors as they aspire to achieve even the slightest improvement to their buy-or-hold strategies so that they may increase profitability. However, obtaining accurate and reliable predictions is challenging, noting that accuracy does not equate to reliability, especially when financial time-series forecasting is applied owing to its complex and chaotic tendencies. To mitigate this complexity, this study provides a comprehensive method for forecasting financial time series based on tactical input–output feature mapping techniques using machine learning (ML) models. During the prediction process, selecting the relevant indicators is vital to obtaining the desired results. In the financial field, limited attention has been paid to this problem with ML solutions. We investigate the use of feature selection with annealing (FSA) for the first time in this field, and we apply the least absolute shrinkage and selection operator (Lasso) method to select the features from more than 1000 candidates obtained from 26 technical classifiers with different periods and lags. Boruta (BOR) feature selection, a wrapper method, is used as a baseline for comparison. Logistic regression (LR), extreme gradient boosting (XGBoost), and long short-term memory are then applied to the selected features for forecasting purposes using 10 different financial datasets containing cryptocurrencies and stocks. The dependent variables consisted of daily logarithmic returns and trends. The mean-squared error for regression, area under the receiver operating characteristic curve, and classification accuracy were used to evaluate model performance, and the statistical significance of the forecasting results was tested using paired t-tests. Experiments indicate that the FSA algorithm increased the performance of ML models, regardless of problem type. The FSA hybrid models showed better performance and outperformed the other BOR models on seven of the 10 datasets for regression and classification. FSA-based models also outperformed Lasso-based models on six of the 10 datasets for regression and four of the 10 datasets for classification. None of the hybrid BOR models outperformed the hybrid FSA models. Lasso-based models, excluding the LR type, were comparable to the best models for six of the 10 datasets for classification. Detailed experimental analysis indicates that the proposed methodology can forecast returns and their movements efficiently and accurately, providing the field with a useful tool for investors.
AtıfScopus28WoS18OpenAlex32
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Yüksek Lisans TeziAçık Erişim2021/r/bayburt/17167
Bitcoin fiyat hareketlerinin makine öğrenme algoritmaları ile tahmini
Hulusi Mehmet Tanrıkulu
Kripto para terimi her ne kadar finans dünyasına geçtiğimiz on yıl içerisinde girmiş olsa da başlangıç döneminde var olan güven kaygıları yenmiş ve bir ticaret ve yatırım aracı olarak piyasalarda yerini almıştır. Kripto para birimlerinin internet kaynaklı bir değer olması, arzının sınırlı olması, bir merkeze bağlı olmaması ve 7/24 anında finansal işlemlerin yapılabilmesi kripto paraları cazip bir yatırım aracı haline getirmektedir. Günümüzde bilinen binin üzerinde kripto para birimi vardır. Bu kripto para birimlerinin ilki ve en yaygın kullanılanı olan Bitcoin'dir. Son günlerde fiyatlarındaki hareketlilikten dolayı medyada kendinden sıkça söz ettiren Bitcoin, sadece yatırım olmaktan çıkmış günlük ihtiyaçlarında karşılanabildiği genel bir değer haline gelmiştir. Bu çalışmada Blok Zincir, Kripto Para kavramlarına değinilerek, şimdiye kadar Bitcoin özelinde fiyat hareketlerinin tahminlerinde kullanılan makine öğrenme algoritmaları, tahmin için kullanılan analiz göstergeleri(indikatörler) ve geçmiş akademik çalışmalarda izlenen yöntemler ve verimlilik bulguları üzerine incelemeler yapılmıştır. Yapılan incelemeler sonunda; makine öğrenme yöntemlerinin çok veri ve az veri ile performansların kıyaslanabilmesi adına geçtiğimiz 7 yıllık ve 1 yıllık Bitcoin hareketleri üzerine analizler yapılmıştır. Teknik indikatörlerle beraber günlük Tweet sayıları ve Google Arama Trendleri verisinin dahil edildiği veri setleri 7 farklı makine öğrenme yöntemi ile fiyat yönünün tespiti üzerine performans analizi yapılmıştır. Son olarak eğitilen makine öğrenme yöntemlerinin tahmin performansları yeni bir veri seti üzerinde ölçülmüştür. Bu performans analizleri ile, henüz uzun vadeli verisi oluşmamış yeni kripto paraların fiyat yönü tahmininde kullanılabilecek makine öğrenme yöntemlerinin belirlenmesinde yol gösterici olması hedeflenmiştir.
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MakaleAçık Erişim2020/r/bayburt/13203
MACHINE LEARNING APPLICATION FOR OPTIMIZING ASYMMETRICAL REDUCTION OF ACETOPHENONE EMPLOYING COMPLETE CELL OF LACTOBACILLUS SENMAIZUKE AS AN ENVIRONMENTALLY FRIENDLY APPROACH
Taylan, Osman; Yilmaz, Mustafa Tahsin; Balubaid, Mohammed; Alamoudi, Rami; El-Obeid, Tahra; Dertli, Enes; Şahin, Engin; Bakhsh, Ahmed; Herrera-Viedma, Enrique
Recently, optimization of the bioreduction reactions by optimization methodologies has gained special interest as these reactions are affected by several extrinsic factors that should be optimized for higher yields. An important example for these kinds of reactions is the complete cell implications for the bioreduction of prochiral ketones in which the culture parameters play crucial roles. Such biocatalysts provide environmentally friendly and clean methodology to perform reactions under mild conditions with high conversion rates. In the present work, at the first step the Lactobacillus senmaizuke was isolated from sourdough and the complete cell application of Lactobacillus senmaizuke for the bioreduction of acetophenone was optimized by an Artificial Neural networks (ANNs) to achieve the highest enantiomeric excess (EE, %). The culture parameters, pH, temperature, incubation period and agitation speed were the experimental factors that were optimized to maximize EE (%) by machine learning algorithm of Artificial Intelligence modeling and the best conditions to maximize EE (95.5 %) were calculated to be pH of 5.7, temperature of 35 ºC, incubation period of 76 h and agitation speed of 240 rpm with very low sum of squared error value (0.611236 %) to bioreduce acetophenone using complete cell of Lactobacillus senmaizuke as a sourdough isolate GRAS microbial species. Accordingly, The ANN was employed to correctly establish the enantiomeric excess values of the specimen with an average absolute error 0.080739 %.
AtıfScopus3WoS2OpenAlex2
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