Sürdürülebilir Kalkınma Amaçları

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Alt birimler

• Devreler ve Sistemler Anabilim Dalı
• Elektro Manyetik Alanlar ve Mikrodalga Tekniği Anabilim Dalı

Araştırmacılar(8)

• Akçay, Latif
• Coşkun, Aykut
• Çakır, Mehmet
• Engin, M. Alptekin
• Engin, Mustafa Alptekin
• Kaleli, Aliriza
• Köksal, Hüseyin
• Tabakcioglu, M.B.

• 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.

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  İncele
• MakaleAçık Erişim2026Scopus SJRQ1/r/bayburt/12675
  Enhanced Absorption-Dominant EMI Shielding Performance of Pyramidal Cementitious Composites Incorporating Recycled Plastics and Magnetite Minerals for 5G Applications

  Cakir, Mehmet; Engin, Mustafa Alptekin; Camuzcuoglu, Murat

  In this study, waste polypropylene (PP) and magnetite (Fe3O4) mineral-reinforced cement-based pyramidal composite structures were designed, manufactured, and experimentally characterized to reduce electromagnetic interference (EMI) problems in the 3.3–4.9 GHz frequency band for 5G communication systems. Unlike traditional planar concrete surfaces, the aim was to minimize surface reflections and obtain an absorption-dominant shielding mechanism by providing gradient impedance matching through the pyramidal geometry. Although the use of carbon-based nanomaterials is common in the current literature, their high cost and corrosion risks limit their large-scale applications. This study involves the evaluation of waste polypropylene disposal and self-enriching magnetite mineral together. Theoretical analyses were supported by the Lichtenecker Logarithmic Mixing Rule and the Maxwell–Garnett model, and seven different mixing scenarios (S1–S7) were measured using the free-space method with a Libre vector network analyzer. Experimental results showed that the pure concrete sample exhibited predominantly reflective behaviour, with shielding performance improving significantly as the filler ratio increased. The S4 sample, containing 15% PP and 10% magnetite, offered broadband and balanced absorption performance, while the S7 sample, containing 25% PP and 25% magnetite, provided the highest shielding effectiveness with reflection below −10 dB across the entire band and transmission loss reaching −65 dB.

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  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.

  AtıfScopus1·WoS1·OpenAlex1

  İncele
• MakaleAçık Erişim2026Scopus SJRQ1/r/bayburt/11521
  Non-Destructive Intelligent Microwave Sensing of Water Content in Sand–Water Mixtures Using S-Parameter Features and PLS Regression

  Çakır, Mehmet

  The accurate and rapid estimation of water content in granular materials is important for geotechnical engineering, construction-material evaluation, agricultural monitoring, and non-destructive material assessment. This study presents a controlled laboratory feasibility framework for estimating water content in sand–water mixtures using microwave S-parameter measurements and physically interpretable features. Sixty measurements were acquired from six nominal mixture levels containing 0%, 5%, 10%, 15%, 20%, and 25% water by mass using a WR-229 waveguide-based fixture over 3.30–4.90 GHz. Descriptors were extracted from the raw and empty-reference-normalized S11 and S21 responses, including extrema, slopes, area-based indicators, band-averaged values, selected-frequency responses, and phase statistics. Ridge regression, partial least squares regression, support vector regression, Gaussian process regression, random forest, and gradient boosting regression were evaluated. The selected PLS model achieved RMSE = 3.56%, MAE = 2.92%, and R2 = 0.826 under leave-one-mixture-level-out group-wise cross-validation, which was used as the primary indicator of generalization to an unseen mixture level. The substantially lower error obtained under repeated-measurement leave-one-out cross-validation primarily reflects within-level repeatability under controlled conditions and should not be interpreted as evidence of universal calibration performance. The results demonstrate that magnitude- and phase-derived microwave descriptors, particularly transmission-based features, provide an interpretable and repeatable framework for water-content estimation under the specific sand type, sample geometry, water source, and laboratory conditions investigated. Further validation using independent preparation batches, different granular materials, measured water conductivity, temperature variation, and field-like conditions is required before practical deployment.

  İncele
• 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.

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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.

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  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
• MakaleYalnız künye2026Scopus SJRQ1/r/bayburt/11152
  Experimental investigation of magnetic forces in PMA and HTS configurations using a high-speed electrodynamic levitation test system

  Mollahasanoglu, Hakkı; Abdioglu, Murat; Ozturk, Ufuk Kemal; Okumus, Halil Ibrahim

  This study presents a comparative experimental investigation of magnetic force parameters in high-speed electrodynamic levitation systems using permanent magnet arrays and high-temperature superconductors. A novel modular test platform with a rotating aluminium rail, cryostat, and integrated three-axis force measurement unit was employed to evaluate levitation and drag forces under controlled conditions at linear velocities up to 280 km/h. Three different permanent magnet arrays configurations were designed and tested at working gaps of 10, 12, and 15 mm, while high-temperature superconductors bulks fabricated from YBCO by the top-seed-melt-growth method were tested at 10 and 12 mm gaps. The experimental results demonstrated that permanent magnet arrays configurations produced higher levitation forces, with a maximum of 100 N at working gap of 10 mm. High-temperature superconductors bulks, in contrast, generated lower levitation forces (maximum 17 N at 10 mm) due to limited trapped flux (∼0.25 T) but showed the advantage of substantially reduced drag forces (3.5 N at 10 mm). A lift-to-drag ratio analysis confirmed the trade-off between force generation and energy losses in permanent magnet arrays systems, while highlighting the efficiency potential of high-temperature superconductors-based levitation. The findings of this study provide valuable insights for the engineering development of high-temperature superconductors-based Maglev technologies, emphasizing their potential importance in future transportation systems.

  AtıfScopus5·WoS4·OpenAlex2

  İncele
• MakaleAçık Erişim2026Scopus SJRQ2/r/bayburt/11111
  Dual-Band Electromagnetic Shielding of Concrete Block Walls Using Concentric Double Square-Ring Frequency-Selective Surfaces near the n78 and n79 5G Frequency Regions

  Cakir, Mehmet

  Featured Application: This study demonstrates a concrete-integrated dual-band frequency-selective shielding approach for selectively attenuating the main sub-6 GHz 5G n78 and n79 frequency regions in building walls without increasing wall thickness or altering conventional concrete block geometry. This paper presents a dual-band shielding approach in which two concentric square rings, fabricated on a single 0.2 mm copper sheet, are embedded in the mortar joint of prefabricated concrete blocks. The outer ring targets n78 (design center 3.5 GHz) and the inner ring targets n79 (design center 4.7 GHz). Both rings are dimensioned from the effective wavelength inside the concrete medium (εr = 5.0): the outer ring has an outer side of 9.93 mm and the inner ring an outer side of 7.43 mm, with a 180° gap offset between the two elements to suppress inter-ring coupling. CST Microwave Studio simulations predicted two shielding peaks of 37.2 dB at 3.48 GHz and 35.8 dB at 4.72 GHz, with −10 dB bandwidths of 580 MHz and 490 MHz, respectively. Computer numerical control (CNC)-milled copper panels cast into ordinary Portland cement (OPC) concrete blocks achieved measured peak SE of 34.9 dB (n78) and 33.4 dB (n79), with bandwidths of 510 MHz and 420 MHz. The 2.3–2.4 dB shortfall relative to simulation is attributed to coupling-gap drift during milling and spacer-induced surface voids. A permittivity sweep from εr = 4.5 to 6.0 showed that peak SE remains above 30 dB throughout.

  İncele
• MakaleAçık Erişim2026Scopus SJRQ2/r/bayburt/11088
  Deep Learning-Based Classification of Common Lung Sounds via Auto-Detected Respiratory Cycles

  Engin, Mustafa Alptekin; Uzun Arslan, Rukiye; Senyer Yapici, İrem; Aras, Selim; Gangal, Ali

  Chronic respiratory diseases, the third leading cause of mortality on a global scale, can be diagnosed at an early stage through non-invasive auscultation. However, effective manual differentiation of lung sounds (LSs) requires not only sharp auditory skills but also significant clinical experience. With technological advancements, artificial intelligence (AI) has demonstrated the capability to distinguish LSs with accuracy comparable to or surpassing that of human experts. This study broadly compares the methods used in AI-based LSs classification. Firstly, respiratory cycles—consisting of inhalation and exhalation parts in LSs of different lengths depending on individual variability, obtained and labelled under expert guidance—were automatically detected using a series of signal processing procedures and a database was obtained in this way. This database of common LSs was then classified using various time-frequency representations such as spectrograms, scalograms, Mel-spectrograms and gammatonegrams for comparison. The utilisation of proven, convolutional neural network (CNN)-based pre-trained models through the application of transfer learning facilitated the comparison, thereby enabling the acquisition of the features to be employed in the classification process. The performances of CNN, CNN and Long Short-Term Memory (LSTM) hybrid architecture and support vector machine methods were compared in the classification process. When the spectral structure of gammatonegrams, which capture the spectral structure of signals in the low-frequency range with high fidelity and their noise-resistant structures, is combined with a CNN architecture, the best classification accuracy of 97.3% ± 1.9 is obtained.

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  AtıfScopus2·WoS2·OpenAlex2

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