Yazar "Engin, Mustafa Alptekin" seçeneğine göre listele

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    Deep Learning-Based Classification of Common Lung Sounds via Auto-Detected Respiratory Cycles
    (MDPI, 2026) Engin, Mustafa Alptekin; Uzun Arslan, Rukiye; Senyer Yapici, Irem; 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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    Enhanced Absorption-Dominant EMI Shielding Performance of Pyramidal Cementitious Composites Incorporating Recycled Plastics and Magnetite Minerals for 5G Applications
    (MDPI, 2026) 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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    Enhancing salt stress tolerance in sugar beet via TiO2 nanoparticles: a machine learning approach
    (Taylor & Francis Inc, 2026) 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.
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    Gömülü ve Sarıcı Öznitelik Seçim Yöntemleri Kullanılarak Akciğer Rahatsızlıklarının Tespiti
    (2022) Engin, Mustafa Alptekin; Aras, Selim
    Son yıllarda biyomedikal sinyal işleme alanındaki gelişmelere rağmen, akciğer rahatsızlıklarının tespiti üzerine hızlı ve yüksek doğrulukta çalışan teşhis sistemlerine duyulan ihtiyaç artmaktadır. Yapılan çalışmada fiziki muayene ile 94 farklı kişiden, solunum döngülerinin otomatik olarak tespit edilmesiyle elde edilen 150 adet normal ve 444 adet normal olmayan akciğer sesleri veri tabanı olarak kullanılmıştır. Sınıflandırma işleminde öznitelik olarak frekans ve zaman bölgesinde 12 farklı yöntem uygulanmıştır. Tüm veriler %80 eğitim %20 test aşamasında kullanılacak şekilde ikiye bölünmüştür. Elde edilen öznitelikler gömülü ve sarıcı öznitelik seçim yöntemleri kullanılarak değerlendirilmiştir. Bu yöntemler; özyinelemeli öznitelik eliminasyonu, uyarlanabilir yapı öğrenimi ile öznitelik seçimi, bağımlılık kılavuzlu denetimsiz öznitelik seçimi, sıralı yerellik ile denetimsiz öznitelik seçimi, içbükey küçültme yoluyla öznitelik seçimi, en küçük mutlak büzülme ve seçim operatörü öznitelik seçim yöntemleri olarak isimlendirilmektedir. İncelenen bu öznitelikler doğrusal destek vektör makineleri, k en yakın komşuluk, karar ağaçları ve naive bayes yöntemleri ile sınıflandırılmıştır. Sonuç olarak öznitelik sayısının sınırlandırılmadığı durum için, özyinelemeli öznitelik eliminasyonu yönteminin k en yakın komşuluk sınıflandırma ile beraber kullanıldığı durum için %97,3 doğruluk değerindeki başarıma ulaşılmaktadır. Öznitelik sayısının üç ile sınırlandırıldığı durumda ise uyarlanabilir yapı öğrenimi ile öznitelik seçimi yönteminin karar ağaçları yöntemi ile beraber kullanılması ile %91,4 değerinde başarıma ulaşılmıştır.
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    Improvement of machine learning-based diabetes diagnosis via resampling techniques
    (Pamukkale Univ, 2026) 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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    Machine Learning-Based Material Thickness and Type Classification via Scattering Parameters
    (Mdpi, 2025) 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.
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    Tek Kanallı Akciğer Seslerinde Süzgeç Tipi Özellik Seçim Yöntemlerini Kullanarak Solunum Patolojisinin Teşhisi
    (Osman SAĞDIÇ, 2022) Engin, Mustafa Alptekin; Akçay, Latif; Aras, Selim
    Yapılan çalışmada, tek kanallı yaygın akciğer sesleri kullanılarak patolojik ve sağlıklı denekler üzerinde detaylı bir öznitelik analizi gerçekleştirilmiştir. 94 kişiden elde edilen normal, ronküs, ince ral ve kaba ral seslerine ait 594 adet solunum döngüsünün otomatik tespiti ile elde edilen veri tabanı kullanılmıştır. Daha sonra, sıfır geçiş oranı, enerji, enerjinin entropisi, spektral merkezilik, bir çerçevenin yayılımı, spektral entropi, spektral akı, spektral devrilme, Mel-frekans cepstral katsayıları, harmonik oran, pencerenin temel frekansı ve renk vektörü öznitelik çıkarma yöntemleri veri tabanına uygulanmıştır. Sonsuz gizli öznitelik seçimi, sonsuz öznitelik seçimi, özvektör merkeziliği, minimum artıklık, maksimum ilgililik, relief, karşılıklı bilgi, laplace skoru, çoklu küme, fisher, denetimsiz ayrımcı, yerel öğrenmeye dayalı kümeleme, korelasyona dayalı öznitelik seçim yöntemleri eğitim aşamasında kullanılmıştır. Sınıflandırma için destek vektör makinesi, k en yakın komşu, naive bayes ve karar ağaçları algoritmaları kullanılmıştır. Sonuç olarak, öznitelik sayısı sınırlı olmadığı durumda, k en yakın komşuluk sınıflandırıcısı ve çoklu küme öznitelik seçim yöntemi kullanılarak %97,5 sınıflandırma doğruluğu elde edilmiştir. Öznitelik sayısı 3 ile sınırlandırıldığında ise k en yakın komşu sınıflandırıcısı ve özvektör merkeziliği veya sonsuz öznitelik seçimi yöntemleri kullanılarak %91,6 sınıflandırma doğruluğu elde edilmektedir.