Ipek Atik

The relationship between systemic inflammation and thyroid nodule size has received less attention than its role in malignancy. This retrospective study evaluated the associations of the neutrophil-to-lymphocyte (NLR), platelet-to-lymphocyte (PLR), lymphocyte-to-monocyte (LMR), monocyte-to-high-density lipoprotein cholesterol (MHR), and neutrophil-to-high-density lipoprotein cholesterol (NHR) ratios with maximum ultrasonographic thyroid nodule diameter. Of 279 screened records, 161 patients aged 18–70 years were analyzed. Spearman correlations with Holm correction constituted the primary analysis; multivariable linear regression and five machine-learning regression algorithms evaluated with repeated five-fold cross-validation (n = 155) were exploratory. In unadjusted analysis, LMR was the only index significantly correlated with nodule diameter (ρ = 0.230, 95% bootstrap CI: 0.069–0.384; Holm-adjusted p = 0.020). After adjustment for age, sex, body mass index, thyroid functional status, and HDL cholesterol, LMR remained associated with nodule diameter (β = 1.824 mm per unit; 95% CI: 0.171–3.478; n = 144), although the model explained little variance (R² = 0.106). Random Forest achieved the lowest cross-validated root mean squared error (10.94 ± 1.55 mm; R² = 0.116 ± 0.305), and LMR showed the highest permutation importance. These indices alone were insufficient for clinically reliable prediction of nodule diameter, and the weak LMR association should be regarded as hypothesis-generating.

Endüstriyel görsel muayene sistemleri; yüzey kusurları, eksik veya hatalı montaj, geometrik bozukluklar, baskı ve etiket hataları, renk farklılıkları ve yabancı nesneler gibi üretim hatalarının otomatik olarak belirlenmesinde kullanılmaktadır. Klasik makine görmesi uygulamalarında eşikleme, kenar çıkarımı, morfolojik işlemler ve elle tasarlanmış özniteliklere dayalı yöntemler kullanılabilirken, ürün görünümündeki değişkenlik, karmaşık yüzey dokuları ve değişen üretim koşulları sabit kurallara dayalı yöntemlerin uygulanmasını zorlaştırmaktadır. Derin öğrenme tabanlı yöntemler ise görüntü özelliklerinin doğrudan veriden öğrenilmesine olanak sağlayarak sınıflandırma, nesne tespiti, segmentasyon ve anomali tespiti gibi farklı görsel muayene problemlerinde kullanılabilmektedir. Bu bölümde derin öğrenme tabanlı görsel muayene yaklaşımları algoritmik performansın yanı sıra üretim hattında uygulanabilirlik açısından incelenmiştir. Etiketli kusurlu örneklerin bulunduğu uygulamalar ile kusurlu örneklerin sınırlı olduğu anomali tespiti problemleri değerlendirilmiş; zero-shot ve few-shot yaklaşımlar, sentetik anomali üretimi ve veri dengesizliği ele alınmıştır. Kamera çözünürlüğü, optik sistem, aydınlatma, hareket bulanıklığı, gerçek zamanlı çıkarım, edge deployment ve PLC tabanlı üretim hattı entegrasyonu tartışılmıştır. Bu nedenle sistem başarısı yalnızca model doğruluğuyla değil; minimum tespit edilebilir kusur boyutu, yanlış kabul ve yanlış ret oranları, toplam karar gecikmesi, proses senkronizasyonu ve gerçek üretim koşullarındaki doğrulama sonuçlarıyla birlikte değerlendirilmelidir.

Background/Objectives: Anthropometric machine-learning models may approximate body-composition classifications, but performance can be inflated by inconsistent preprocessing, non-independent validation records, and incomplete calibration reporting. This study evaluated a sex-specific bioelectrical impedance analysis (BIA)-defined high-adiposity phenotype using simple anthropometric variables in adults with and without hypertension. Methods: Of 583 prespecified records, 11 with invalid placeholder-coded values in required anthropometric or body-composition fields were excluded, leaving 572 participants (385 normotensive and 187 hypertensive). A high-adiposity phenotype was defined as BIA-derived body fat ≥ 25% in males or ≥35% in females. Predictors were sex, height, body weight, waist circumference, and hypertension status; body mass index and body-fat percentage were excluded. Eight algorithms were assessed using 10 repetitions of stratified five-fold group cross-validation, with identical predictor profiles kept within the same fold. Continuous predictors were standardized within training folds. Performance was estimated from averaged out-of-fold probabilities with 2000 stratified bootstrap confidence intervals, calibration measures, and SHAP analysis. Results: A high-adiposity phenotype was present in 441 participants (77.1%). Random forest achieved the highest discrimination (ROC AUC = 0.959, PR AUC = 0.980). Gradient boosting provided the strongest threshold-dependent performance (accuracy = 0.937, balanced accuracy = 0.895, sensitivity = 0.973, specificity = 0.817, precision = 0.947, F1 score = 0.960, MCC = 0.817) and the lowest Brier score (0.057), but its calibration slope was 0.462, indicating overconfident probabilities. SHAP analysis identified waist circumference as the largest attribution within the fitted gradient-boosting model; this result must be interpreted jointly with the ablation analysis because sex, height, and body weight are inputs to the proprietary Tanita equation. Conclusions: Simple anthropometric variables classified the prespecified Tanita BIA-derived high-adiposity threshold with strong internal performance after duplicate-aware validation. Sex, height, and body weight alone achieved a ROC AUC of 0.920; adding waist circumference produced a small and uncertain increase in discrimination (ΔROC AUC = 0.0054, 95% CI −0.0060 to 0.0153), whereas hypertension status added a negligible value. The findings represent internal validation of a device-defined outcome, not prediction of an independent biological reference, and require external validation against criterion body-composition methods before clinical application.

Background/Objectives: Type 2 diabetes mellitus (T2DM) is a prevalent metabolic disorder associated with substantial long-term morbidity and mortality. Routinely collected anthropometric, biochemical, and hematological variables may contain useful discriminatory information for data-driven classification. This study aimed to compare the apparent classification performance of multiple machine learning algorithms for distinguishing individuals with and without T2DM using routinely obtained clinical parameters in a single-center dataset. Methods: This single-center observational study included 160 adults (95 females, 65 males) evaluated at the Endocrinology Outpatient Clinic of Gaziantep Islam Science and Technology University, Faculty of Medicine, Ersin Arslan Training and Research Hospital. The dataset comprised anthropometric measurements, biochemical markers, and complete blood count parameters. SMOTE was applied only within the training folds to address class imbalance and to avoid information leakage. Following fold-internal data preprocessing, which included imputing missing values and feature standardization where appropriate, the dataset was evaluated using stratified 5-fold cross-validation. SHAP analysis was performed to interpret the model predictions. A calibration curve was used to assess the model’s reliability. Eight supervised machine learning models were evaluated with and without HbA1c: Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Decision Tree, Random Forest, Extra Trees, Gaussian Naive Bayes, and k-Nearest Neighbors. Model performance was evaluated using accuracy, sensitivity, specificity, and F1 score, and ROC curves were used as a diagnostic tool. Results: The models were evaluated in two different ways: with and without HbA1c. Random Forest demonstrated the best classification performance in the cross-validated evaluation; without HbA1c, it achieved 92.2% accuracy, 93.9% sensitivity, 97.9% specificity, and a 95.9% F1 score. When HbA1c was included, it achieved 98.0% accuracy, 97.9% sensitivity, 98.8% specificity, and a 99.0% F1 score. Decision Tree and Extra Trees demonstrated strong performance with accuracy rates of 87.6% and 92.8%, respectively, without HbA1c, and 90% and 93.5% when HbA1c was included; in contrast, KNN yielded the lowest accuracy rate (70.6%). Overall, tree-based models performed better than linear classifiers on this dataset. Conclusions: Machine learning models based on routine clinical and anthropometric variables demonstrated promising performance for T2DM classification in this single-center dataset; tree-based approaches yielded the most promising results. Including HbA1c improved the models’ ability to classify individuals with and without T2DM. However, since HbA1c was included both as a predictor and as part of the operational definition of the diabetes group, the findings should be interpreted with caution due to the risk of target leakage. Therefore, these results should be considered exploratory rather than evidence of clinically applicable predictive performance, and an independent external validation study should be conducted prior to clinical application.

Background: Accurate sex estimation is crucial in forensic identification when DNA analysis is impractical or remains are fragmented. Traditional anthropometric approaches often rely on single bone measurements and yield moderate levels of accuracy. Objective: This study aimed to evaluate deep convolutional neural networks (CNNs) for automated sex estimation using entire foot radiographs, an approach rarely explored. Methods: Digital foot radiographs from 471 adults (238 men, 233 women, aged 18–65 years) without deformities or prior surgery were retrospectively collected at a single tertiary center. Six CNN architectures (AlexNet, ResNet-18, ResNet-50, ShuffleNet, GoogleNet, and InceptionV3) were trained using transfer learning (70/15/15 train–validation–test split, data augmentation). The model performance was assessed using accuracy, sensitivity, specificity, precision, and F1-score. Results: InceptionV3 achieved the highest accuracy (97.1%), surpassing previously reported methods (typically 72–89%), with balanced sensitivity (97.5%) and specificity (96.8%). ResNet-50 followed closely (95.7%), whereas simpler networks, such as AlexNet, underperformed (90%). Conclusions: Deep learning applied to whole-foot radiographs delivers state-of-the-art accuracy for sex estimation, enabling rapid, reproducible, and cost-effective forensic identification when DNA analysis is delayed or unavailable, such as in mass disasters or clinical emergency settings.

This study examines the impacts of artificial intelligence (AI) technologies in the healthcare sector, particularly in the areas of medical imaging, diagnosis, treatment planning, and patient monitoring. AI's ability to analyze medical images more accurately and quickly, diagnose diseases at an early stage, create personalized treatment plans, and continuously monitor patient conditions has significantly enhanced the quality and accessibility of healthcare services. However, the ethical, privacy, and security issues associated with AI underscore the need for these technologies to be responsibly integrated into the healthcare sector. Moving forward, the role of AI in healthcare services is expected to expand, and realizing this potential in an ethical and secure manner will require ongoing effort.

The use of remote sensing has great potential for detecting many natural differences, such as disasters, climate changes, and urban changes. Due to technological advances in imaging, remote sensing has become an increasingly popular topic. One of the significant benefits of technological advancement has been the ease with which remote sensing data is now accessible. Physical and spatial information is detected by remote sensing, which can be described as the process of identifying distinctive characteristics of an environment. Resolution is one of the most important factors influencing the success of the detection processes. As a result of the resolution being below the necessary level, features of the objects to be differentiated become incomprehensible and therefore constitute a significant barrier to differentiation. The use of deep learning methods for classifying remote sensing data has become prevalent and successful in recent years. This study classified Satellite images using deep learning and machine learning methods. Based on the transfer learning strategy, a parallel convolutional neural network (CNN) was designed in the study. To improve the feature mapping of an image, convolutional branches use pre-trained knowledge of the transmitted network. Using the offline augmentation method, the raw data set was balanced to overcome its unbalanced class distribution and increased network performance. A total of 35 classes of landforms have been studied in the experiments. The accuracy value of the developed model in the classification study of landforms was 97.84%. According to experimental results, the proposed method provides high classification accuracy in detecting landforms and outperforms existing studies.

Electronic components are basic elements that are widely used in many industrial and technological fields. With the development of technology, their dimensions are being produced in smaller and smaller sizes. As a result, making fast distinctions becomes difficult. Being able to classify electronic components quickly and accurately will save labor and time in all areas where these elements are used. Recently, deep learning algorithms have become preferential in product classification studies due to their high accuracy and speed. In this paper, a classification study of electronic components was carried out with the deep learning method. A new convolutional neural network (CNN) model is proposed in the study. The model has six convolution layers, four pooling layers, two fully connected layers, softmax, and a classification layer. The training parameters of the network were determined as an ensemble size of 16, maximum period of 100, initial learning rate of 1 × 10⁻³, and the optimizing method sgdm. While determining the CNN model layers and training parameters, the values with the highest predictive values were selected as a result of the trials. Classification research was conducted using the pre-trained networks AlexNet, ShuffleNet, SqueezeNet, and GoogleNet for the same data, and their performance success parameters were compared to those of the proposed CNN model. The proposed CNN model showed higher performance than the other methods, and an accuracy value of 98.99% was obtained.

This study proposes a new convolutional neural network (CNN) method with an input-signal decomposition algorithm. With the proposed CNN architecture, hourly electricity consumption data for the Covid-19 period in Turkey were used as input data, and the short-term electricity consumption was forecasted. The input data were decomposed into its subcomponents using a signal decomposition process called Empirical Mode Decomposition (EMD). To extract the deep features, all input data were transformed into 2D feature maps and fed into the CNN. The obtained results were compared with the pre-trained models GoogleNet, AlexNet, SqueezeNet, and ResNet18. Model-wise comparisons showed that the proposed method had the highest correlation coefficient (R) and lowest root mean square error (RMSE) and mean absolute error (MAE) values for 1-h, 2-h, and 3-h. The mean R-values of the proposed method were 95.6%, 95.2%, and 94.0% for 1h, 2h and 3h ahead, respectively. The mean RMSE values were 8.2%, 8.7%, and 10.2% for 1h, 2h and 3h ahead, respectively. The experimental results confirm that the proposed method outperforms other pretrained methods despite its simpler structure.