The liver, a life-sustaining organ, plays a substantial role in many body functions. Liver diseases have become an important world health problem in terms of prevalence, incidences, and mortalities. Liver fibrosis/cirrhosis is great of importance, because if not treated in time liver cancer could be occurred and spread to other parts of the body. For this reason, early diagnosis of liver fibrosis/cirrhosis gives significance. Accordingly, this study investigated the performances of different machine learning algorithms for prediction of liver fibrosis/cirrhosis based on demographic and blood values. In this context, random forest, k nearest neighbour, C4.5 decision tree, K-star, random tree and reduced error pruning tree algorithms were used. Two distinct approaches were employed to evaluate the performances of machine learning algorithms. In the first approach, the entire features of dataset were utilized, while in the second approach, only the features selected through principal component analysis were used. Each approach was rigorously assessed using both 10-fold cross-validation and data splitting (70% train and 30% test) techniques. By conducting separate evaluations for each approach, a comprehensive understanding of the effectiveness of utilizing all features versus extracted features based principal component analysis was attained, providing valuable insights into the impact of feature dimensionality reduction on model performance. In this study, all analyses were implemented on WEKA data mining tool. In the first approach, the classification accuracies of random forest algorithm were 89.72% and 90.75% with the application of data splitting (70%-30%) and cross-validation techniques, respectively. In the second approach, where feature reduction is performed using principal component analysis technique, the accuracy values obtained from data splitting and cross-validation techniques of random forest algorithm were 88.61% and 88.83%, respectively. The obtained results revealed out that random forest algorithm outperformed for both approaches. Besides, the application of principal component analysis technique negatively affected the classification performance of used machine learning algorithms. It is thought that the proposed model will guide specialist physicians in making appropriate treatment decisions for patients with liver fibrosis/cirrhosis, potentially leading to death in its advanced stages.

The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries, and is approaching approximately 34,986,502 cases worldwide according to the statistics of European Centre for Disease Prevention and Control. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using 5-fold cross validation. Considering the performance results obtained, it has seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.

Photoplethysmography (PPG) signals are signals obtained as a result of optically measuring volumetric changes in capillaries. Volumetric changes in capillaries also depend on the work of heart. According to recent researches, it has been seen that PPG signals contain a lot of information about the physiological and biological state of related person. Most of these studies are based on the analysis of characteristics and waveforms of PPG signals obtained with a single wavelength in time and frequency domains. In this study, 10 minutes of data was taken from the left index finger of a 24-year-old male, which was positioned horizontally using a MAX30100 sensor and Raspberry Pi 4 microprocessor kit. Experiments are carried out in the fully resting state of a male volunteer in outdoors and stressful environments. While the MAX30100 sensor shows the heartbeat on the screen, it also gives PPG signal data, which is a single wavelength, into a .csv file as received data. In these cases, five different time domain parameters of received PPG signals are extracted. When the results are interpreted, it is seen that all results are meaningful and consistent.

In this study, polystyrene (PS) and PS/TiO₂ nanofibers were fabricated through electrospinning and quantitatively characterized to analyze and predict fiber diameters. To advance predictive methodologies for materials design, artificial neural network (ANN) models based on multilayer perceptron (MLP) and radial basis function (RBF) architectures were developed using system- and process-level parameters as inputs and the fiber diameter as the output. Two data classes were constructed: Class 1, consisting of PS/TiO₂ nanofibers, and Class 2, containing both PS and PS/TiO₂ nanofibers. The architectural optimization of the ANN models, particularly the number of neurons in hidden layers, had a critical influence on the correlation between predicted and experimentally measured fiber diameters. The optimal MLP configuration employed 40 and 20 neurons in the hidden layers, achieving mean square errors (MSEs) of 4.03 × 10⁻³ (Class 1) and 7.01 × 10⁻³ (Class 2). The RBF model reached its highest accuracy with 30 and 250 neurons, yielding substantially lower MSE values of 1.42 × 10⁻³² and 2.75 × 10⁻³² for Class 1 and Class 2, respectively. These findings underline the importance of methodological rigor in data-driven modeling and demonstrate that carefully optimized ANN frameworks can serve as powerful tools for predicting structural features in nanostructured materials, thereby supporting rational materials design and synthesis.