It is known that, with the development of artificial intelligence science in recent years, it has started to be used in all areas of life. Due to the increase in diseases that threaten human life, such as epidemics and cancer, more attention has been paid to research in this field. Especially in the field of biomedical image processing, very successful results have been obtained in recent years with the use of deep learning methods. For this study, MR images are utilized to diagnose brain tumors. To assist doctors and radiologists in automatic brain tumor diagnosis and to overcome the need for manual diagnosis, a brain MR image automated classification system is being developed. The data used in the study are open access data obtained from the Kaggle library. This paper presents a novel approach for classifying brain MR images utilizing a dataset of 7022 MR images. To give an unbiased evaluation of the dataset, it is divided into a 40% test and 60% training set. Respectively, VGG, ResNet, DenseNet and SqueezeNet architectures are trained and used for feature extraction from brain MRI images. In order to classify the extracted features, machine learning methods (Support Vector Machines, K-Nearest Neighbors, Naive Bayes, Decision Tree, Linear Regression Analysis) are applied first, then an ensemble learning method is applied and the best validation method is selected. In addition, parameter optimization is applied to the trained CNN algorithms. In order to develop the proposed methods, the Python software program was used in the training and testing phases of the models, and the classification success rates were mutually evaluated. Among the results found, it can see that the ResNet architecture reached 100% accuracy. The data obtained as a result of the study were compared with the results of similar studies. In conclusion, the techniques and methods applied highlight their effectiveness in accurately classifying brain MRI images and their potential to improve diagnostic capabilities.

In recent years, as a result of the increasing demand for health services, medical waste (MW) generated from health facilities has increased significantly. Problems that threaten the environment and public health may arise as a result of inadequate medical waste management (MWM), especially in densely populated metropolitan areas. Therefore, it is very important that the disposal process of waste is carried out in a way that minimizes harm to human health and the environment. MW disposal site selection is among the most important decisions that local governments make. These decisions have many conflicting and similar criteria and alternatives. However, decision-makers may experience significant uncertainty when evaluating the alternatives. This study adopts the interval-valued neutrosophic (IVN) fuzzy EDAS method for the evaluation of MW disposal facility siting alternatives in Istanbul. This approach is used to evaluate potential sites based on a comprehensive, hierarchical criteria framework designed to address data uncertainty and inconsistency common in multi-criteria decision-making (MCDM) scenarios. Within the scope of the study, six main criteria (distance settlement area, social acceptance, costs, environmental impacts, infrastructure facilities, and disaster and emergency) and nineteen sub-criteria are meticulously analyzed. Considering the geographical location and dense urban texture of Istanbul, the study emphasizes the criteria related to distance to residential areas, logistics costs, and potential disaster risks. Among the identified criteria, land costs, topographical features, proximity to landfills, and distance to high-voltage lines are emphasized as the least important criteria. This study, which evaluated various alternatives, identified Pendik, located on the Anatolian side of Istanbul, as the most suitable site for MW disposal due to its minimal risk. The study also compares the four main alternatives and highlights their relative strengths and weaknesses.

Unemployment is the most important problem that countries need to solve in their economic development plans. The uncontrolled growth and unpredictability of unemployment are some of the biggest obstacles to economic development. Considering the benefits of technology to human life, the use of artificial intelligence is extremely important for a stable economic policy. This study aims to use machine learning methods to forecast unemployment rates in Turkey on a monthly basis. For this purpose, two different models are created. In the first model, monthly unemployment data obtained from TURKSTAT for the period between 2005 and 2023 are trained with Artificial Neural Networks (ANN) and Support Vector Machine (SVM) algorithms. The second model, which includes additional economic parameters such as inflation, exchange rate, and labor force data, is modeled with the XGBoost algorithm in addition to ANN and SVM models. The forecasting performance of both models is evaluated using various performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings of the study show how successful artificial intelligence methods are in forecasting economic developments and that these methods can be used in macroeconomic studies. They also highlight the effects of economic parameters such as exchange rates, inflation, and labor force on unemployment and reveal the potential of these methods to support economic decisions. As a result, this study shows that modeling and forecasting different parameter values during periods of economic uncertainty are possible with artificial intelligence technology.

Accurate stock price forecasting is crucial for supporting informed investment decisions, effective risk management, and the identification of profitable market opportunities. Financial time series present considerable challenges for prediction due to their complex, nonlinear dynamics and sensitivity to a wide range of economic factors. Although various statistical methods have been developed to model the multidimensional relationships inherent in such datasets, advancements in big data technologies have greatly facilitated the recording, analysis, and interpretation of large-scale financial data, thereby accelerating the adoption of deep learning (DL) algorithms in this domain. In the present study, RNN-, LSTM-, and GRU-based models were developed to forecast the closing prices of two airline stocks, with hyperparameter optimization conducted via the Bayesian optimization algorithm. The dataset consisted of daily closing prices of THYAO and PGSUS stocks obtained from Yahoo Finance. Comparative analysis demonstrated that the GRU model yielded the highest accuracy for THYAO stock price prediction, achieving a MAPE of 3.05% and an RMSE of 3.195, whereas for PGSUS, the model achieved a MAPE of 3.97% and an RMSE of 3.232. Beyond its empirical contribution, this study also emphasizes the conceptual relevance of symmetry in financial forecasting. The proposed deep learning framework captures the balanced relationships and nonlinear interactions inherent in stock market behavior, reflecting both symmetry and asymmetry in market responses to economic factors.

Forecasting streamflow in stream basin systems plays a crucial role in facilitating effective urban planning to mitigate floods. In addition to employing intricate hydrological modeling systems, machine learning and statistical techniques offer an alternative means for streamflow forecasts. Nonetheless, the precision and dependability of these methods are subjects of paramount importance. This study rigorously investigates the effectiveness of three distinct machine learning techniques and two statistical approaches when applied to streamflow data from the Göksu Stream in the Marmara Region of Turkey, spanning from 1984 to 2022. Through a comparative analysis of these methodologies, this examination aims to contribute innovative advancements to the existing methodologies used in the prediction of streamflow data. The methodologies employed include machine learning methods such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) and statistical methods such as Simple Exponential Smoothing (SES) and Autoregressive Integrated Moving Average (ARIMA) model. In the study, 444 data points between 1984 and 2020 were used as training data, and the remaining data points for the period 2021–2022 were used for streamflow forecasting in the test validation period. The results were evaluated using various metrics, such as the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R²), and Nash–Sutcliffe efficiency (NSE). Upon analyzing the results, it was found that the model generated using the XGBoost algorithm outperformed other machine learning and statistical techniques. Consequently, the models implemented in this study demonstrate a high level of accuracy in predicting potential streamflow in the river basin system.

Background/Objectives: In recent years, there has been a significant increase in the number of women with breast cancer. Breast cancer prediction is defined as a medical data analysis and image processing problem. Experts may need artificial intelligence technologies to distinguish between benign and malignant tumors in order to make decisions. When the studies in the literature are examined, it can be seen that applications of deep learning algorithms in the field of medicine have achieved very successful results. Methods: In this study, 11 different deep learning algorithms (Vanilla, ResNet50, ResNet152, VGG16, DenseNet152, MobileNetv2, EfficientB1, NasNet, DenseNet201, ensemble, and Tuned Model) were used. Images of pathological specimens from breast biopsies consisting of two classes, benign and malignant, were used for classification analysis. To limit the computational time and speed up the analysis process, 10,000 images, 6172 IDC-negative and 3828 IDC-positive, were selected. Of the images, 80% were used for training, 10% were used for validation, and 10% were used for testing the trained model. Results: The results demonstrate that DenseNet201 achieved the highest classification accuracy of 89.4%, with a precision of 88.2%, a recall of 84.1%, an F1 score of 86.1%, and an AUC score of 95.8%. Conclusions: In conclusion, this study highlights the potential of deep learning algorithms in breast cancer classification. Future research should focus on integrating multi-modal imaging data, refining ensemble learning methodologies, and expanding dataset diversity to further improve the classification accuracy and real-world clinical applicability.