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.

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.

With the rapid advancement of technology and growing concerns about data privacy, federated learning (FL) has attracted considerable attention from the scientific community. The emergence of FL as a novel machine-learning approach and the volume of relevant papers and studies now call for a thorough investigation of FL. In the present research, an analysis was conducted on 3107 articles about federated learning exported from the Web of Science (WoS). The paper performs a bibliometric analysis to examine the productivity, citations, and bibliographic matching of significant authors, universities/institutions, and countries. The evolution of research material on federated learning over time was analyzed in the research. The study also provides comprehensive analysis by examining the most frequently used terms in the articles and attempting to identify trending areas of study with federated learning. This paper offers primary information on FL for readers worldwide and a comprehensive and accurate analysis of potential contributors.

Escalating global production and consumption are driving rapid growth in energy demand, increasing pressure on finite natural resources. In response, this study proposes a data-driven framework that integrates deep learning-based electricity demand forecasting with economy-wide input–output material footprint analysis to support long-term energy planning and policymaking. The innovative aspect of this framework is its ability to jointly assess future electricity generation and related material requirements within a single analytical structure. A comparative analysis is conducted for Türkiye, Germany, and Spain, evaluating the material footprint of electricity generation across renewable and fossil-based energy sources under business-as-usual (BAU) and alternative energy development scenarios. The forecasting models demonstrate strong predictive performance, achieving Mean Absolute Percentage Error (MAPE) values of 1.39% for Türkiye, 4.39% for Germany, and 3.90% for Spain, significantly outperforming conventional statistical methods. Scenario-based results indicate that sustainability-oriented pathways (ST and GCA) can reduce material requirements by approximately 20–30% compared to the BAU scenario, particularly for metal-intensive inputs such as iron and refined oil. The findings underscore the importance of integrating material footprint considerations into energy transition strategies and provide practical insights for policymakers seeking to balance energy security with resource sustainability. The study highlights the value of integrated analytical approaches in supporting more resilient and resource-efficient energy systems.