Breast cancer diagnosis is a crucial domain where Explainable Artificial Intelligence (XAI) integration holds immense importance. Understanding AI model decisions not only enhances trust but also aids in treatment strategies. However, the need for explainability must address privacy concerns, prompting the exploration of Federated Learning. This study explores the intersection of Explainable AI, Privacy, and Federated Learning in breast cancer diagnosis. Utilizing Wisconsin Diagnostic Breast Cancer Dataset and Wisconsin Breast Cancer Dataset, our results showcase that Federated Learning enhances user privacy while maintaining performance, achieving an accuracy of 97.59% and F1 score of 98.393% in Wisconsin Diagnostic Breast Cancer Dataset using artificial neural networks and 97.14% accuracy and 95.65% F1 score in Wisconsin Breast Cancer Dataset employing XGBoost. By computing SHAP values locally, we maintain explainability while enhancing privacy. Our findings highlight the potential of federated learning in maintaining privacy and explainability, advancing breast cancer diagnosis and treatment.

In an era of increasing reliance on digital health solutions, safeguarding user privacy has emerged as a paramount concern. Health applications often need to balance advanced AI functionalities with sufficient privacy measures to ensure user engagement. This paper presents the architecture of FLORA, a privacy-first ovulation-tracking application that leverages federated learning (FL), privacy-enhancing technologies (PETs), and blockchain to protect user data while delivering accurate and personalized health insights. Unlike conventional centralized systems, FLORA ensures that sensitive information remains on users’ devices, with predictive algorithms powered by local computations. Blockchain technology provides immutable consent tracking and model update transparency, further improving user trust. In addition, FLORA’s design incentivizes participation through a token-based reward system, fostering collaborative data contributions. This work illustrates how the integration of cutting-edge technologies creates a secure, scalable, and user-centric health application, setting a new standard for privacy-preserving digital health platforms.

Covid-19 is a new invisible threat that has affected almost all countries of the world. In this paper we study the effects of policy responses to the spread of coronavirus pandemic. For this purpose, we utilized the dataset of Oxford Coronavirus Government Response Tracker (OxCGRT) and we examined the correlation of policies with the number of daily coronavirus cases in five countries (Greece, Italy, Spain, Sweden and United Kingdom). In order to achieve that, we calculated Kendall Correlation Coefficient and Spearman Correlation Coefficient as well as p-value for the statistical importance of our data. Our results indicate that the policies have a direct impact on the spread of Covid-19.

Trapped lee waves and the associated turbulent rotors pose significant hazards for aviation and land-based transport. While high-resolution numerical weather prediction (NWP) models can capture these phenomena, there is a lack of automated, reliable tools for detecting and characterizing lee waves in model output. Traditional spectral methods, although useful, are often computationally expensive and require domain-specific tuning. This study presents an operational pipeline based on deep learning (DL) for the real-time detection and characterisation of trapped lee waves. Building on previous research demonstrating the effectiveness of a DL model (LeeWaveNet) for segmenting and extracting wave characteristics from vertical velocity fields in the UK, we have deployed this model in a production environment for the Iceland-Greenland region using an operational NWP model for Iceland and Greenland, named Harmonie-IG. The model was containerized and hosted on a server at Danish Meteorological Institute, receiving Harmonie-IG output as input and producing near-real-time predictions of lee wave activity. The results, including key characteristics such as wavelength, orientation, and amplitude, are stored in an AWS S3 bucket and visualized through an automated post-processing pipeline. To assess the generalizability and reliability of the model beyond the training domain, we performed an extended validation over Norway using the Danish ReAnalysis dataset (DANRA). In particular, we compared the predicted orientation of the lee waves with the observed wind direction, demonstrating promising correlation and physical consistency. This validation step is a key milestone towards confirming the robustness of the model when applied to new geographies and datasets. This work highlights not only the potential of DL in advancing the operational use of AI in meteorology, but also showcases a complete MLOps workflow—from inference at scale to data management and visualization. Our pipeline demonstrates that with the right infrastructure, deep learning models can be effectively integrated into real-time forecasting systems, providing timely and accurate identification of hazardous atmospheric features.