Abstract
Medical image analysis is crucial for the efficient diagnosis of many diseases. Typically, hospitals maintain vast repositories of images, which can be leveraged for various purposes, including research. However, access to such image collections is largely restricted to safeguard the privacy of the individuals whose images are being stored, as data protection concerns come into play. Recently, the development of solutions for Automated Medical Image Analysis has gained significant attention, with Deep Learning being one solution that has achieved remarkable results in this area. One promising approach for medical image analysis is Federated Learning (FL), which enables the use of a set of physically distributed data repositories, usually known as nodes, satisfying the restriction that the data do not leave the repository. Under these conditions, FL can build high-quality, accurate deep-learning models using a lot of available data wherever it is. Therefore, FL can help researchers and clinicians diagnose diseases and support medical decisions more efficiently and robustly. This article provides a systematic survey of FL in medical image analysis, specifically based on Magnetic Resonance Imaging, Computed Tomography, X-radiography, and histology images. Hence, it discusses applications, contributions, limitations, and challenges and is, therefore, suitable for those who want to understand how FL can contribute to the medical imaging domain.

Abstract
Taxi demand prediction with scarce historic information is among the most encountered challenges of the present decade for the traffic network of a smart city. Lack of sufficient information results in the failure of conventional approaches in prediction for a new city. Additionally, the prevalent Deep Neural Network (DNN) Models resort to ineffectual approaches which fail to meet the required prediction performance for the network. Moreover, existing domain adaptation (DA) models could not sufficiently reap the domain-shared features well from multiple source, questioning the models' applicability. Complex structure of these DA models tends to a nominal performance gain due to inefficient resource utilization of the sources. The present paper introduces a domain adaptation deep neural network model, Bootstrap Zero-Shot (BTS-Z) learning model which focuses on capturing the latent spatio-temporal features of the whole city traffic network shared among every source city and maneuver them to predict for the target city traffic network with no prior information. The presented model proves the efficacy of the bootstrap algorithm in the prediction of demands for the unseen target over the computationally expensive MAML models. The experimental results on three real-world city taxi data on the standard benchmark metrics report a minimum of 23.41% improvement over the best performing competitive system. © 2024 IEEE.

Abstract
Accurate identification of transportation mode distribution is essential for effective urban planning. Recent advancements in machine learning have spurred research on automated Transportation Mode Detection (TMD). While existing TMD methods predominantly employ standard flat classification methods, this paper introduces HiClass4MD, a novel hierarchical approach. By leveraging the misclassification errors from standard flat classifier, HiClass4MD learns the class hierarchy for transportation modes. Although hierarchical metrics initially indicated performance improvements when applied to real-world GPS trajectories dataset, a subsequent evaluation using conventional metrics revealed inconsistent results. While decision trees benefited marginally, other classifiers exhibited no significant gains or even degraded. This study highlights the complexity of applying hierarchical classification to TMD and underscores the need for further investigation into the factors influencing its effectiveness.

Abstract
Self-adaptive Systems (SaS) are becoming increasingly important for adapting to dynamic environments and for optimizing performance on resource-constrained devices. A practical approach to achieving self-adaptability involves using a Pareto-Front (PF) to store the system's hyper-parameters and the outcomes of hyperparameter combinations. This paper proposes a novel method to approximate a PF, offering a configurable number of solutions that can be adapted to the device's limitations. We conducted extensive experiments across various scenarios, where all PF solutions were replaced, and real world scenarios were performed using actual measurements from a Human Activity Recognition (HAR) system. Our results show that our method consistently outperforms previous methods, mainly when the maximum number of PF solutions is in the order of hundreds. The effectiveness of our method is most apparent in real-case scenarios where it achieves, when executed in a Raspberry Pi 5, up to 87% energy consumption reduction and lower execution times than the second-best algorithm. Additionally, our method ensures a more evenly distributed solution across the PF, preventing the high concentration of solutions.

Abstract
This paper introduces the Large Events Model (LEM) for soccer, a novel deep learning framework for generating and analyzing soccer matches. The framework can simulate games from a given game state, with its primary output being the ensuing probabilities and events from multiple simulations. These can provide insights into match dynamics and underlying mechanisms. We discuss the framework's design, features, and methodologies, including model optimization, data processing, and evaluation techniques. The models within this framework are developed to predict specific aspects of soccer events, such as event type, success likelihood, and further details. In an applied context, we showcase the estimation of xP+, a metric estimating a player's contribution to the team's points earned. This work ultimately enhances the field of sports event prediction and practical applications and emphasizes the potential for this kind of method.

Abstract
In manufacturing chatter is an unwanted phenomenon that can lead to product quality reduction and tool wear. Real time chatter detection is key to preventing these issues and improving overall machining efficiency. In this paper we propose an unsupervised chatter detection method using autoencoders (AE) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm that uses internal signals of Computer Numerical Control (CNC) machines. The proposed method starts by using an AE to extract features from raw internal signals collected from CNC machines. This step reduces the dimensionality of the data and captures the underlying patterns of chatter. Then the extracted features are fed into DBSCAN clustering algorithm which is a density based algorithm that groups similar data points and identifies outliers. We tested the proposed method with real world data collected from various CNC machines. The results show that our unsupervised chatter detection method has high accuracy, precision and recall, can detect chatter and distinguish it from normal machining. Also the method is robust to noise and can adapt to dynamic machining conditions. In summary our work presents an unsupervised chatter detection method using AE and DBSCAN clustering that uses internal signals of CNC machines. This method is a reliable and efficient solution for real time chatter detection so manufacturers can improve product quality, optimize machining process and reduce tool wear during machining.