# Special Issue on eHealth Innovative Approaches and Applications
## 2 Published Articles
The findings not only provided insights related to the unique challenges associated with Android programming, but also suggested possible customised educational strategies that can optimise the learning experience. [PERSON] et al. (contribution 3) presented a framework for the efficient management of IoMT devices over an Information Centric Network (ICN). The current massive traffic growth generated by IoMT causes challenges related to the transmission of these data [12]. An approach to reduce IoMT traffic is the adoption of ICN [28], which leverages persistent naming multicast communication and thus reduces the response time. In this way, ICN used for IoMT can reduce the overhead due to the distribution of commonly accessed content. On the other hand, the excessive and unbalanced energy consumption of the IoMT devices that the ICN could cause can degrade the performance of the network. To address this issue, the authors presented a framework called the Dynamic Cache Scheme (DCS), which implements energy-efficient cache scheduling in IoMT over ICN with the aim of reducing network traffic and mitigating energy-related issues. This framework establishes a balance between multi-hop traffic and data item freshness by leveraging an effective use for caching in IoMT. The authors tested several parameters, such as the cache-hit ratio, stretch and content retrieval latency, comparing the obtained results with the current state of the art. The results demonstrated that the DCS approach outperforms the compared models in terms of the cache-hit ratio, stretch and content retrieval latency. The authors also assessed its possible adoption in other domains and scenarios. [PERSON] et al. (contribution 4) compared different ML models for the correct classification of COVID-19 and influenza (which present similar symptoms) during coexisting outbreaks in Brazil. In detail, they tested Decision Tree (DT), Multilayer Perceptron (MLP), Gradient Boosting Machine (GBM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), K-Nearest Neighbors, Support Vector Machine (SVM), and Logistic Regression algorithms on real COVID-19 and influenza datasets, which are openly available and were acquired by Brazilian healthcare. The obtained results were also assessed using a 10-fold cross-validation method, which increased their confidence. Finally, the authors analysed and commented on the obtained results, highlighting some specific features of each tested approach, and providing useful hints for the adoption of ML-based systems in the decision-making processes of physicians and policy makers. The paper from [PERSON] et al. (contribution 5) discusses the cyber security issues related to the transmission of standard DICOM (Digital Imaging and Communications in Medicine) medical images. The universal adoption of DICOM images has revolutionised digital imaging in medicine, providing standard tools for the storage, sharing and transmission of both medical images and their related data (included, in a standard way, into the DICOM tags of the image). On the other hand, the implementation and deployment of the DICOM protocol often suffer from incomplete understanding, causing cyber security vulnerabilities within the healthcare ecosystem [29]. The authors of the paper proposed a specific implementation of DICOM for the communication of images and their related data to the PACS (Picture Archiving and Communication System) servers, whose main purpose is to address possible cyber threats. Moreover, they also implemented a simulation environment which is able to produce network traffic related to the use of the DICOM protocol. Overall, the resources provide the researchers with an advanced platform for the implementation of security control tests for evaluating the robustness of security measures, identifying vulnerabilities, and testing security configurations. In this way, they gain a comprehensive understanding of DICOM communication and its practical implementation.