# Special Issue on eHealth Innovative Approaches and Applications
## 2 Published Articles  
The main purpose of this Special Issue was to collect recent developments and research in the eHealth domain, presenting innovative approaches and applications while considering a multidisciplinary approach and multi-perspective views, including medicine, Information and Communication Technologies, Artificial Intelligence (AI), Internet of Things (IoT), Big Data Analytics, Cyber Security, and others. The focus of this Special Issue centres on the whole e-health domain. In total, there were 13 contributions selected for this Special Issue (listed after Section 3), which propose innovative approaches and applications in the previously mentioned areas. These articles have been provided by researchers with broad expertise in different fields and backgrounds, such as medicine, informatics and engineering. The published papers belong to the main specific areas of eHealth, while some papers present multidisciplinary approaches; therefore, they are included in more than one area: (i) COVID-19 classification; (ii) Artificial Intelligence; iii) Medical Imaging; (iv) Fractal Analysis; (v) Internet of Medical Things (IoMT); (vi) Machine Learning; (vii) Cyber Security; viii) Mobile Medical Apps; (ix) Scientific Literature Retrieval; and (x) Large Language Models. A summary of the papers published in this Special Issue is presented below. [PERSON] et al. (contribution 1) described a complex Deep Learning architecture, whose main aim is to improve the eXtreme Multilabel Text Classification problem [26] related to the classification and large-scale semantic indexing of scientific articles in PubMed [27]. In detail, the authors proposed an architecture called the Hierarchical Deep Neural Network (HDNN), which reproduces the same topology of the label set, in the case where this set is hierarchically organised (such as the MeSH adopted by PubMed). The experimental assessment performed by the authors compared the performances of the proposed HDNN with a classic flat CNN DL architecture for text classification when applied to task of semantically indexing PubMed articles. The results showed that the HDNN outperforms the CNN, in particular in the cases of hierarchical measures, demonstrating that, when a hierarchical label set is available, the reproduction of the label hierarchy directly into the DL architecture provides significant benefits. On the other hand, this complex and deep architecture has a high computational time, due to the need, during training, of propagating the weights calculated in a certain level to the next level before updating the weights of the next level of the HDNN architecture. Therefore, a more efficient parallel architecture is foreseen as an area of future work by the authors. The contribution of [PERSON] (contribution 2) introduced an innovative Problem-Based Learning (PBL) methodology to enhance the teaching of Android programming education, specifically tailored for the development of nutritional applications. The main novelty of the proposed approach is the integration of advanced programming concepts with practical application development, fostering deeper understanding and engagement among students. In detail, this application allows the programmers to access extensive and detailed nutritional information from different open-access food databases. The proposed approach was tested in a research project that involved third-year students of the Computer Science faculty at the University of Deusto in Spain. The students were engaged in the development of applications aimed at improving access to nutritional knowledge. The preliminary results of this research indicated a significant improvement in student engagement and learning outcomes compared to traditional teaching methods, underlining the possible use of this methodology in fostering research and advancing educational practices in computer science.