# Special Issue on eHealth Innovative Approaches and Applications
## 2 Published Articles
(contribution 9) described Chidroid, a mobile Android application whose purpose is retrieving, collecting and distributing logs from smart healthcare devices in the domain of the IoMT. The growing adoption of IoMT-connected devices in healthcarehas also caused an increase in cyber attacks [36], which could potentially provide attackers with access to patients' Personal Health Information (PHI) [37]. The approach described in this paper allows for the creation of datasets, semi-structured data or structured data from unstructured data. These datasets can be very useful for the development of AI methods for detecting cyber security threats and vulnerabilities and mitigating them. Moreover, Chidroid can be used as a policy-based tool to analyse the security issues in the most recent versions of Android. The tests performed demonstrated the effectiveness of the application, which retrieves logs and system metrics from several IoMT devices. Finally, the paper presented a method to perform a security analysis on Android devices that uses minimal system resources and reduces battery consumption. The study from [PERSON] et al. (contribution 10) focuses on an entropy-based fuzzy clustering technique for interval-valued data (EFC-ID) for cancer detection. The early detection of cancer can improve the chances of successful treatment, and screening techniques have the purpose of identifying possible signs of specific cancers or pre-cancers before symptoms have developed. On the other hand, precise detection mainly relies on human experience, and is thus affected by human and visual inspection errors. The authors tested the proposed clustering approach on the Breast dataset, demonstrating that EFC-ID can obtain better results compared to FKM in terms of several metrics (AUC, sensitivity and specificity). Moreover, further experiments on the Multiple Myeloma data demonstrated that EFC-ID outperforms the classic FKM in terms of Chi-squared, Accuracy rate and Adjusted Rand Indexes. The obtained results confirmed that the proposed EFC-ID is able to correctly identify the natural partition. [PERSON] et al. (contribution 11) presented a review paper that reports on the recent literature on the applications of fractal analysis devoted to the diagnosis of oral cancer and Oral Potentially Malignant Disorders. In the case of fractal analysis applied to diagnostics, the Fractal Dimension (FD) is usually calculated as a measure of the degree of regularity of a tissue or structure [38]. This feature can be applied to the analysis of lesions (such as oral lesions) to determine the degree of irregular tissue/vascularisation derailment; physicians correlate this with the nature of the lesion with the aim of identifying possible cancerous lesions. This paper evaluates the recent published literature on the fractal analysis of oral cancer and its precursors, namely Oral Potentially Malignant Disorders (OPMDs), investigating the specific FD that can be predictive of cancer and OPMDs. The authors only considered articles from three literature databases (Scopus, PubMed and Web of Science), and investigated according to the PRISMA checklist to analyse if fractal analysis can support the diagnosis of oral cancer and, moreover, if it can distinguish it from its precursors. The results of this review highlighted that fractal analysis, when applied to oral oncology, is promising, because it can be adopted as an effective and noninvasive diagnostic and prognostic tool for various premalignant lesions, especially for measuring the progression of premalignant lesions and OPMDs [39]. [PERSON] et al. (contribution 12) framed the current state of cyber hygiene, explaining the related context and habits of end-users. They then proposed several best practices that should be adopted by healthcare organisations and professionals in order to achieve a high level of cyber hygiene, particularly regarding human-centric approach.