Humerus anomalies are a problem that requires rapid and accurate diagnosis to ensure immediate and efficient treatment. In this context, the main goal of this paper is to develop and analyze well-known Convolutional Neural Network models for the automatic recognition of humeral fractures, with the aim of proposing a useful tool for healthcare personnel. Specifically, three distinct architectures were implemented and compared: a three-layer untrained neural network, a network based on the ResNet18 architecture and one based on the DenseNet121 model, both of which were trained. The performance analysis highlighted a trade-off between accuracy and generalization ability, showing better accuracy in the pre-trained models - in particular, the DenseNet121 model achieved optimal accuracy across multiple runs of 85%. - which however proved more prone to suffer from overfitting compared to the non-pre-trained model. As a result, this study aims to propose the integration of deep learning tools in medical practice, laying important foundations for future developments, with the hope of improving the efficiency and accuracy of orthopedic diagnoses.

Modeling and automatically detecting complex events in different domains, such as video surveillance and healthcare, is becoming an increasingly topical issue nowadays. In fact, deriving knowledge on higher level from low-level events by combining the latter to complex structures is the task of an Event Query Language (EQL), whose main issue is the lack of formal semantics. Consequently, in order to cope with this issue, in this paper we propose $$ISEQL+$$ , an extension of ISEQL (an Interval-based Surveillance Event Query Language, that we previously defined), aimed at further improving its expressiveness. More specifically, we provide formal proofs demonstrating that the language fully covers the well-known Allen’s interval relationships, additionally supports conditional overlap ratio and conditional cardinality constraints over the interval relationships, provides robustness with respect to small variations in the intervals, and can be formalized as relational algebra extension, which will in turn allow a very efficient implementation exploiting an existing algorithm. Eventually, we also show how typical events in the healthcare domain can be easily expressed via $$ISEQL+$$ .

Telemonitoring and wearable devices are transforming healthcare by enabling continuous patient monitoring and personalized interventions. However, their integration remains fragmented due to technical, ethical, and regulatory barriers. This systematic review, following PRISMA 2020 guidelines, synthesizes insights from 165 studies (2020-2024) to assess key advancements and persistent challenges. AI-driven diagnostics show potential but require clinical validation, while energy-efficient communication protocols lack standardization. Data security improvements, such as hybrid encryption, enhance protection but introduce computational overhead. Regulatory misalignment and clinician skepticism hinder interoperability and large-scale adoption. Despite usability advancements, disparities in patient accessibility and clinician engagement highlight the need for human-centered design. To convert promises into practice, we outline a blueprint that pairs harmonized regulatory pathways with explainable, edge-deployed AI, leverages low-latency data exchange, and promotes cross-sector interoperability convergence. Realizing this agenda through longitudinal, multicenter trials and inclusive, community-anchored implementation will unlock equitable and scalable telemonitoring ecosystems.

In this demo paper, we present SPARK (Semantic Planning with Augmented Retrieval and Knowledge), an AI-driven web application that generates personalized itineraries with natural language input. By addressing the limitations of traditional routing systems and machine learning models that lack adaptability to user intent and real-time contexts, SPARK integrates a Neo4j [6] graph that built on OpenStreetMap [8] data with semantic analysis, custom route optimization, and retrieval-augmented generation. By using large language models (LLM), the system outputs travel planning with real-time and enriched data via external APIs. Also, SPARK dynamically adapts to personalized user queries and optimizes routes based on contextual relevance. As demonstrated in an urban scenario, this demo paper highlights the potential of combining graph-based retrieval and LLMs to deliver flexible and context-aware route planning. Meanwhile, our demo shows that the proposed system is scalable and deployable for travel guidance.

Telemedicine has gained increasing relevance, particularly after COVID-19, emphasizing the need for digital tools to support clinicians and enhance access to care. In such a context, this paper presents a system for automated nevus and melanoma analysis using two methods: a pre-trained ResNet-50 CNN for dermoscopic image feature extraction, and a LinearSVC classifier for structured clinical data collected by medical doctors. The framework was evaluated on 9,024 dermoscopic images and 200 clinically documented lesions; the results demonstrate how machine learning can support skin lesion diagnosis, improving efficiency and accessibility.

The technique of recording and evaluating (processing) streams of data about occurrences and drawing conclusions from them is known as Complex Event Processing, or CEP. In recent years, due to ever-increasing security concerns, CEP is being applied increasingly broadly in heterogeneous domains in order to detect potentially dangerous events early or even prevent their occurrence; possible examples are bank robberies in the context of video surveillance, or identity theft in the domain of social network analysis. For such reasons, in this paper we propose a survey reporting the most interesting and innovative approaches to complex event processing in heterogeneous domains; specifically, here we focus on video analysis, social network analysis, and healthcare, classifying the papers into different subcategories and highlighting their advantages and disadvantages.

Abstract This paper describes an intelligent ecosystem that can continuously monitor patients’ health conditions, whether at home, at work or during recreational activities, by leveraging a creative blend of wearable medical devices, intelligent agents (IA) and complex event processing (CEP). With the help of a smart application, linking wearable devices and the power of IA and CEP, patients will be constantly and actively supervised during their daily activities. This can even save their lives in case they experience sudden or gradual problems. Thanks to our system, patients with chronic illnesses that are not serious but potentially unstable will no longer overburden first aid services. This is also helpful in containing the spread of COVID-19. Specifically, in this paper, we focus on automatic monitoring of vital parameters, electrocardiogram analysis and psoriasis detection. Experimental results conducted on real patients show how promising our approach is.

Reasoning with temporal data has attracted the attention of many researchers from different backgrounds including artificial intelligence, database management, computational linguistics and biomedical informatics. More specifically, activity detection is a very important problem in a wide variety of application domains such as video surveillance, cyber security, fault detection, but also clinical research. Thus, in this paper we present a prototype architecture designed and developed for activity detection in the medical context. In more detail, we first acquire data in real time from a cricothyrotomy simulator, when used by medical doctors, then we store the acquired data into a scientific database and finally we use an Activity Detection Engine for finding expected activities, corresponding to specific performances obtained by the medical doctors when using the simulator. Some preliminary experiments using real data show the approach efficiency and effectiveness. Eventually, we also received positive feedbacks by the medical personnel who used our prototype.