The increasing frequency of natural disasters and climate change events presents significant challenges for emergency response operations. Area monitoring for civil protection purposes is critical to safeguard resources, track and survey assets, and optimize decisionmaking. Coordination of resources is crucial in crisis situations where timely and accurate information is essential. Aviation traffic plays a key role in civil protection missions such as firefighting or search and rescue operations. Monitoring aviation and satellite data is of major importance and is one of the primary goals of this research. Real-time monitoring and alerting can be extremely useful to local authorities, NGOs, and the general public. In this paper, we introduce an online visualization tool that uses data aggregation/fusion to provide command capabilities and informative visualizations along with great functionality for real-time command and control operations. This system could be particularly beneficial for multiple stakeholders, including municipalities and the Ministry of Climate Crisis and Civil Protection.
Early detection of Coronary Artery Disease (CAD) and Non-Small Cell Lung Cancer (NSCLC) is crucial for improving patient outcomes. In this study, RGB-CNN (Convolutional Neural Network) was implemented, and trained from scratch using Polar Maps for CAD diagnosis and Computed Tomography (CT) images for NSCLC diagnosis. The CNN predictions were then integrated with clinical data into a Fuzzy Cognitive Map (FCM) classifier for each type of diagnosis. Nuclear medicine experts provided linguistic values in the form of fuzzy sets to define the relationships between input and output concepts, which were later converted into interval values. Extreme Learning Machine (ELM) and Genetic Algorithm (GA) were applied to the FCM learning process to refine the interconnections based on expert knowledge. To ensure the robustness of the results, 10-fold cross-validation was employed. The DeepFCM-ELM model demonstrated superior performance, achieving 80.4% similar to 4.97% accuracy for CAD diagnosis, and 91.9% similar to 3.07% for NSCLC diagnosis using CT images. Heatmaps were generated to interpret CNN predictions by highlighting pathological regions. These heatmaps were then used in GPT, along with DeepFCM weights, CNN, and DeepFCM prediction and input clinical values, employing Natural Language Generation to translate DeepFCM results into humanreadable language, enhancing the model's overall explainability. All these techniques have been integrated into a Medical Decision Support System (MDSS) designed to effectively manage both medical classification challenges.
Software accessibility reveals how easy it is to use an interactive system, regardless of disabilities and devices used. Website accessibility is particularly important due to the popularity of the Web. This is why there are web accessibility standards, such as the Web Content Accessibility Guidelines (WCAG), and technologies available that can help in developing accessible websites. WCAG includes many criteria and techniques that need to be followed to ensure website accessibility. As a result, software tools, often referred to as automated accessibility evaluation tools, have been developed to facilitate this rather demanding task. These tools do, however, report many potential accessibility issues that call for human judgment and thus still require a significant amount of time and effort. Do such potential issues reported by automated tools affect the results of guideline-based web accessibility evaluation? This paper attempts to provide an answer to this question in the context of web accessibility evaluation of Greek public hospitals. To this end, 112 Greek public hospital websites were evaluated against WCAG 2.1 using the TAW automated tool. Results found a significant effect of the manual inspection of potential issues on the accessibility results obtained. The only exception was when the data were analyzed per WCAG priority level where no significant effect was found for level AA. We also build linear regression models to estimate the number of actual accessibility issues based only on the ones that are automatically identified.
The explosive increase in sensory equipment for the gathering of data in recent years has pushed computing resources to their limits and has created the need for a more decentralized approach in Big Data Analytics. The term Edge Computing refers to this new paradigm where computation is performed away from the traditional cloud, in a more distributed manner and physically closer to the actual gathering of data. However, in large scale applications like an energy smart grid system spanning entire cities, there are many challenges that need to be faced for such a system to perform effectively, namely the continuous communication and exchange of information between heterogeneous edge devices, sensors and actuators, and the division of devices in spatial clusters that need to perform special operations depending on the characteristics of the area. The aim of this paper is to provide a small-scale conceptual architecture for the communication of clusters of edge devices in a smart grid system, using the actor model and the AKKA framework as a communication backbone.
This paper compares three of the most commonly used applicationlayer communication protocols, namely HTTP3, MQTT, and CoAP, for IoT applications. While HTTP3 is used as a baseline, MQTT and CoAP are optimized for resource-constrained environments and have thus found widespread use in IoT. This research work investigates the mechanisms, design features, and performance characteristics of each protocol and outlines their respective strengths and weaknesses. It will also carry out practical simulations to assess the utilization of processing resources under different communication models, such as publish-subscribe and request-response. The results of this work will be very useful in selecting an appropriate protocol for different IoT use cases with a trade-off between efficiency, security, and ease of implementation.
