Vehicular monitoring systems have been increasingly adopted to support public safety operations in urban environments. Among available technologies, Automatic License Plate Recognition (ALPR) has attracted attention due to its affordability, scalability, and ease of deployment. However, most existing ALPR applications remain predominantly reactive, relying on static watchlists, deterministic rules, or direct database matching, which may limit their ability to support anticipatory decision-making in dynamic public safety scenarios. To better understand this domain, this article presents a Systematic Literature Review (SLR) on the integration of AI-driven predictive modeling into license plate recognition systems for vehicular monitoring in public safety. The review was conducted across six scientific databases and analyzed different variables, highlighting and comparing critical characteristics in recent literature. The findings show that reactive and rule-based approaches still dominate the field, while predictive solutions remain limited and fragmented, with AI-driven methods gaining relevance to support risk assessment and contextual alert generation. Beyond consolidating existing literature, this review clarifies the transition from reactive plate-recognition applications toward predictive and alert-oriented decision-support systems. Moreover, a technical and governance-oriented taxonomy is proposed to organize the main concepts and guide future research efforts, potentially bringing relevant contributions to the area.

Protocol non-adherence in teledermatology, particularly during image acquisition, such as missing rulers or incorrect image orientations, leads to invalid examinations, delayed diagnoses, and increased patient burdens. This study addresses these challenges by evaluating modern neural network architectures for instance segmentation and proposing a novel orientation correction pipeline to improve adherence in two protocols: Approximation (identifying rulers/patient tags) and Panoramic (correcting body orientation errors). Using the Santa Catarina State Telemedicine System dataset (14,238 images for Approximation; 3,692 for Panoramic), models were benchmarked against previous study measuring computational efficiency and precision metrics. Results demonstrate YOLOv11's superiority, achieving state-of-the-art performance with$86.08 \text{AP}_{75}$, reducing segmentation errors by 14 % compared to Mask R-CNN, while maintaining computational efficiency (1.3 GB VRAM, 566-699 ms latency). For panoramic protocol images, our proposed pipeline mitigated orientation errors by methodically rotating misaligned human masks, improving weighted$F$-scores from 0.51 to 0.82 and significantly reducing misclassifications between valid and invalid poses. While Mask2Former's transformerbased design architecture exhibited higher precision, the computational demands (11.7 GB VRAM) hindered deployability in low-resource clinics. This study concludes that hybrid models like YOLOv11 optimally balance segmentation accuracy and operational efficiency, offering actionable insights for real-world clinical implementation bridging AI advancements with clinical pragmatism, improving protocol adherence to reduce invalid examinations by addressing orientation inconsistencies in teledermatology.

Fifth-generation technology represents a transformative shift in telecommunications, offering enhanced speed, reliability, and ultralow latency. This paper addresses the challenge of load balancing in 5G networks, especially within multi-access edge computing architectures, by evaluating strategies that ensure quality of service and meet the stringent ultra-reliable low-latency communication requirements.Using Free5GC to emulate 5G core and UERANSIM to simulate user equipment and radio access network behavior, the proposed EdgeLB framework integrates the LoxiLB load balancer to evaluate algorithms such as Round Robin, Weighted Round Robin, Hash-based, and Least Connections. Through Netperf-based experiments, we assessed performance under varying numbers of concurrent connections and UEs, as well as network delays that emulate geographic distance.The results demonstrate that intelligent traffic distribution significantly improves network performance and scalability. All algorithms maintained submillisecond latency in ultra-reliable low-latency scenarios, and some exhibited strong jitter and throughput control. Furthermore, polynomial regression models were derived to approximate the degradation of performance under scaling conditions. These findings validate the EdgeLB architecture as a viable solution for latency-sensitive multi-access edge computing and AI inference applications in 5G environments.

The provisioning of Quality of Service (QoS) in 5G networks plays a crucial role in ensuring efficient performance in scenarios characterized by high demand and dynamism. In the data era, where the generation and consumption of information are growing exponentially, effective traffic management becomes indispensable. This study investigates the application of queueing methodologies — FIFO, PQ, and WFQ — in the context of SDN (Software-Defined Networking) for 5G networks, utilizing ONOS as the SDN controller. The research highlights the importance of traffic management in meeting the stringent requirements of 5G networks, such as ultra-low latency, high reliability, and broad transmission capacity, which are essential for applications like eMBB, URLLC, and mMTC. In addition to a comprehensive theoretical review of 5G, SDN, QoS, and queueing techniques, the study included a practical implementation in a simulated environment using the ONOS software, validating theoretical concepts through a comparative analysis of the impact of queueing on critical metrics such as latency, throughput, packet loss, and jitter. The results demonstrate that optimized queueing techniques are essential for efficient traffic management, reducing latency and increasing throughput across different network scenarios. Thus, this study reaffirms the relevance of integrating SDN and queueing techniques as adaptive and effective solutions for managing modern mobile networks. Furthermore, it highlights promising avenues for future research, including the use of adaptive algorithms in next-generation networks.

This systematic literature review investigates the state of the art in the application of artificial intelligence (AI), particularly convolutional neural networks (CNNs), in the diagnosis of pneumoconioses and asbestos-related diseases (ARDs). A total of 30 articles published between 2020 and 2025 were analyzed, selected from major scientific databases (IEEE Xplore, ScienceDirect, Springer Link, ACM Digital Library, Nature, Wiley Online Library). The analysis addressed the models used types of radiological images (chest X-rays and computed tomography), performance metrics, and limitations. A significant advancement was observed in the use of CNNs and 3D architectures, with an emphasis on automated screening and the interpretability of clinical patterns.

Climate change, along with reckless and sometimes criminal human activities, has increased the frequency and severity of wildfires in recent years. These fires not only destroy habitats but also release large quantities of greenhouse gases, further exacerbating global warming. In this context, innovative solutions to facing these alarming climate emergencies are highly welcome. This paper presents an optimised and affordable solution based on an energy self-sufficient Internet of Things platform for wildfire detection, allowing quick fire warning and evacuation procedures even in remote areas. Such architecture leverages the Raspberry Pi Zero 2 board as the computing core and the YOLOv8n object detector for image-based analysis, which allows easy extensions to meet the particularities of any deployment area. Moreover, for wildfire detection far from energy and communication infrastructure, the designed platform employs LoRa communication for long-range alerts and integrates photovoltaic panels. Performance metrics, hardware utilisation, and energy efficiency were assessed, supporting practical exploitation in realworld scenarios.

Improvements in I/O architectures are becoming increasingly required nowadays. This is an essential point to complex and data intensive scalable applications. Data-Intensive Scalable Computing (DISC) and High-Performance Computing (HPC) applications frequently need to transfer data between storage resources. In the scientific and industrial fields, the storage component is a key element, because usually those applications employ a huge amount of data. Therefore, the performance of these applications commonly depends on some factors related to time spent in execution of the I/O operations. However, researchers, through their works, are proposing different approaches targeting improvements on the storage layer, thus, reducing the gap between processing and storage. Some solutions combine different hardware technologies to achieve high performance, while others develop solutions on the software layer. This paper aims to present a characterization model for classifying research works on I/O performance improvements for large scale computing facilities. Analysis over 36 different scenarios using a synthetic I/O benchmark demonstrates how the latency parameter behaves when performing different I/O operations using distinct storage technologies and approaches.

Publications on the Web can influence the public opinion about certain entities (e.g., politicians, institutions). At the same time, a variety of indicators can be extracted from these publications and used to estimate entity performance (e.g., popularity, votes share). This work proposes an automatic method that employs state-of-the-art natural language processing tools to extract indicators about entities mentioned in texts, for estimating the performance of these entities or semantically related ones. Our method calculates performance metrics from performance indicators consolidated for semantically related entities, assess correlations of these consolidated metrics with ground true performance, and uses these metrics to predict certain fluctuations in entity performance. Experimental results in a case study on politics show that consolidated metrics for several interrelated entities are better correlated to observed real performance measures of some target entities and lead to better predictions, than metrics for just one entity.

Artificial immune systems (AIS) are computational intelligence inspired by the human biological immune system. The AIS four main algorithms are negative selection, clonal selection, immune network, and danger theory. This paper incorporates the AIS approach to develop an agent-based detection method to analyze network traffic. The system works with an attack graph based correlation technique. This technique can improve detection performance by decreasing false alerts. This work was tested for denial of service (DoS), remote to local (R2L), user to root (U2R) and probe attack classes. Results have shown the addition of the correlation technique can aid to the detection performance of AIS detection systems.

In this paper, we explore the AIS approach to develop an agent-based detection method to analyze network traffic. The system works in conjunction with attack graph based correlation and software-defined network (SDN) technology to mitigate attacks. In the correlation technique, alerts are correlated through an attack graph which improves detection performance by decreasing the false alert rate. The false alert reduction can avoid the negative effect that an SDN countermeasure can bring to the cloud Service Level Agreement (SLA) on the absence of threats. This work was tested for multi-step and distributed denial of service (DDoS) attacks. Results have shown the addition of the correlation technique can aid to the detection performance of AIS detection systems.

Internet of Things (IoT) is becoming part of our daily life. Indeed, studies predict a sharp market growth by 2020. One of the challenges of this fast-growing market is how to store and manage the amount of non-structured data generated by IoT devices. In this paper, we propose a hybrid storage architecture for IoT for addressing scalability, performance, and heterogeneity issues. We selected three of the most commonly used NoSQL databases (Redis, MongoDB, and Cassandra) to perform the first evaluation of our architecture. Our results suggest that the hybrid storage architecture is a feasible and promising approach to address some of the issues related to the ever-growing amount of data generated by IoT devices. Additionally, our findings also show that Redis achieves a better overall performance for the two chosen types of data, namely scalar and positional.

Traffic reduction in network segments through cache implementations has become a major research topic due to the exponential increase in data requests through the network. Even with high-speed connections, the conventional model still depends on point-to-point communication between two systems. Throughout the world, more connected devices are, accessing services and obtaining information. To support this activity, servers must have massive storage to support creation, retrieval, updated and deletion of large amounts of data. Therefore, in studies of Information Centric Networks (ICN), this model has been widely discussed as the new content distribution model for the Internet. To provide improved network management many approaches are using software-defined networks (SDN) to develop flexible content-based networks. This paper proposes to use cache replication for ICN through SDN to avoid duplicated requests in the same connection. The redundant cache reduced the bandwidth consumed by duplicated requests, from a maximum of 3.20 Gbps to 2.07 Gbps, reducing the bandwidth consumption by 11.3%.

Nowadays, it is a common ground to find an e-health environment flooded by a large amount of data, which comes from several mobile devices/sensors, and could not represent hundred percent of useful information. In other words, a process to enhance this data scenario is an essential effort. Therefore, in this paper, we present an approach oriented to the context which targets to provide more dynamic and personalized services in an e-health environment. The proposal adopts a Quality of Context (QoC) paradigm which was conceived to improve an e-health IoT environment. The scenario was characterized by supporting the care of people with special needs (elderly or with health problems) thus improving their quality of life. Thereby, the objective was to demonstrate the use of the proposed QoC evaluation, appraising some parameters. Experiments considered the use of diverse types of sensors, such as pulse and oxygen in the blood, body temperature, blood pressure, patient's position and falls, environment temperature and humidity. Results indicate the success of the proposal.

Mobility is a major problem in urban centers and vehicular networks have been a focus of study in the attempt to create applications focused on intelligent transport systems (ITS). With that in mind, ITS initiatives act as a possible solution to improve the functioning and performance of traffic systems, reducing congestion and increasing security for citizens. In this way, the present work presents a flexible and extensible platform called i9Vanets, whose objective is the virtualized management through a cloud vehicular network to assist in the solutions of the main challenges related to VANETs. An analysis of laboratory tests is also presented, with the objective of evaluating their performance and operational capacity. Thus, the conclusion of this work was to present the I9Vanet platform and its technical feasibility as well as its possible applications.

Software Defined Networking (SDN) is a new approach to computer networks that decouple the control from the data transmission function and is directly programmable through a high level programming language. In parallel Information Centric Network (ICN) influences the use of information through network caching, multipart communication and interaction of models, separating senders and recipients. Moreover, to due programmable features, SDN and ICN projects are architectures developed to flexibilize environments, mitigate traffic problems, deliver content through a scalable network structure, and yet avaiable a simple management. The premise of the SDN that contemplates the ICN besides decoupling, is the flexibility of the network configurations to reduce the overhead of the segments because of the retransmission of duplicate files by the same segment where they travel. Another important point is that contents provided do not give guarantees on the files integrity since they are routed through the same nodes several times pointing to biases. Based on this information, an architecture is designed to provide reliable content, that can be replicated in the network [16]. The proposed architecture aims to develop an ICN network, storing the information through a logical volume using private clouds, and that the stored content remains in a file system external to the network.

As a result of population growth in large cities face everyday problems related to urban mobility such as congestion, quality of urban roads and inefficiency of public transport. Intelligent transport systems initiatives act as an efficient solution to improve the functioning and performance of traffic systems, reducing congestion and increasing safety for citizens. However, due to the inclusion of different and distributed information sources on urban mobility, interoperability of the various technologies involved and the retention of these data are challenges that involve complex and costly efforts to governments and businesses. Thus, this article presents a proposal for georeferenced data retention architecture of Intelligent Transportation System in order to store this information georeferenced urban mobility in order to allow perform these activities more easily, and to promote interoperability between various applications. Therefore, the proposal was i9ITS architecture based on Service Oriented Architecture. It conducted a case study related to building an application that uses the i9ITS architecture for a taxi service company with real data. The use of this architecture proved to be effective and efficient to meet the proposed problem, as well as other possibilities to meet the demands and challenges related to Intelligent Transportation System.

The trend of grid computing available on the Internet has generated challenges to the allocation of resources provided by this type of environment. Once a grid computing is context sensitive system, so its possible to deal jointly with user's satisfaction and system performance. In this sense the quality of context can be used to process the context informations and provide several management decisions. So in this paper we propose a model to deal with user's satisfaction and system performance using quality of context. Experimental results shows improvements in the environment performance when applied our model in a grid scenario for the most execution tests.

The spread of projects focused on smart cities have grown in recent years. With this, the massive amount of data generated in these initiatives, creates a degree of complexity in how to manage all this information. In this paper we propose an infrastructure model for big data for a smart city projet. The goal of this model is to present the stages for the processing of data in the step of extraction, storage, processing and visualization, as well as the types of tools needed for each phase. To implement our proposed model, we used the Particip ACT Brazil a project based in smart cities. This project uses different databases to compose its big data and uses this data to seek solutions to urban problems. We observe that our model provides a structured vision of the software to be used in big data server of ParticipACT Brazil. In addition, we can also note that our model can be used in other big data servers.

Nowadays the studies of how to store massive amounts of data is a constant concern for the entire scientific community. In this paper is presented a data storage approach for DICOM medical image in distributed environments, using the distributed file systems CEPH, FhGFS, PVFS and Lustre versus a traditional RDBMS. The main contribution behind this work is open an important discussion about the research for better long-term data persistence methods, improving the management levels and still, bringing practical benefits to the medical systems. Beyond that, the experiments results have show that our approach using a distributed file system CEPH had the best performance to storage 1000 and 2500, reaching up to 907.49% better than a conventional approach using RDBMS.

This research work investigates the performance impact of virtualizing parallel file systems' (PFS) environments. Through an extensive experimental analysis, using three distinct computing platforms and considering 11 factors with relevant impact on systems' performance, we demonstrate how PFSs' write throughput is affected by network and disk contentions on virtualized environments. Results indicate that on extreme cases, in which the whole PFS environment executes over a single host, performance losses of up to 93% are observed when the number of I/O and computing nodes increases. On a PFS's environment deployed over Microsoft Azure cloud infrastructure, where it is expected a more balanced resource sharing, network contention proved more critical causing performance losses of up to 80% with the increase of the number of I/O nodes. Disk contention in this environment, however, was not an issue, behaving more like an environment deployed over a dedicated cluster.

The trend of grid computing available on the Internet has generated challenges to the allocation of resources provided by this type of environment. Many of these challenges can be solved by the quality of experience paradigm that takes into account several context parameters. In this context, this paper presents the proposal of a new quality of experience-driven model for resource allocation for grids named MAROQ. We detail an experimental evaluation using context information with MAROQ that presents improvements of 7.46% on the average execution time of tasks.

This work presents an information retrieval architecture developed for the Santa Catarina State Telemedicine System. This architecture employs DICOM Structured Reporting, controlled vocabularies for data catalogization and a specially developed search engine for data indexing and storing. Results of our case study show that searches can be performed much faster with the proposed search mechanism and that the precision of results is acceptable in most cases. In some searches, irrelevant items within the 15 first results were identified. This occurred partially because search terms found in the additional free text observations inserted into the findings reports were treated with the same relevance as formally diagnostically relevant items of the DICOM SR structure and, partially because the semantics of negations associated to search terms in the findings reports were not taken into consideration.

This paper presents a technological solution with the purpose of collaborative medical image visualization with support of decision in telemedicine environments. A fully web-based modular platform was developed which contains visualization, marking and manipulation synchronized between multiple users with communication established through video call or text chat. Pure HTML5 was used ensuring the portability of the tool, meaning that any device with a modern web browser and internet connection can access it. Finally, the platform usability is confirmed by the excellent score resulted from a System Usability Scale evaluation.

The Santa Catarina State Telemedicine and Telehealth System — STT/SC stores well over 2 million examinations and every month about 20 thousand new imaging exams are sent to the system database. As a significant part of the findings associated to these medical data are stored as text documents, the precise extraction of information is a difficult task. In the healthcare domain it is common to find different terms, some of them appearing as composed expressions, used to represent the same concept, whereas the existence of simple modifiers can turn an expression into another, different concept. This work presents an information retrieval architecture developed for the STT/SC. It consists in a module integrated into the STT/SC and was developed in order to support both experienced and inexperienced users to perform queries. Experiments were performed to evaluate the accuracy of the proposed system in real situations. Results show that the search can be performed more much faster and with acceptable precision.

This paper presents a research study consisting of a comparison analysis of replication and retrieval of medical image data which use the Digital Imaging and Communications in Medicine (DICOM) standard. These data are stored in a relational database management system (RDBMS) and in the Hierarchical Data Format (HDF) using a distributed file system as a data backend. The importance of this work was measured by verification of elapsed-time reduction for medical image data replication and retrieval in a real telemedicine environment.

Telemedicine systems currently have increased the volume of information stored in their databases. A centralized telemedicine system project covers datasets that ranges from personal patient's information, physicians and institutions to all images of examinations performed. As they store large amounts of examinations, the medical databases can reach several terabytes of volume. This paper presents a contribution characterized by an asynchronous replication model for medical distributed databases. The model, called postgresreplication, is an extension to the relational database postgreSQL. In our experiment, an engine has been created to manage all integration operations and information replication for the medical databases. The early results indicate that the model and its implementation have successfully reached a good performance level and interoperability.

This paper reports the development and design of the State of Santa Catarina's telemedicine network in Brazil. The resources concentration, like hospitals and clinical staff, in Brazilian large cities have been a problem in public healthcare policies improvement. Telemedicine technology for large scale telediagnostic, processing of routine outpatient examinations, the electronic delivery of examinations results integrated with the decision process of wetter to provide further treatment for a patient, is one strategy to overcome the difficulties imposed by healthcare concentration. The State needs to reduce costs like patient transportation, improve the quality of healthcare service and the origins control of these examinations, motivated the creation of this medical knowledge network. As a result it promoted better patient care, making faster diagnosis, creating a patient information history, reducing the examinations redundancy, maximizing this way, the social welfare and the health technological park on the Santa Catarina State.

Nowadays, there is a growing interest in telemedicine in Brazil, because the country is the fifth largest country in the world and there is a huge gap between the population size and the number of available doctors. Institutions and the government are considering solutions for the integration and availability of data produced by several different systems. In this paper we present a contribution that is characterized by an asynchronous replication strategy between distributed medical databases. The contribution, named PGR, is an enhancement to the PostgreSQL database software that introduces lightweight asynchronous operations. In addition, we have implemented a multi-master engine with partial replication and hybrid fragmentation in order to allow interoperability between different telemedicine systems. Our early results indicate that the model and its implementation have reached a successful level in terms of performance and interoperability.