The integration of drones in health informatics is transforming real-time health data collection and surveillance, particularly in high-risk construction environments. Construction workers are frequently exposed to hazardous conditions, including heat stress, airborne pollutants, and physical fatigue, necessitating continuous health monitoring to prevent accidents and long-term health complications. This paper explores the deployment of drone-based systems equipped with advanced sensors, including thermal imaging, gas detectors, and real-time physiological monitoring, to assess workers’ health conditions remotely. By leveraging AI-driven analytics and cloud-based data processing, these drones provide instant alerts for abnormal health indicators, enabling timely intervention. Unlike conventional monitoring methods that rely on periodic manual checks, drones offer a scalable and autonomous solution, enhancing both worker safety and operational efficiency. The study presents a framework for integrating drones with wearable health sensors, outlining their potential to revolutionize workplace health surveillance in construction fields. Key challenges, including data privacy, regulatory compliance, and system reliability, are also discussed, along with future directions for improving drone-assisted health monitoring.

Next-Generation Sequencing has revolutionized the study of genetic mutations, enabling large-scale investigations into their roles in disease development. However, extracting meaningful insights from the vast amount of biomedical literature remains a complex challenge that cannot be addressed manually. In this paper, we present pre-trained models (PTMs) for the automatic extraction of relations from biomedical text, specifically targeting the variant-phenotype domain. Our evaluation on the SNPPhenA corpus demonstrates that fine-tuning small BERT-based models, particularly DeBERTa, yields strong performance, approaching the current state-of-the-art (SOTA). Additionally, our results indicate that carefully fine-tuning Google’s Gemini Pro 1.0 outperforms the existing SOTA for both sentence-level tasks (where the model processes only the target sentence) and abstract-level tasks (where the model processes the entire abstract).

Emotion detection in conversational settings holds significant importance across various domains, such as customer service, mental health support, and virtual assistants. In this paper, we introduce the Emotion Contextual Fusion Network (ECFN), a novel model architecture designed to discern emotional nuances within conversations, leveraging Language-Agnostic Sentence Representations (LASER) [1], alongside categorical metadata indicating speakers and numerical sentiment scores characterizing emotional content. Through a deliberate focus on relationship capture, ECFN employs attention mechanisms to gather information from both immediate and historical context. We perform tests on three different task-specific datasets using both paid and free resources. Experimental results on standard datasets show that our model managed to match and even surpass state of art models, all while being trainable within a reasonable timeframe.

This paper aims to study a new innovative Unmanned aerial vehicle (UAV) designed for monitoring the health of the workers in the construction fields in Madrid, Spain. The paper is based upon Madrid community regulations and the European Commission regulations that point to “Safer and Healthier Work for All”, under Modernization of EU Occupational Safety and Health legislation and policy, through the Framework Directive 89/391/EEC. Previous research work was undertaken on the measurement of safety not a way for monitoring the workers’ health and safety. Through this paper, a new innovative helicopter named Nuntius is presented by Drone Hopper Company, to solve the problem of the ordinary tools used in the construction sites and monitoring the health care for the workers inside the construction field. The solution is based upon a comprehensive evaluation platform for sustainability in civil works. this new innovative helicopter is equipped with an onboard PC, and HD camera, and uses 5G Communication, with a total endurance of one hour. The results obtained show an accurate delivery for a real video stream, production, and processing of data in a real-time without any time delay.

This comprehensive review delves into the intricate realm of Soft Sets and their extensions, including the HyperSoft Set, IndetermSoft Set, IndetermHyperSoft Set, and TreeSoft Set, within the context of biomedical data analysis. Soft Sets serve as a foundational framework for managing the inherent uncertainty and imprecision inherent in biological data, thereby facilitating informed decision-making and knowledge discovery. The exploration of Soft Set Products, particularly in the context of multiple soft sets, underscores their pivotal role in advancing biomedical research. By extending these concepts to HyperSoft Sets, researchers can unlock deeper insights into complex biological phenomena, enabling more accurate predictions and classifications.

Since 2006, crowdsourcing has become a significant field that has been widely used. In biomedicine, crowdsourcing involves obtaining contributions or services from a significant community, often undefined, through an open call. In fact, crowdsourcing approaches have been used to engage the public, researchers, and clinicians in a range of activities related to research, drug discovery, patient care, and disease prevention. In emergency situations (e.g., CBRN threats) hospitals have been damaged or even destroyed. There is a need for mobile emergency medical facilities to take over the functions of those that are unusable. Even if that mobile hospitals on wheels cannot easily reach disaster-stricken regions, they are very useful. Naturally protected waterways, lakes, lagoons, etc. can be found in good time to accommodate mobile floating medical units for emergencies and for the medical care of human populations in isolated water environments. This article presents an original approach to using a patented, stabilized naval platform to carry a mobile medical unit for emergency or special needs in hard-to-reach water areas.

Today, with so much free information online, it becomes increasingly difficult to make sense of what content is based on fact, half-truths or lies. Furthermore, in accordance with the events (e.g., COVID-19) that are increasingly alarming, the speed of false news spread is unprecedented. Of course, fake news impairs social stability and public trust, which calls for increasing demand for their detection. How to spot as faithful as possible fake news? The response that this article gives, by exploring the textual features using artificial intelligence and machine learning. This research addresses the problem of automatic fake news detection for Romanian language. First, we present a new corpus for automatic fake news detection that contains two subsets of 977 and 29 154 news articles in Romanian, separated according to labelling and collection methods. Second, we explore several text-based approaches for automatic fake news detection by using machine learning and artificial intelligence which resulted in an accuracy of 93%.

Healthcare offers a rich palette of potential applications of RFID technology. Healthcare provides a rich palette of possible applications of RFID technology. Besides traditional uses such as tracking medical equipment and devices or access control, healthcare can benefit even more significantly from RFID technology. However, using the RFID technology in healthcare raises various problems of scalability, timely identification of tags, security, privacy, and efficient implementation in practice. That is because such systems contain many tags, operate with private personal data, and must respond promptly in concrete, practical situations to avoid malfunctions (errors in the decision process, traffic congestion, and so on). This paper discusses the fundamental requirements of RFID systems raised by healthcare and the limitations of existing schemes. Then, we propose a new RFID scheme that achieves mutual authentication, strong privacy, and constant-time identification in the HPVP model. The scheme employs a secure symmetric-key encryption scheme, making it very efficient in implementation and physically unclonable functions (PUFs) to protect the secret key against adversaries with corruption capabilities.

Generating fake news has become a prevalent issue in the digital age, with profound consequences on society, politics, and media. Due to an exponential increase in the use of unverified information, spread on social media by users with different backgrounds, a significant task facing natural language processing is real-time recognition of generated fake news. The objective of this paper is to investigate the feasibility of utilizing artificial intelligence (AI) for the automatic generation of fake news that possesses a sufficiently high level of quality to serve as a synthetic corpus. These automatically generated texts are subsequently used to improve fake news detectors, evaluated on different publicly available datasets fake news datasets, but also against a novel fake news corpus, especially developed for this research.

As revolutionary technology, scanning technology - like OCR - knows an attracting increasing interest in the medical system. In fact, scanning technology, driven by big data and machine learning, helps to drive successful change processes in healthcare. Nowadays, the OCR systems based on the promise of artificial intelligence can contribute to a greater understanding of entire processes by automating the medical transcription and in general mining information (largely handwritten) clinical notes. The project aims to create an OCR application for offline character recognition using deep learning (a combination of RNN and CNN) that can be used in the medical sector. Concerning problems of medical care, large data such as dispersed dataset, weak consistency, low electronic degree, and low visualization degree, a set of innovative image recognition, dataset, arrangement, and storage projects were put forward. The proposed pilot method achieves results comparable to current SoTA.

Atrial fibrillation is the most common sustained heart rhythm abnormality in clinical practice that can lead to well-known medical complications associated with increased mortality. The diagnosis of atrial fibrillation can be detected by using a short electrocardiogram recording (ECG). However, because the heartbeat is irregular and does not constantly present on a simple electrocardiogram, a single diagram is not enough for a final and certain diagnosis. To monitorizing one single patient requires hours of monitoring, important costs, and low yield. Our aim is to develop a rapid, inexpensive way to identify patients with atrial fibrillation using neural networks. This study serves to help the clinician with an automatic approach to give a quick and safe diagnosis for each patient population. The experiments show that this approach offers a promising atrial fibrillation classification and outperforms recently published studies that either use extracted features or raw data separately.

Due to an exponential increase in the use of Internet by persons from different countries and educational backgrounds, the offensive online language detection has become a significant task facing natural language processing. Considering the major negative impact of this type of content in the case of youngers, detecting online toxic language to protect users’ online safety becomes an urgent issue. The project has two main goals: (1) developing an annotated corpus of offensive content for Romanian language and (2) testing various machine learning algorithms to identify a best approach. The proposed methods achieve results with a few percentages more than the accuracy of the current SoTA.

Due to the rapid development of artificial intelligence (AI) technologies, the medical system knows a permanent improvement in the doctor-patient relationship that has undergone a long and unproductive transition throughout the ages. Nowadays, the smart systems focused on how to obtain an accurate and detailed medication history for each patient. The purpose of this paper was to develop a method that can produce a disease profile based on the decision trees algorithm, taking into account medical reports made along years for different patients from all over the world. It is well known that in the medical decision making there are many situations where decision must be made rapidly, rigorously and efficiently. In the paper we present the basic characteristics of decision trees and the successful alternatives to the traditional induction approach with the emphasis on existing and possible future applications in biomedicine domain.

Nowadays, the interest in data modelling from the spatial-temporal perspective is constantly increasing. Moreover, a wide variety of applications, such as social network data, need to be done to study spatiotemporal patterns. In general, however, these patterns are highly complex and challenging, so it is a demanding process to analyze or to classify them as the conventional context in various types of event data. In order to analyze the traffic viral within the text from the perspective of impressive negative effects, we should spatial-temporally localize the event and geographical regions and give a semantically interpreting of what happened. We propose a review of the best models and techniques applied for social media data processing to formalize a novel theory of action and time. This investigation intends to draw the basic knowledge level over which research intended to decipher in texts the occurrence of events, together with their involved characters, and their relationship with time and space.

Augmented Reality (AR) has been accepted as an effective learning method which means that it becomes complementary to traditional learning, especially in chemistry. In fact, AR is an interactive experience of a real-world environment. Before recent releases of cheap and affordable smart devices, AR large-scale applications in education were almost impossible. After a brief analysis of current trends in the use of AR, we propose a new system, named ARChemistry Learning, to support the Romanian educational system. In this study, we propose a modern AR tool in the chemistry education used to support children or anyone who wants to learn chemistry, to develop logic, and to explore the world seen only on a smart device. The purpose of this research is to demonstrate how effective these AR applications are, even that in Romania they are still in a pioneering phase.

Nowadays, social media credibility is a pressing issue for each of us who are living in an altered online landscape. The speed of news diffusion is striking. Given the popularity of social networks, more and more users began posting pictures, information, and news about personal life. At the same time, they started to use all this information to get informed about what their friends do or what is happening in the world, many of them arousing much suspicion. The problem we are currently experiencing is that we do not currently have an automatic method of figuring out in real-time which news or which users are credible and which are not, what is false or what is true on the Internet. The goal of this is to analyze Twitter in real-time using neural networks in order to provide us key elements about both the credibility of tweets and users who posted them. Thus, we make a real-time heatmap using information gathered from users to create overall images of the areas from which this fake news comes.

One of the industry’s most important trends in enterprise architecture is related to the use of microservices, to the detriment of monolithic architectures, which are beginning to no longer be used. Due the cloud-native architectures the deployment of microservices systems is more productive, flexible and cost effective. Anyway, a lot of companies started to migrate from one type of architecture to another, but it is still in the early phase. In this paper we address the challenges raised by the need to develop a scalable and fault tolerance system based on microservices. In our experiments we consider two types of microservices, simple and extended and the proposed solution proves to be an innovative one especially based on its dynamic behavior.

This study introduces the concept of uncertainty analysis of Neutrosophic Theory in the sphere of Maintenance Operating System (MOS). The aim of this study is to underline the importance of uncertainties solving in Maintenance Operating System. In maintenance process appear ambiguous states that can't be assimilated neither true, nor false, meaning that the threshold state is a neutral one, being defined as fault in most of cases. In this regard, this uncertaintymaking decision process can be associated as functioning according to rules of Neutrosophy, and can be evaluated using elements of Neutrosophic Structures, Pareto Charts, maintenance metrics. Identification of uncertainties and study of their impact on maintenance, making the right decision, is the main focus of this paper. The study is useful for business area, especially manufacturing lines endowed with complex equipment and facilities, also for researchers interested to make improvements for maintenance procedures.

This paper presents a diachronic analysis centered on the exploration of differences between the writing styles of journalistic texts in Romanian language. This analysis is focused on the time evolution of this language across two adjacent regions, Bessarabia and Romania in two major periods that were marked by important historical differences. Our aim is to examine these language differences based on corpora of historical and contemporary texts. To this end, we employ the ReaderBench framework to calculate a number of textual complexity indices that can be reliably used to characterize writing style. These analyses are conducted on two independent corpora for each of the two language styles, covering the following time periods: 1941-1991, when Bessarabia was separated from Romania and became a state in the Soviet Union (and there were few connections and language influences with Romania), and after July 1991, when Bessarabia became an independent state, Republic of Moldavia (and many language interactions with Romania occurred). The results of our analyses highlight the lexical and cohesive textual complexity indices that best reflect the differences in writing style, ranging from sentence and paragraph structure to word entropy and cohesion, measured in terms of Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA).