The aim of this paper was to enhance the process of diagnosing and detecting possible vulnerabilities within an Internet of Things (IoT) system by using a named entity recognition (NER)-based solution. In both research and practice, security system management experts rely on a large variety of heterogeneous security data sources, which are usually available in the form of natural language. This is challenging as the process is very time consuming and it is difficult to stay up to date with the constant findings in the areas of security threats, vulnerabilities, attacks, countermeasures, and risks. The proposed system is conceived as a semantic indexing solution of existing vulnerabilities and serves as an information tool for security management experts. By integrating the proposed system, the users can easily discover the potential vulnerabilities of their IoT devices. The proposed solution integrates ontologies and NER techniques in order to obtain a high rate of automation with the scope of reaching a self-maintained and up-to-date system in terms of vulnerabilities and common exposures knowledge. To achieve this, a total of 312 CVEs (common vulnerabilities and exposures) specific to the IoT field were identified. CVEs are arguably one of the most important cybersecurity resources nowadays, containing information about the latest discovered vulnerabilities. This set is further used as data corpus for an NER model designed to identify the main entities and relations that are relevant to IoT security. The goal is to automatically monitor cybersecurity information relevant to IoT, and filter and present it in an organized and structured framework based on users’ needs. The taxonomies specific to IoT security are implemented via a domain ontology, which is later used to process natural language. Relevant tokens are marked as entities and the relations between them identified. The text analysis solution is connected to a gateway which scans the environment and identifies the main IoT devices and communication technologies. The strength of the approach proposed within this research is that the designed semantic gateway is using context-aware searches in the modeled IoT security database and can identify possible vulnerabilities before they can be exploited.

The COVID-19 outbreak is an international problem and has affected people and students all over the world. When lockdowns were imposed internationally, learning management systems began to be used more than in the previous period. These systems have been used also for traditional forms of learning and not only for online learning. This pandemic has highlighted the need for online learning systems in the educational environment, but it is very important for these systems to be secure and to verify the authenticity of the students when they access a course or evaluation questions. In this period, everything is moving towards the digital world, with students that are connected from a distance to online systems. All activities in the educational environment will soon be performed digitally on learning management systems, which includes also the evaluation process of the students. In this paper, we propose a secure learning management system that uses the student’s behavior to identify if they are an authentic student or not. This system can support the teacher’s activities in the learning process and verify the authenticity of the students logged on to the system. This paper is aimed at learning management system developers, who can use the proposed algorithms in their developed platforms, and also at teachers, who should understand the importance of the identification of students on these platforms.

Considering the current prediction from the Food and Agriculture Organization, food production needs an increase of over 70 percent by 2050, agriculture sector requires a boost that is also obtained by integrating novelty technologies and methods of artificial intelligence (AI) field. The current research explores the innovation lifecycle of AI-driven agriculture field through a causal inference analysis within University-Industry-Research ecosystem, extracting four key pillars: scientific research papers, research projects, patents and startups. Using an extensive time-series database, Granger causality analysis is applied for uncovering prospective causal relationships that guide the interest in innovation within AI-driven agriculture. Our results indicate an overall increase in AI applied agriculture domain within all four pillars starting from the 2010 year for start-ups pillar and impacting in Granger point of view all the way to 2020 for patents perspective. The findings suggest a potential sequential innovation pathway where progress in one pillar propels advancements in the next, according this flow: startups, projects, scientific research and patents. The implications of this study are significant, providing insights that could guide strategic planning and investment in AI applications in agriculture. By understanding the potential causality and sequentially in innovation, policymakers, investors, and entrepreneurs can better align their efforts with the most impactful areas. This research not only advances in academic knowledge but also provides practical insights that influence real-world applications and practices in agricultural technology.

Considering the current prediction from the Food and Agriculture Organization, food production needs an increase of over 70 percent by 2050, and the agriculture sector requires a boost that is also obtained by integrating novelty technologies and methods of the artificial intelligence (AI) field. This study investigates the innovation lifecycle of AI-driven agriculture through a causal inference analysis within the university–industry–research (UIR) collaboration framework. Using time series data from 1985 to 2023, collected from Web of Science (academic articles), CORDIS (research projects), WIPO (patents), and Crunchbase (start-ups), the study explores the causal dynamics among four key innovation pillars. Results from Granger causality and impulse-response analyses reveal a sequential innovation pathway in which start-up activity precedes research projects, followed by academic publications and patent filings. These findings highlight the interdependence of innovation stages and the evolving role of UIR collaboration in driving agricultural transformation. The study provides quantitative insights for policymakers, investors, and researchers to strategically foster innovation ecosystems in AI-driven agriculture.