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The purpose of this paper is to present an algorithm for detecting communities in social networks.

The majority of existing methods of community detection in social networks are based on structural information, and they neglect the content information. In this paper, the authors propose a novel approach that combines the content and structure information to discover more meaningful communities in social networks. To integrate the content information in the process of community detection, the authors propose to exploit the texts involved in social networks to identify the users’ topics of interest. These topics are detected based on the statistical and semantic measures, which allow us to divide the users into different groups so that each group represents a distinct topic. Then, the authors perform links analysis in each group to discover the users who are highly interconnected (communities).

To validate the performance of the approach, the authors carried out a set of experiments on four real life data sets, and they compared their method with classical methods that ignore the content information.

The experimental results demonstrate that the quality of community structure is improved when we take into account the content and structure information during the procedure of community detection.

The aim of this paper is to present an online framework for building a domain taxonomy, called TaxoLine, from Web documents automatically.

TaxoLine proposes an innovative methodology that combines frequency and conditional mutual information to improve the quality of the domain taxonomy. The system also includes a set of mechanisms that improve the execution time needed to build the ontology.

The performance of the TaxoLine framework was applied to nine different financial corpora. The generated taxonomies are evaluated against a gold-standard ontology and are compared to state-of-the-art ontology learning methods.

The experimental results show that TaxoLine produces high precision and recall for both concept and relation extraction than well-known ontology learning algorithms. Furthermore, it also shows promising results in terms of execution time needed to build the domain taxonomy.

This work provides an overview of academic articles on the application of artificial intelligence (AI) in healthcare. It delves into the innovation process, encompassing a two-stage trajectory of exploration and development followed by dissemination and adoption. To illuminate the transition from the first to the second stage, we use prospect theory (PT) to offer insights into the effects of risk and uncertainty on individual decision-making, which potentially lead to partially irrational choices. The primary objective is to discern whether clinical decision support systems (CDSSs) can serve as effective means of “cognitive debiasing”, thus countering the perceived risks.

This study presents a comprehensive systematic literature review (SLR) of the adoption of clinical decision support systems (CDSSs) in healthcare. We selected English articles dated 2013–2023 from Scopus, Web of Science and PubMed, found using keywords such as “Artificial Intelligence,” “Healthcare” and “CDSS.” A bibliometric analysis was conducted to evaluate literature productivity and its impact on this topic.

Of 322 articles, 113 met the eligibility criteria. These pointed to a widespread reluctance among physicians to adopt AI systems, primarily due to trust-related issues. Although our systematic literature review underscores the positive effects of AI in healthcare, it barely addresses the associated risks.

This study has certain limitations, including potential concerns regarding generalizability, biases in the literature review and reliance on theoretical frameworks that lack empirical evidence.

The uniqueness of this study lies in its examination of healthcare professionals’ perceptions of the risks associated with implementing AI systems. Moreover, it addresses liability issues involving a range of stakeholders, including algorithm developers, Internet of Things (IoT) manufacturers, communication systems and cybersecurity providers.