Introduction: Machine learning is increasingly utilized over recent years in order to develop models that represent and solve problems in a variety of domains, including those of obstetrics and midwifery. The aim of this systematic review was to analyze research studies on machine learning and...

The extensive pool of content within educational software platforms can often overwhelm learners, leaving them uncertain about what materials to engage with. In this context, recommender systems offer significant support by customizing the content delivered to learners, alleviating the confusion and enhancing the learning experience. To this end, this paper presents a novel approach for recommending adequate educational content to learners via the use of knowledge graphs. In our approach, the knowledge graph encompasses learners, educational entities, and relationships among them, creating an interconnected framework that drives personalized e-learning content recommendations. Moreover, the presented knowledge graph has been enriched with contextual signals referring to various learners’ characteristics, such as prior knowledge level, learning style, and current learning goals. To refine the recommendation process, the cosine similarity technique was employed to quantify the likeness between a learner’s preferences and the attributes of educational entities within the knowledge graph. The above methodology was incorporated in an intelligent tutoring system for learning the programming language Java to recommend content to learners. The software was evaluated with highly promising results.

The need to review maritime education has been highlighted in the relevant literature. Maritime curricula should incorporate recent technological advances, as well as address the needs of the maritime sector. In this paper, the Fuzzy Delphi Method (FDM) and the Fuzzy Analytic Hierarchy Process (FAHP) are utilized in order to propose a fuzzy multicriteria decision-making (MCDM) methodology that can be used to assess the importance of new technologies in maritime education and design a fuzzy evaluation model that can assist in maritime education policy-making. This study integrates the perspectives of the main maritime education stakeholders, namely, lecturers and maritime sector management. We selected data from a group of 19 experienced maritime professors and maritime business managers. The results indicate that new technologies such as artificial intelligence (AI), augmented and virtual reality (AR/VR), the Internet of Things (IoT), digital twins (DTs), and cybersecurity, as well as eLearning platforms, constitute a set of requirements that maritime education policies should meet by designing their curricula appropriately. This study suggests that fuzzy logic MCDM methods can be used as a human-centered AI approach for developing explainable education policy-making models that integrate stakeholder requirements and capture the subjectivity that is often inherited in their perspectives.

Fuzzy logic can be used to model and manipulate imprecise and subjective knowledge imitating the human reasoning. The aim of this systematic review was to analyze research studies pertaining to fuzzy logic and fuzzy intelligent systems applications in midwifery and obstetrics. A thorough literature review was performed in four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect). Only the papers that discussed fuzzy logic and fuzzy intelligent systems applications in midwifery and obstetrics were considered in this review. Selected papers were critically evaluated as for their relevance and a contextual synthesis was conducted. Twentynine papers were included in this systematic review as they met the inclusion and methodological criteria specified in this study. The results suggest that fuzzy logic and fuzzy intelligent systems have been successfully applied in midwifery and obstetrics topics, such as diagnosis, pregnancy risk assessment, fetal monitoring, bladder tumor, etc. This systematic review suggests that fuzzy logic is applicable to midwifery and obstetrics domains providing the means for developing affective intelligent systems that can assist human experts in dealing with complex diagnosis and problem solving. However, its full potential is not yet been examined, thus presenting an opportunity for further research.

Preterm births account for almost 1 million deaths globally. The objective of this study is to develop and evaluate a model that assists clinicians in assessing the risk of preterm birth, using fuzzy multicriteria analysis. The model allows experts to incorporate their intuition and judgment into the decision-making process and takes into consideration six (6) risk dimensions reflecting the socio-economic, behavioural and medical profile of pregnant women, thus adopting a holistic approach to risk assessment. Each risk dimension is further analysed and measured in terms of risk factors associated with it. Data was collected from a selected group of 35 experts, each one with more than 20 years of obstetric experience. The model criteria were selected after a thorough literature analysis, so as to ensure a holistic approach to risk assessment. The criteria were reviewed by the experts and the model structure was finalised. The fuzzy analytic hierarchy method was applied to calculate the relative importance of each criterion and subsequent use of the model in assessing and ranking pregnant women by their preterm risk. The proposed model utilises fuzzy logic and multicriteria analysis. It addresses the multifactorial nature of decision making when assessing the preterm birth risk. It also incorporates the obstetricians’ intuitive judgement during risk assessment and it can be used to classify cases based upon their risk level. Additionally, it can be applied to evaluate the risk of individual cases in a personalised manner. The proposed model is compared and validated for its predictive value against judgments made by experts.