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 increasing volatility in energy markets, particularly in Greece where electricity costs reached a peak of 236 EUR/MWh in 2022, underscores the urgent need for accurate short-term load forecasting models. In this study, the application of deep learning techniques, specifically Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), to forecast hourly electricity demand is investigated. The proposed models were trained on historical load data from the Greek power system spanning the years 2013 to 2016. Various deep learning architectures were implemented and their forecasting performances using statistical metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) were evaluated. The experiments utilized multiple time horizons (1 h, 2 h, 24 h) and input sequence lengths (6 h to 168 h) to assess model accuracy and robustness. The best performing GRU model achieved an RMSE of 83.2 MWh and a MAPE of 1.17% for 1 h ahead forecasting, outperforming both LSTM and RNN in terms of both accuracy and computational efficiency. The predicted values were integrated into a dynamic Power BI dashboard, to enable real-time visualization and decision support. These findings demonstrate the potential of deep learning architectures, particularly GRUs, for operational load forecasting and their applicability to intelligent energy systems in a market-strained environment.

Assessing the usefulness of reviews has been the aim of several research studies. However, results regarding the significance of usefulness determinants are often contradictory, thus decreasing the accuracy of reviews’ helpfulness estimation. Also, bias in user reviews attributed to differences, e.g., in gender, nationality, etc., may result in misleading judgments, thus diminishing reviews’ usefulness. Research is needed for sentiment analysis algorithms that incorporate bias embedded in reviews, thus improving their usefulness, readability, credibility, etc. This study utilizes fuzzy relations and fuzzy synthetic evaluation (FSE) in order to calculate reviews’ usefulness by incorporating users’ biases as expressed in terms of reviews’ articulacy and sentiment polarity. It selected and analyzed 95,678 hotel user reviews from Tripadvisor, written by users from five specific nationalities. The findings indicate that there are differences among nationalities in terms of the articulacy and sentiment of their reviews. The British are most consistent in their judgments expressed in titles and the main body of reviews. For the British and the Greeks, review titles suffice to convey any negative sentiments. The Dutch use fewer words in their reviews than the other nationalities. This study suggests that fuzzy logic captures subjectivity which is often found in reviews, and it can be used to quantify users’ behavioral differences, calculate reviews’ usefulness, and provide the means for developing more accurate voting systems.

The reviews usefulness has been the aim of several research studies. However, results regarding the significance of usefulness determinants are often contradicting, thus decreasing the accuracy of reviews’ helpfulness estimation. Also, bias in user reviews attributed to differences e.g. in gender, nationality, etc., may result into misleading judgments thus diminishing reviews’ usefulness. Research is needed for sentiment analysis algorithms that incorporate bias embedded in reviews, thus improving their usefulness, readability, credibility, etc. This study utilizes fuzzy relations and fuzzy synthetic evaluation (FSE) in order to calculate reviews’ usefulness by incorporating users’ biases as expressed in terms of reviews’ articulacy and sentiment polarity. It selected and analysed 95.678 hotel user reviews from Tripadvisor, for five nationalities. The findings indicate that there are differences among nationalities. The British are most consistent in their judgments expressed in titles and review documents. The British and the Greek review titles suffice to convey any negative sentiments. The Dutch use fewer words in their reviews than the other nationalities. This study suggests that fuzzy logic captures subjectivity which is often found in reviews, and it can be used to quantify users’ behavioral differences, calculate reviews usefulness, and provide the means for developing more accurate voting systems.

Long Short-Term Memory and Gated Recurrent Unit-based networks are examined for short-term load forecasting for the Greek power system, where predictions of the next hour's load are made for the whole year. The recurrent property of the forecasters has the advantage to allow the use of a reduced input set since no prior knowledge of the most suitable previous loads is required for the model to identify the temporal relations that exist in the load time-series. An extensive simulation analysis is conducted, to evaluate the forecasters' performance and to highlight their particular learning characteristics. Both deep learning-based forecasters perform very efficiently, with LSTM achieving an average percentage error of 1.18% and a root mean squared error of 83.8 MW, while GRU performs similarly with respective values of 1.17% and 83.4 MW.