Book recommendation to support professors and students in the identification of relevant sources is of significant importance for both universities and digital libraries and, hence, motivates the development of a recommendation system. This paper aims at automatically classifying a multiclass corpus that was created from ebooks from the Springer collection, which is available through the Hellenic Academic Libraries’ subscription, by utilizing an unsupervised neural network (NN) (self-organizing maps, SOM) and two deep neural network (DNN) architectures, namely, a long short-term memory (LSTM) and a convolutional neural network (CNN) combined with a LSTM(CNN+LSTM) under various configuration scenarios. The vector construction leverages information that was extracted from the table of contents (ToC) of each book using the TF-IDF weighting scheme (for the first case) and the Keras tokenizer (for the second). Extensive experiments were conducted using various configurations of preprocessing steps, NN set up and vector and vocabulary sizes to assess their impact on the classifier’s performance. Furthermore, we show that majority voting is more suitable for selecting the dominant label for a specified node. The experimental analysis showed the feasibility of developing a recommendation system for supporting professors and students in the identification of related sources based on a detailed thematic description (e.g., abstract or table of contents of a book) rather than a few keywords. In the conducted experiments, the subsystem that utilized the DNN (LSTM) performed the best, with F1-scores of 67% for the 26 categories and 80% for the 5 general categories, whereas SOM realizes F1-scores of less than 5% in both cases.

The advent of advanced mobile communication systems like 5G and beyond holds promise for vertical sectors, particularly in transport & logistics (T&L), by enhancing network performance and ensuring high levels of quality of service (QoS), crucial for automating and optimizing T&L processes. However, assessing the impact of 5G requires investments in network infrastructure, posing time, and return on investment uncertainties. To address this, the European project VITAL-5G offers an open, virtualized, and flexible experimentation platform that provides 3rd party vertical stakeholders the opportunity to test and validate 5G-enabled services under realistic conditions. This paper explores the VITAL-5G platform’s capabilities, detailing the infrastructure that supports 5G standalone (SA) testing across multiple trial sites, including port and logistics environments. We highlight a subset of performed activities and experimentation campaigns, technical and operational challenges faced by 3rd party experimenters, and the potential business opportunities that emerge from 5G-enhanced services in the T&L sector. Finally, we present insights into future developments and opportunities for extending these capabilities to other vertical sectors.

The proliferation of air travel demand necessitates innovative solutions to enhance passenger experience while optimizing airport operational efficiency. This paper presents the pilot-scale implementation and evaluation of an AI-powered service robot ecosystem integrated with thermal cameras and 5G wireless connectivity at Athens International Airport. The system addresses critical challenges in passenger flow management through real-time crowd analytics, congestion detection, and personalized robotic assistance. Eight strategically deployed thermal cameras monitor passenger movements across check-in areas, security zones, and departure entrances while employing privacy-by-design principles through thermal imaging technology that reduces personally identifiable information capture. A humanoid service robot, equipped with Robot Operating System navigation capabilities and natural language processing interfaces, provides real-time passenger assistance including flight information, wayfinding guidance, and congestion avoidance recommendations. The wi.move platform serves as the central intelligence hub, processing video streams through advanced computer vision algorithms to generate actionable insights including passenger count statistics, flow rate analysis, queue length monitoring, and anomaly detection. Formal trial evaluation conducted on 10 April 2025, with extended operational monitoring from April to June 2025, demonstrated strong technical performance with application round-trip latency achieving 42.9 milliseconds, perfect service reliability and availability ratings of one hundred percent, and comprehensive passenger satisfaction scores exceeding 4.3/5 across all evaluated dimensions. Results indicate promising potential for scalable deployment across major international airports, with identified requirements for sixth-generation network capabilities to support enhanced multi-robot coordination and advanced predictive analytics functionalities in future implementations.

This paper aims at bridging the gap between feature selection and feature space size by utilizing both square and non-square self-organizing maps under different configuration scenarios for classifying a multi-class multi-label corpus, the Reuters Mod Apte’ split. The selection of non-square maps is based on a heuristic process for finding the suitable size for the self-organizing map. Vector construction is based on a simple, yet effective procedure aiming at transforming the vectors from multi-label to uni-label. The proposed solution improves classification efficiency, not only in terms of accuracy but also in computational resources needed and time for training. Extensive experiments were conducted, using different configurations regarding map and vector sizes, training cycles, utilizing also context words, to assess their impact in the classifier’s performance. Furthermore, an intelligent algorithm for label selection is being proposed, aiming to show that the neighboring nodes on the map affect the selection of labels for a specific node. According to the experiments conducted, our approach achieves 10% increase in Macro-Average F1 scores, 30 times decrease in vector dimensionality and approximately 34 times smaller maps when compared to the baseline scenario.