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The evolving role of artificial intelligence (AI) in healthcare can shift the route of automated, supervised and computer-aided diagnostic radiology. An extensive literature review was conducted to consider the potential of designing a fully automated, complete diagnostic platform capable of integrating the current medical imaging technologies. Adjuvant, targeted, non-systematic research was regarded as necessary, especially to the end-user medical expert, for the completeness, understanding and terminological clarity of this discussion article that focuses on giving a representative and inclusive idea of the evolutional strides that have taken place, not including an AI architecture technical evaluation. Recent developments in AI applications for assessing various organ systems, as well as enhancing oncology and histopathology, show significant impact on medical practice. Published research outcomes of AI picture segmentation and classification algorithms exhibit promising accuracy, sensitivity and specificity. Progress in this field has led to the introduction of the concept of explainable AI, which ensures transparency of deep learning architectures, enabling human involvement in clinical decision making, especially in critical healthcare scenarios. Structure and language standardization of medical reports, along with interdisciplinary collaboration between medical and technical experts, are crucial for research coordination. Patient personal data should always be handled with confidentiality and dignity, while ensuring legality in the attribution of responsibility, particularly in view of machines lacking empathy and self-awareness. The results of our literature research demonstrate the strong potential of utilizing AI architectures, mainly convolutional neural networks, in medical imaging diagnostics, even though a complete automated diagnostic platform, enabling full body scanning, has not yet been presented. [ABSTRACT FROM AUTHOR]

This paper introduces a new routing and touring service both for outdoor and indoor places of touristic and cultural interest designed to be used in the wider area of Attica, Greece. This service is the result of the work performed in OPTORER (OPTORER: OPtimal rouTing and explOration of touRistic and cultural arEas of interest within Attica given personalized adaptive preferences, promoted underlying purpose, and interactive experience), project, and it aspires to offer a range of innovative and thematic routes to several specified points of interest in the selected area of Attica, encouraging the combination of indoor and outdoor routes in a single tour. The aim is to optimize the user experience while promoting specific, user-centric features, with safety and social welfare being a priority for every designed tour, resulting in enhancing the touristic experience in the area. Using a common smartphone device, as well as common wearable devices (i.e., smartwatches), the OPTORER service will provide an end-to-end solution by developing the algorithms and end-user applications, together with an orchestration platform responsible for managing, operating, and executing the service that produces and presents to the end user results derived from solving dynamically complex optimization problems. [ABSTRACT FROM AUTHOR]

This paper presents a proposal for extending an existing terabit-class backbone network architecture to enable the use of LiFi technology by power utilities and smart cities. The proposed architecture provides a practical means of integrating three smart grid communication technologies—fiber optics, BPL networks and LiFi LED street lighting networks—across the transmission and distribution power grids of smart cities. In addition to expanding the backbone communications network architecture, the paper provides a comprehensive overview of LiFi technology and analyzes the concept of LiFi LED street lighting networks in a smart city. The analytical investigation of the operation and performance of LiFi LED street lighting networks focuses on the following aspects: (i) typical LED street lighting configurations and default configuration parameter values encountered in smart sustainable cities; (ii) the applied LiFi channel model and corresponding default model parameters; (iii) SNR computations and LiFi channel classifications for a variety of scenarios; and (iv) available LiFi LED street lighting network architectures for integrating LiFi LED street lighting networks with the backbone network. The paper also discusses the potential benefits of LiFi LED street lighting networks for power utilities, smart cities and individuals. [ABSTRACT FROM AUTHOR]

The advancement and acceptance of new technologies often hinges on the level of understanding and trust among potential users. Blockchain technology, despite its broad applications across diverse sectors, is often met with skepticism due to a general lack of understanding and incidents of illicit activities in the cryptocurrency domain. This study aims to demystify blockchain technology by providing an in-depth examination of its application in a novel blockchain-based card game, centered around renewable energy and sustainable resource management. This paper introduces a serious game that uses blockchain to enhance user interaction, ownership, and gameplay, demonstrating the technology's potential to revolutionize the gaming industry. Notable aspects of the game, such as ownership of virtual assets, transparent transaction histories, trustless game mechanics, user-driven content creation, gasless transactions, and mechanisms for in-game asset trading and cross-platform asset reuse are analyzed. The paper discusses how these features, not only provide a richer gaming experience but also serve as effective tools for raising awareness about sustainable energy and resource management, thereby bridging the gap between entertainment and education. The case study offers valuable insights into how blockchain can create dynamic, secure, and participatory virtual environments, shifting the paradigm of traditional online gaming. [ABSTRACT FROM AUTHOR]

Modern supply chains comprise an increasing number of actors which deploy different information technology systems that capture information of a diverse nature and diverse sources (from sensors to order information). While the benefits of the automatic exchange of information between these systems have been recognized and have led to their interconnection, protecting the whole supply chain from potential attacks is a challenging issue given the attack proliferation reported in the literature. In this paper, we present the FISHY platform, which anticipates protecting the whole supply chain from potential attacks by (a) adopting novel technologies and approaches including machine learning-based tools to detect security threats and recommend mitigation policies and (b) employing blockchain-based tools to provide evidence of the captured events and suggested policies. This platform is also easily expandable to protect against additional attacks in the future. We experiment with this platform in the farm-to-fork supply chain to prove its operation and capabilities. The results show that the FISHY platform can effectively be used to protect the supply chain and offers high flexibility to its users. [ABSTRACT FROM AUTHOR]

The satisfaction of the Quality of Service (QoS) levels during an entire service life‐cycle is one of the key targets for Service Providers (SP). To achieve this in an optimal way, it is required to predict the exact amount of the needed physical and virtual resources, for example, CPU and memory usage, for any possible combination of parameters that affect the system workload, such as number of users, duration of each request, etc. To solve this problem, the authors introduce a novel architecture and its open‐source implementation that a) monitors and collects data from heterogeneous resources, b) uses them to train machine learning models and c) tailors them to each particular service type. The candidate solution is validated in two real‐life services showing very good accuracy in predicting the required resources for a large number of operational configurations where a data augmentation method is also applied to further decrease the estimation error up to 32%. [ABSTRACT FROM AUTHOR]

In this paper, four machine learning algorithms are examined regarding their effectiveness in dealing with a complete lack of sensor drift values for a crucial parameter for ship performance evaluation, such as a ship's speed through water (STW). A basic Linear Regression algorithm, a more sophisticated ensemble model (Random Forest) and two modern Recurrent Neural Networks i.e., Long Short-Term Memory (LSTM) and Neural Basis Expansion Analysis for Time Series (N-Beats) are evaluated. A computational algorithm written in python language with the use of the Darts library was developed for this scope. The results regarding the selected parameter (STW) are provided on a real- or near-to-real-time basis. The algorithms were able to estimate the speed through water in a progressive manner, with no initial values needed, making it possible to replace the complete missingness of the label data. A physical model developed with the simulation platform of Siemens Simcenter Amesim is used to calculate the ship STW under the real operating conditions of a banker ship type during a period of six months. These theoretically obtained values are used as reference values ("ground-truth" values) to evaluate the performance of each of the four machine learning algorithms examined. [ABSTRACT FROM AUTHOR]

Undoubtedly, we are witnessing a new era of computer networks that aspire to support modern demanding applications by providing the highest Quality of Experience (QoE) to the end user. Next Generations Networks (NGNs) ensure that characteristics such as ultra-low latency, high availability and wide service coverage can be met across the network regardless of the network infrastructure ownership. To accomplish that, beyond the necessary improvements in the radio propagation field, changes have been made in the core network functions which are now characterized as programmable, and software defined. Software Defined Networks (SDNs) and Network Function Virtualization (NFV) are the keystones of the NGNs flexibility. The high expectations of NGNs' performance and the continuous changes in the network conditions lead to the development of new network management frameworks that add elasticity and dynamicity and minimize human intervention. ETSI (the European Standards Organization) presents the Zero-touch Service Management (ZSM) framework that uses hyped technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to achieve full end-to-end automation of the network services' management across one or many different domains. Focusing on multi-domain network service management, there are several security issues identified by the standardization team which mostly derive from the lack of trust among network providers. In the present research, we explore the suitability of blockchain technology adoption for facing these security issues. Blockchain technology inherently addresses security in trustless environments such as the infrastructures defined by the ZSM team. Our contribution is three-fold: (a) we define the architecture of a multi-domain network infrastructure that adopts the ZSM approach and integrates blockchain functionality, (b) we explore the adoption of different blockchain and distributed ledger technologies (DLT) approaches to address ZSM security needs and (c) we provide guidelines to prospective solution designers/implementers on the detailed requirements that this solution has to meet to maximize the offered value. [ABSTRACT FROM AUTHOR]

In present times, the largest amount of data is being controlled in a centralized manner. However, as the data are in essence the fuel of any application and service, there is a need to make the data more findable and accessible. Another problem with the data being centralized is the limited storage as well as the uncertainty of their authenticity. In the Internet of Things (IoT) sector specifically, data are the key to develop the most powerful and reliable applications. For these reasons, there is a rise on works that present decentralized marketplaces for IoT data with many of them exploiting blockchain technology to offer security advantages. The main contribution of this work is to review the existing works on decentralized IoT data marketplaces and discuss important design aspects and options so as to guide (a) the prospective user to select the IoT data marketplace that matches their needs and (b) the potential designer of a new marketplace to make insightful decisions. [ABSTRACT FROM AUTHOR]

In the extremely competitive environment of shipping, minimizing shipping cost is the key factor for the survival and growth of shipping companies. However, stricter rules and regulations that aim at the reduction of greenhouse gas emissions published by the International Maritime Organization, force shipping companies to increase the operational efficiency of their fleet. The prediction of a ship speed in actual seas with a given power by its engine is the most important performance indicator and thus makes it the "holy grail" in pursuing better efficiency. Traditionally, tank model tests and semi-empirical formulas were the preferred solution for the aforementioned prediction and are still widely applied. However, currently, with the increased computational power that is widely available, novel and more sophisticated methods taking into consideration computational fluid dynamics (CFD) and machine learning (ML) algorithms are emerging. In this paper, we briefly present the different approaches in the prediction of a ship's speed but focus on ML methods comparing a representative number of the latest data-driven models used in papers, to provide guidelines, discover trends and identify the challenges to be faced by researchers. From this comparison, we can distinguish that artificial neural networks (ANN), being used in 73.3% of the reviewed papers, dominate as the algorithm of choice. Researchers mostly rely on physical laws governing the phenomena in the crucial part of data preprocessing tasks. Lastly, most researchers rely on data acquisition systems installed at ships in order to achieve usable results. [ABSTRACT FROM AUTHOR]

The needs for education, learning and training proliferate primarily due to the facts that economy becomes more and more knowledge based (mandating continuous lifelong learning) and people migrate among countries, which introduces the need for learning other languages, for training on different skills and learning about the new cultural and societal framework. Given that in parallel, time schedules continuously become tighter, learning through mobile devices continuously gains in popularity as it allows for learning anytime, anywhere. To increase the learning efficiency, personalisation (in terms of selecting the learning content, type and presentation) and adaptation of the learning experience in real time based on the experienced affect state are key instruments. All these user requirements challenge the current network architectures and technologies. In this paper, we investigate the requirements implied by efficient mobile learning scenarios and we explore how 5G technologies currently under design/testing/validation and standardisation meet these requirements.

Residential energy consumption reserves a significant portion of the total energy consumption in modern cities. The rates of construction of new buildings as well as the rates of renovation on existing ones are generally very low. At the same time, unlike centrally operated large commercial buildings, the installation of energy management systems is a rather expensive solution leaving residential users with limited means to improve their energy efficiency as results are not evident. Considering that to drive energy efficient behaviour, we have to first raise awareness, then provide evidence through measurements and then support further, more elaborate, energy efficiency actions, we capitalise on ICT as soft measures towards reaching hard goals. We propose a novel incremental solution starting from a rather simple mobile application exploiting the sensors available in our smartphones and tablets to proceed to more intelligent advice provisioning towards energy efficiency. We present its implementation architecture and discuss certain market-wise challenges to prove its potential.

Free space optical (FSO) communications links is a promising solution for the provision of high data rate point to point communications. In particular deploying FSO technology for mobile links between Unmanned Aerial Vehicles (UAVs) and fixed Ground Stations (GS) introduces several interesting challenges. In this paper, we investigate the ability of a mobile FSO system to operate in different atmospheric conditions. Specifically, we characterize the quality of the optical channel with a proper model in terms of Bit Error Rate (BER) and average Signal to Noise Ratio (SNR) and we report a detailed optical amplification model able to support a constant Quality of Service for different distances from 1 km up to 35 km at 10 Gbps with 1550 nm wavelength. An extensive comparative analysis among different FSO configurations links considering the altitude of the UAV, the wavelength and the atmospheric conditions is provided. The results show that there is degradation at the BER over a slanted path compared to a horizontal path at the same conditions.

Wastes contain large quantities of energy that can be extracted using a variety of methods. European Union has established a waste‐to‐energy initiative to minimize waste and increase recovery. Τhe EU Landfill Directive promotes more environmental friendly waste management options. Accordingly, EU member states are adopting mechanical‐biological treatment processes, bio‐methanization and waste‐derived fuels. Furthermore, EU is energy‐dependent, as its resources are insufficient to meet rising energy demands, and fossil fuels must be imported in large quantities. Wastes are increasingly produced and have a non‐negligible lower calorific value that are considered as a power source. Findings highlight the role of waste‐to‐energy facilities in meeting a portion of the European Union's energy needs and contributing to the achievement of renewable energy targets. The untapped potential, through specific actions, can increase capacity in EU without threatening the goals for recycling. This work presents the current status of energy recovery in EU and investigates the unutilized potential energy recovery from wastes, considering the currently landfilled quantities. The results showed that even in the worst scenario, the energy recovery and the renewable content are too high to be neglected, supporting the decarbonization efforts and enhance circular economy, in line with EU's Energy Strategy and Paris agreement. [ABSTRACT FROM AUTHOR]

The optimum utilization of infrastructural resources is a highly desired yet cumbersome task for service providers to achieve. This is because the optimal amount of such resources is a function of various parameters, such as the desired/agreed quality of service (QoS), the service characteristics/profile, workload and service life-cycle. The advent of frameworks that foresee the dynamic establishment and placement of service and network functions further contributes to a decrease in the effectiveness of traditional resource allocation methods. In this work, we address this problem by developing a mechanism which first performs service profiling and then a prediction of the resources that would lead to the desired QoS for each newly deployed service. The main elements of our approach are as follows: (a) the collection of data from all three layers of the deployed infrastructure (hardware, virtual and service), instead of a single layer of the deployed infrastructure, to provide a clearer picture on the potential system break points, (b) the study of well-known container based implementations following that microservice paradigm and (c) the use of a data analysis routine that employs a set of machine learning algorithms and performs accurate predictions of the required resources for any future service requests. We investigate the performance of the proposed framework using our open-source implementation to examine the case of a Hadoop cluster. The results show that running a small number of tests is adequate to assess the main system break points and at the same time to attain accurate resource predictions for any future request. [ABSTRACT FROM AUTHOR]

As the number of migrants arriving in Europe increases, host societies face the challenge of supporting their smooth integration, respecting their needs and recognizing their competencies. A key element for their new life is their integration in the labour market. This paper presents a platform which offers a set of tools that has been developed to support migrants to find their way into the labour market in EU countries. This set includes tools for skill assessment, artificial intelligence tools providing recommendations for jobs that match their personal skills and needs, tools for suggesting training paths and options to empower their candidacy, and easy-to-use tools for creating their CV/portfolio. We focus on the evaluation of this platform in real life settings in two countries (Greece and Spain), which is part of our co-creation approach. The results are very promising and show the satisfaction of the migrants. Additionally, they provide valuable insights for both those that develop such tools and those that can use them in their work/activities with the migrants (such as the non-governmental organisations and host authorities). [ABSTRACT FROM AUTHOR]

The maritime industry is one of the most competitive industries today. However, there is a tendency for the profit margins of shipping companies to reduce due to an increase in operational costs, and it does not seem that this trend will change in the near future. The most important reason for the increase in operating costs relates to the increase in fuel prices. To compensate for the increase in operating costs, shipping companies can either renew their fleet or try to make use of new technologies to optimize the performance of their existing one. The software structure in the maritime industry has changed and is now leaning towards the use of Artificial Intelligence (AI) and, more specifically, Machine Learning (ML) for calculating its operational scenarios as a way to compensate the reduction of profit. While AI is a technology for creating intelligent systems that can simulate human intelligence, ML is a subfield of AI, which enables machines to learn from past data without being explicitly programmed. ML has been used in other industries for increasing both availability and profitability, and it seems that there is also great potential for the maritime industry. In this paper the authors compares the performance of multiple regression algorithms like Artificial Neural Network (ANN), Tree Regressor (TRs), Random Forest Regressor (RFR), K-Nearest Neighbor (kNN), Linear Regression, and AdaBoost, in predicting the output power of the Main Engines (M/E) of an ocean going vessel. These regression algorithms are selected because they are commonly used and are well supported by the main software developers in the area of ML. For this scope, measured values that are collected from the onboard Automated Data Logging & Monitoring (ADLM) system of the vessel for a period of six months have been used. The study shows that ML, with the proper processing of the measured parameters based on fundamental knowledge of naval architecture, can achieve remarkable prediction results. With the use of the proposed method there was a vast reduction in both the computational power needed for calculations, and the maximum absolute error value of prediction. [ABSTRACT FROM AUTHOR]

With the advent of Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies, the networking infrastructures are becoming increasingly agile in their attempts to offer the quality of services needed by the users, maximizing the efficiency of infrastructure utilization. This in essence mandates the statistical multiplexing of demands across the infrastructures of different Network Providers (NPs), which would allow them to cope with the increasing demand, upgrading their infrastructures at a slower pace. However, to enjoy the benefits of statistical multiplexing, a trusted authority to govern it would be required. At the same time, blockchain technology aspires to offer a solid advantage in such untrusted environments, enabling the development of decentralized solutions that ensure the integrity and immutability of the information stored in the digital ledger. To this end, in this paper, we propose a blockchain-based solution that allows NPs to trade their (processing and networking) resources. We implemented the solution in a test-bed deployed on the cloud and we present the gathered performance results, showing that a blockchain-based solution is feasible and appropriate. We also discuss further improvements and challenges. [ABSTRACT FROM AUTHOR]

5G is considered to be the technology that will accommodate the development and management of innovative services with stringent and diverse requirements from end users, calling for new business models from the industry. In this context, the development and efficient management of Network Services (NS) serving specific vertical industries and spanning across multiple administrative domains and heterogeneous infrastructures is challenging. The main challenges regard the efficient provision of NSs considering the Quality of Service (QoS) requirements per vertical industry along with the optimal usage of the allocated resources. Towards addressing these challenges, this paper details an innovative approach that we have developed for managing and orchestrating such NSs, called SONATA, and compare it with OSM and Cloudify, which are two of the most known open-source Management and Orchestration (MANO) frameworks. In addition to examining the supported orchestration mechanisms per MANO framework, an evaluation of main operational and functional KPIs is provided based on experimentation using a real testbed. The final aim is the identification of their strong and weak points, and the assessment of their suitability for serving diverse vertical industry needs, including of course the Internet of Things (IoT) service ecosystem. [ABSTRACT FROM AUTHOR]