02085nas a2200241   4500008004100000245005600041210005400097300001200151520128400163100002101447700002301468700003101491700002401522700002201546700002501568700002501593700001701618700002901635700002801664700002601692700002201718856010301740       2023                        eng d00aA Chatbot Generator for Improved Digital Governance0 aChatbot Generator for Improved Digital Governance  a123-1343 aChatbots, the pioneering conversational artificial intelligence (AI) agents, have experienced remarkable growth and integration in various domains. In modern societies, chatbots have emerged as transformative digital entities, revolutionizing the way humans interact with technology. These conversational AI agents have transcended their initial applications to become integral parts of various industries and daily life. One of the most prominent roles of chatbots is in customer service, where they offer round-the-clock assistance, swift issue resolution, and personalized interactions.By handling routine queries and tasks, chatbots free up human agents to focus on complex and specialized issues, thus optimizing overall efficiency and customer satisfaction. To this end, this paper aims to present and describe the architecture of a novel chatbot generator with improved functionality in terms of quality of communication with end users and level of provided services, with a specialized infrastructure understanding the Greek language. The chatbot generator was developed in the framework of a research project and will
be pilot tested by two end-users, the National Bank of Greece (NBG) and the General Secretariat for Information Systems & Digital Governance (GSIS-DG).1 aBouras, Christos1 aDiasakos, Damianos1 aKatsigiannis, Chrysostomos1 aKokkinos, Vasileios1 aGkamas, Apostolos1 aKaracapilidis, Nikos1 aCharalabidis, Yannis1 aLachana, Zoi1 aAlexopoulos, Charalampos1 aPapadopoulos, Theodoros1 aKaramanolis, Georgios1 aPsalidas, Michail  uhttp://telematics.upatras.gr/telematics/publications/chatbot-generator-improved-digital-governance02081nas a2200205   4500008004100000245005600041210005500097300001200152520139700164100002501561700002001586700002901606700002801635700001701663700002101680700002501701700002401726700002201750856010301772       2023                        eng d00aChatbot technology asseeement: 40 cases from Greece0 aChatbot technology asseeement 40 cases from Greece  a167-1813 aIn recent years, the field of Artificial Intelligence has seen significant progress, particularly in the development of chatbots via Natural Language Processing (NLP) technology. Recently, however, there has been a real race in this sector with major technology companies constantly presenting new improved solutions. However, the Greek reality presents several peculiarities and difficulties in adopting modern solutions, both due to the diosyncrasies and rarity of the language and the limited funding capabilities of the Greek economy. The purpose of this research is to evaluate the performance of chatbots in terms of the quality of their responses regarding relevance, naturalness, cohesion, accuracy, vocabulary, as well as to assess the user experience and satisfaction. Another goal is to gain a comprehensive comparative picture of chatbot operation in Greece, both
per question and in comparison, between relevant questions. A guided interview with closed-type questions was chosen as the method of evaluation. The aim is to obtain structured and quantified data in an area where the average internet user is not fully familiarized and does not have previous relevant evaluation experience. Conclusions were drawn per question in order to evaluate the level of solutions in a focused and comparative way to identify possible trends and to confirm the consistency of the responses.1 aCharalabidis, Yannis1 aAnagnou, Thanos1 aAlexopoulos, Charalampos1 aPapadopoulos, Theodoros1 aLachana, Zoi1 aBouras, Christos1 aKaracapilidis, Nikos1 aKokkinos, Vasileios1 aGkamas, Apostolos  uhttp://telematics.upatras.gr/telematics/publications/chatbot-technology-asseeement-40-cases-greece01867nas a2200229   4500008004100000245009500041210006900136520100400205100002101209700002301230700003101253700002401284700002201308700002501330700002501355700001701380700002901397700002801426700002601454700002201480856013501502       2023                        eng d00aOn the Development of a Novel Chatbot Generator Architecture: Design and Assessment Issues0 aDevelopment of a Novel Chatbot Generator Architecture Design and3 aChatbots are witnessing remarkable growth in various fields. In modern society, chatbots have evolved into innovative digital entities that have revolutionized the way people interact with technology. These conversational AI agents have gone beyond their initial uses to become an integral part of various industries. One of the most important roles of chatbots is customer service, providing 24/7 support, quick problem resolution, and personalized interactions. Chatbots free up human agents by handling routine queries and tasks, allowing them to focus on complex and specific issues, optimizing the overall efficiency and customer satisfaction. To this end, this paper aims to present the architecture of a novel chatbot generator with improved functionality in terms of quality of communication with end users and level of provided services. In addition, the paper presents the results of two pilot chatbots assessment by users with experience in artificial intelligence and chatbot technology.1 aBouras, Christos1 aDiasakos, Damianos1 aKatsigiannis, Chrysostomos1 aKokkinos, Vasileios1 aGkamas, Apostolos1 aKaracapilidis, Nikos1 aCharalabidis, Yannis1 aLachana, Zoi1 aAlexopoulos, Charalampos1 aPapadopoulos, Theodoros1 aKaramanolis, Georgios1 aPsalidas, Michail  uhttp://telematics.upatras.gr/telematics/publications/development-novel-chatbot-generator-architecture-design-and-assessment-issues01103nas a2200169   4500008004100000245009000041210006900131520044400200100002100644700001800665700002200683700002500705700002400730700002300754700002300777856013300800       2021                        eng d00aAn agent-based simulation model for energy saving in large passenger and cruise ships0 aagentbased simulation model for energy saving in large passenger3 aUndoubtedly, energy saving is of paramount importance in the shipping industry, as far as both the protection of environment and the reduction of the associated operating costs are concerned. In this direction, the
International Maritime Organization aims to reduce ship emissions by at least 50% by 2050, while ships to be built by 2025 are expected to be a massive 30% more energy efficient than those built some years ago [IMO,
2018].1 aBouras, Christos1 aBarri, Eirini1 aGkamas, Apostolos1 aKaracapilidis, Nikos1 aKaradimas, Dimitris1 aKournetas, Giorgos1 aPanaretou, Yiannis  uhttp://telematics.upatras.gr/telematics/publications/agent-based-simulation-model-energy-saving-large-passenger-and-cruise-ships02045nas a2200169   4500008004100000245013000041210006900171520132000240100002101560700001801581700002201599700002501621700002401646700002401670700002301694856015801717       2021                        eng d00aA Novel Approach to Energy Management in Large Passenger and Cruise Ships: Integrating Simulation and Machine Learning Models0 aNovel Approach to Energy Management in Large Passenger and Cruis3 aIt has been broadly admitted that the prediction of energy consumption in large passenger and cruise ships is a complex and challenging issue. Aiming to address it, this chapter reports on the development of a novel approach that builds on a sophisticated agent-based simulation model, which takes into account diverse parameters such as the size, type and behavior of the different categories of passengers onboard, the energy consuming facilities and devices of a ship, spatial data concerning the layout of a ship’s decks, and alternative ship operation modes. According to the proposed approach, outputs obtained from multiple simulation runs are then exploited by prominent Machine Learning algorithms to extract meaningful patterns between the composition of passengers and the corresponding energy demands in a ship. In this way, our approach is able to predict alternative energy consumption scenarios and trigger meaningful insights concerning the overall energy management in a ship. Overall, the proposed approach may handle the underlying uncertainty by blending the process centric character of a simulation model and the data-centric character of Machine Learning algorithms. The chapter also describes the overall architecture of the proposed solution, which is based on the microservices approach.1 aBouras, Christos1 aBarri, Eirini1 aGkamas, Apostolos1 aKaracapilidis, Nikos1 aKaradimas, Dimitris1 aKournetas, Georgios1 aPanaretou, Yiannis  uhttp://telematics.upatras.gr/telematics/publications/novel-approach-energy-management-large-passenger-and-cruise-ships-integrating-simulation-and-machine01835nas a2200181   4500008004100000245009600041210006900137300001200206520113500218100001801353700002101371700002201392700002501414700002401439700002401463700002301487856014301510       2020                        eng d00aBlending simulation and Machine Learning models to advance energy management in large ships0 aBlending simulation and Machine Learning models to advance energ  a101-1093 aThe prediction of energy consumption in large passenger and cruise ships is certainly a complex and challenging issue. Towards addressing it, this paper reports on the development of a novel approach that builds on a sophisticated agent-based simulation model, which takes into account diverse parameters such as the size, type and behavior of the different categories of passengers onboard, the energy consuming facilities and devices of a ship, spatial data concerning the layout of a ship’s decks, and alternative ship operation modes. Outputs obtained from multiple simulation runs are then exploited by prominent Machine Learning algorithms to extract meaningful patterns between the composition of passengers and the corresponding energy demands in a ship. In this way, our approach is able to predict alternative energy consumption scenarios and trigger meaningful insights concerning the overall energy management in a ship. Overall, the proposed approach may handle the underlying uncertainty by blending the process-centric character of a simulation model and the data-centric character of Machine Learning algorithms.1 aBarri, Eirini1 aBouras, Christos1 aGkamas, Apostolos1 aKaracapilidis, Nikos1 aKaradimas, Dimitris1 aKournetas, Georgios1 aPanaretou, Yiannis  uhttp://telematics.upatras.gr/telematics/publications/blending-simulation-and-machine-learning-models-advance-energy-management-large-ships01753nas a2200145   4500008004100000245010400041210006900145520113500214100001801349700002101367700002201388700002401410700002501434856014801459       2020                        eng d00aA Novel Approach for Handling Diverse Energy Consumption Issues in Large Passenger and Cruise Ships0 aNovel Approach for Handling Diverse Energy Consumption Issues in3 aThe prediction of energy consumption in large passenger and cruise ships is certainly a complex and challenging issue. Towards addressing it, this paper reports on the development of a novel approach that builds on a sophisticated agent-based simulation model, which takes into account diverse parameters such as the size, type and behavior of the different categories of passengers onboard, the energy consuming facilities and devices of a ship, spatial data concerning the layout of a ship’s decks, and alternative ship operation modes. Outputs obtained from multiple simulation runs are then exploited by prominent Machine Learning algorithms to extract meaningful patterns between the composition of passengers and the corresponding energy demands in a ship. In this way, our approach is able to predict alternative energy consumption scenarios and trigger meaningful insights concerning the overall energy management in a ship. Overall, the proposed approach may handle the underlying uncertainty by blending the process-centric character of a simulation model and the data-centric character of Machine Learning algorithms.1 aBarri, Eirini1 aBouras, Christos1 aGkamas, Apostolos1 aKournetas, Georgios1 aKaracapilidis, Nikos  uhttp://telematics.upatras.gr/telematics/publications/novel-approach-handling-diverse-energy-consumption-issues-large-passenger-and-cruise-ships01435nas a2200169   4500008004100000245011000041210006900151520073500220100001800955700002100973700002200994700002501016700002401041700002401065700002301089856015301112       2020                        eng d00aTowards an informative simulation-based application for energy saving in large passenger and cruise ships0 aTowards an informative simulationbased application for energy sa3 aOver the years, the need to save energy and efficiently manage its consumption becomes increasingly imperative. This paper reports on the development of a novel application for handling diverse energy consumption issues in large passenger and cruise ships. Our overall approach is based on a comprehensive agent-based simulation model, which takes into account spatial data concerning a ship’s decks and position of energy consuming facilities, as well as data concerning the ship’s passengers and their behavior during the operation of the vessel. The proposed application may predict energy consumption for a particular vessel and passenger group and accordingly facilitate informed decision making on energy saving matters.1 aBarri, Eirini1 aBouras, Christos1 aGkamas, Apostolos1 aKaracapilidis, Nikos1 aKaradimas, Dimitris1 aKournetas, Georgios1 aPanaretou, Yiannis  uhttp://telematics.upatras.gr/telematics/publications/towards-informative-simulation-based-application-energy-saving-large-passenger-and-cruise-ships01359nas a2200169   4500008004100000245007600041210006900117260002100186300001200207520073500219100002100954700002300975700002500998700002401023700002201047856012001069       2015                        eng d00aA Mobile Learning Application for Self-Management of Health and Disease0 aMobile Learning Application for SelfManagement of Health and Dis  cNovember 19 - 20  a101-1053 a<p class="rtejustify">Supporting self-management of patients is a highly challenging task, which needs to meaningfully exploit and interrelate approaches and technologies concerning interactive communication, personalized health and mobile learning. In line with these remarks, this paper reports on the development of an innovative clinical decision support platform for selfmanagement of health and disease purposes. Work presented focuses on two basic components of the platform, namely a webbased collaboration support tool and a mobile application, both aiming to augment the interaction of all types of stakeholders with the platform. The functionality of the above components is sketched through a realistic use case.</p>
1 aBouras, Christos1 aKapoulas, Vaggelis1 aKaracapilidis, Nikos1 aKokkinos, Vasileios1 aPapazois, Andreas  uhttp://telematics.upatras.gr/telematics/publications/mobile-learning-application-self-management-health-and-disease