Βιβλία και Συλλογικοί Τόμοι

@book{vrahatis2024modeling,

title = {Modeling and Analysis of Complex Networks in Molecular Biology and Neuroscience: Using R and Python},

author = {Aristidis Vrahatis and Georgios N. Dimitrakopoulos and Panagiotis Vlamos},

url = {https://doi.org/10.57713/kallipos-1055},

doi = {10.57713/kallipos-1055},

isbn = {978-618-228-305-9},

year  = {2024},

date = {2024-01-01},

publisher = {Kallipos, Open Academic Editions},

address = {Athens},

abstract = {A complex system consists of many components, which may interact with each other in several ways. Graph/Network-based modelling is a suitable approach for understanding such complex systems. Also, machine learning techniques now play an important role in analysing large volumes of data. Two such systems in the wider area of Biology, which are still not fully understood, are the cell and the brain. Bioinformatics is an interdisciplinary scientific field that develops methodologies and software tools for understanding biological data, which can focus on the genomic, transcriptomic, proteomic or metabolomic level. Today, with the recent techniques of Molecular Biology, a huge amount of data is produced; thus, adapted algorithmic techniques are required for their efficient processing. In the field of Computational Neuroscience, we focus on electroencephalography (EEG), which captures signals from multiple areas of the brain. These data require careful pre-processing to be analysed and provide useful information. The most advanced analysis techniques include the generation of networks in time and frequency domains. Therefore, in this book, we present in detail the most recent and widely used methods in the literature for graph analysis of distinct types of data in Molecular Biology and Neurosciences.},

note = {Greek-language textbook: Μοντελοποίηση και Ανάλυση Πολύπλοκων Δικτύων της Μοριακής Βιολογίας και των Νευροεπιστημών. Με τη χρήση των R και Python},

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Kouroupis, M., Korfiatis, N., & Cornford, J. (2019). Artificial intelligence-assisted detection of diabetic retinopathy on digital fundus images: Concepts and applications in the national health service. https://doi.org/10.1016/B978-0-12-819043-2.00011-3

Ivanov, T., Izberovic, S., & Korfiatis, N. (2016). The heterogeneity paradigm in big data architectures. https://doi.org/10.4018/978-1-5225-1759-7.ch021

Stai, E., Karyotis, V., & Papavassiliou, S. (2016). Modeling and control of user interest dictated information dissemination in communications and social networks. Nova Science Publishers, Inc..

@book{Stai201645,

title = {Modeling and control of user interest dictated information dissemination in communications and social networks},

author = {E. Stai and V. Karyotis and S. Papavassiliou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85156108074&partnerID=40&md5=283dfb537c58ae4ad64224391ac08e60},

isbn = {9781634852432; 9781634852258},

year  = {2016},

date = {2016-01-01},

journal = {Advances in Social Networking Research},

pages = {45-88},

publisher = {Nova Science Publishers, Inc.},

abstract = {Information dissemination is one of the most critical processes in modern societies and one of the most important reasons for the proliferation of communications networks, especially of online social networks that have enabled global information exchange at rapid scales. This chapter will focus on information dissemination models that compared to the current literature, they specifically take into account aspects related to user interest and its temporal variability in the dynamics of information spreading. We focus on a dissemination framework regarding solely useful information, where users of communications and online social networks are willing to accept information and potentially further propagate it to their uninformed peers, depending on the interest each user has on specific topics. This is a novel perspective to consider information dissemination under inhomogeneous mixing, since the time-varying user interests affect significantly the outcome and the dynamics of information propagation and have an impact on its control process. Such models can be employed for further improving information dissemination analysis and control methodologies, and they can be exploited in various application domains, e.g., from infrastructure design and management, to marketing and advertising improvement. This chapter addresses both the modeling of information dissemination, especially via epidemics and evolutionary game theoretic techniques, as well as their control via optimal control frameworks and dual optimization techniques. © 2016 Nova Science Publishers, Inc. All rights reserved.},

note = {cited By 0},

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Karyotis, V., & Khouzani, M. H. R. (2016). Malware Diffusion Models for Modern Complex Networks: Theory and Applications. Morgan Kaufmann. https://doi.org/10.1016/C2014-0-02168-5

@book{Karyotis20161,

title = {Malware Diffusion Models for Modern Complex Networks: Theory and Applications},

author = {Vasileios Karyotis and M. H. R. Khouzani},

url = {https://doi.org/10.1016/C2014-0-02168-5},

doi = {10.1016/C2014-0-02168-5},

isbn = {9780128027141},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

publisher = {Morgan Kaufmann},

abstract = {Malware Diffusion Models for Wireless Complex Networks: Theory and Applications provides a timely update on malicious software (malware), a serious concern for all types of network users, from laymen to experienced administrators. As the proliferation of portable devices, namely smartphones and tablets, and their increased capabilities, has propelled the intensity of malware spreading and increased its consequences in social life and the global economy, this book provides the theoretical aspect of malware dissemination, also presenting modeling approaches that describe the behavior and dynamics of malware diffusion in various types of wireless complex networks. Sections include a systematic introduction to malware diffusion processes in computer and communications networks, an analysis of the latest state-of-the-art malware diffusion modeling frameworks, such as queuing-based techniques, calculus of variations based techniques, and game theory based techniques, also demonstrating how the methodologies can be used for modeling in more general applications and practical scenarios. Presents a timely update on malicious software (malware), a serious concern for all types of network users, from laymen to experienced administrators. Systematically introduces malware diffusion processes, providing the relevant mathematical background. Discusses malware modeling frameworks and how to apply them to complex wireless networks. Provides guidelines and directions for extending the corresponding theories in other application domains, demonstrating such possibility by using application models in information dissemination scenarios. © 2016 Elsevier Inc. All rights reserved.},

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Karyotis, V., Stai, E., & Papavassiliou, S. (2013). Evolutionary dynamics of complex communications networks. CRC Press.

Abstract | BibTeX

@book{karyotis2014evolutionary,

title = {Evolutionary dynamics of complex communications networks},

author = {Vasileios Karyotis and Eleni Stai and Symeon Papavassiliou},

isbn = {9781466518414; 9781466518407},

year  = {2013},

date = {2013-01-01},

urldate = {2014-01-01},

publisher = {CRC Press},

abstract = {Until recently, most network design techniques employed a bottom-up approach with lower protocol layer mechanisms affecting the development of higher ones. This approach, however, has not yielded fascinating results in the case of wireless distributed networks. Addressing the emerging aspects of modern network analysis and design, Evolutionary Dynamics of Complex Communications Networks introduces and develops a top-bottom approach where elements of the higher layer can be exploited in modifying the lowest physical topology-closing the network design loop in an evolutionary fashion similar to that observed in natural processes. This book provides a complete overview of contemporary design approaches from the viewpoint of network science and complex/social network analysis. A significant part of the text focuses on the classification and analysis of various network modification mechanisms for wireless decentralized networks that exploit social features from relevant online social networks. Each chapter begins with learning objectives and introductory material and slowly builds to more detailed analysis and advanced concepts. Each chapter also identifies open issues, while by the end of the book, potential research directions are summarized for the more interested researcher or graduate student. The approach outlined in the book will help network designers and administrators increase the value of their infrastructure without requiring any significant additional investment. Topics covered include: basic network graph models and properties, cognitive methods and evolutionary computing, complex and social network analysis metrics and features, and analysis and development of the distinctive structure and features of complex networks. Considering all aspects of modern network analysis and design, the text covers the necessary material and background to make it a suitable source of reference for graduate students, postdoctoral researchers, and scientists. © 2014 by Taylor & Francis Group, LLC.},

keywords = {},

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Until recently, most network design techniques employed a bottom-up approach with lower protocol layer mechanisms affecting the development of higher ones. This approach, however, has not yielded fascinating results in the case of wireless distributed networks. Addressing the emerging aspects of modern network analysis and design, Evolutionary Dynamics of Complex Communications Networks introduces and develops a top-bottom approach where elements of the higher layer can be exploited in modifying the lowest physical topology-closing the network design loop in an evolutionary fashion similar to that observed in natural processes. This book provides a complete overview of contemporary design approaches from the viewpoint of network science and complex/social network analysis. A significant part of the text focuses on the classification and analysis of various network modification mechanisms for wireless decentralized networks that exploit social features from relevant online social networks. Each chapter begins with learning objectives and introductory material and slowly builds to more detailed analysis and advanced concepts. Each chapter also identifies open issues, while by the end of the book, potential research directions are summarized for the more interested researcher or graduate student. The approach outlined in the book will help network designers and administrators increase the value of their infrastructure without requiring any significant additional investment. Topics covered include: basic network graph models and properties, cognitive methods and evolutionary computing, complex and social network analysis metrics and features, and analysis and development of the distinctive structure and features of complex networks. Considering all aspects of modern network analysis and design, the text covers the necessary material and background to make it a suitable source of reference for graduate students, postdoctoral researchers, and scientists. © 2014 by Taylor & Francis Group, LLC.

Karyotis, V., & Papavassiliou, S. (2010). Mobility-induced capacity-delay tradeoffs in wireless multihop networks. Nova Science Publishers, Inc..

@book{Karyotis2010137,

title = {Mobility-induced capacity-delay tradeoffs in wireless multihop networks},

author = {V. Karyotis and S. Papavassiliou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896200241&partnerID=40&md5=1bccb2aa11aaa0c9d16fc2153b0d90e1},

isbn = {9781608761869},

year  = {2010},

date = {2010-01-01},

journal = {Cluster Computing and Multi-Hop Network Research},

pages = {137-162},

publisher = {Nova Science Publishers, Inc.},

abstract = {Wireless mobile multihop networks, such as ad hoc and sensor networks, have become rather popular in realizing seamless distributed computation and accommodating autonomic behavior. In these networks, the lack of central infrastructure, the spatio-temporal dynamic nature of wireless communications along with their mobilityinduced dynamic topology, pose additional challenges on the traffic-carrying capacity of the system and raise concerns on the delay performance of packet delivery mechanisms. Despite such impediments of the wireless multihop environment, it has been recently shown that mobility can be exploited by various relay techniques for increasing the capacity of such networks. However, the throughput-effectiveness of the mobilityexploiting relay methods takes place at the cost of a greater number of intermediate hops traversed by packets traveling in the network in order to reach their final destination and additional buffering required at the intermediate nodes, revealing an inherent tradeoff between capacity and delay in mobile multihop networks. In this chapter, we first briefly review various mobility models, and then focus on the inherent mobilityinduced tradeoff between capacity and delay in multihop networks. Mobility is treated as both a constructive (increasing capacity), as well as a destructive (increasing delay) factor in wireless infrastructureless networks. Formal problem settings are presented, followed by applications and protocols that could be used in actual networks for increasing performance and realizing viable networking services. We also provide a qualitative and quantitative comparison among the various protocols, from which directions for future research studies are indicated. © 2010 by Nova Science Publishers, Inc. All rights reserved.},

note = {cited By 0},

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Pateli, A., & Giaglis, G. (2007). An interdisciplinary research framework to investigate electronic business models. https://doi.org/10.1142/9789812707628_0007

Korfiatis, N., Sicilia, M. A., Hess, C., Stein, K., & Schlieder, C. (2007). Social network models for enhancing reference-based search engine rankings. https://doi.org/10.4018/978-1-59904-543-6.ch006

Κεφάλαια σε Συλλογικούς Τόμους

Karyotis, V., Bardaki, C., & Kourouthanassis, P. E. (2023). H2UMANISM: Holistic Healthcare Supply Chain Management for Precision Medicine. In Handbook of Computational Neurodegeneration (pp. 1–25). Springer International Publishing. https://doi.org/10.1007/978-3-319-75479-6_12-1

Dimitrakopoulos, G. N. (2023). From Raw EEG Signals to Brain Networks: An EEGLAB Tutorial. In Handbook of Computational Neurodegeneration (pp. 1–16). Springer International Publishing. https://doi.org/10.1007/978-3-319-75479-6_26-1

Christopoulou, E., & Ringas, D. (2023). Sociable Smart Cities. In EAI/Springer Innovations in Communication and Computing (pp. 155–181). Springer International Publishing. https://doi.org/10.1007/978-3-031-30514-6_8

Pateli, A., Kourouthanassis, P., Nikopoulou, M., & Chasapi, G. (2022). Digital transformation for resilient tourism: Evidence from the Greek hospitality industry. In Christou, E., & Fotiadis, A. (Ed.), Reviving Tourism in the Post-Pandemic Era (pp. 69–80). International Hellenic University, School of Economics and Business. https://doi.org/10.5281/zenodo.6428590

Koumadorakis, D. E., Dimitrakopoulos, G. N., Krokidis, M. G., & Vrahatis, A. G. (2022). Gene Regulatory Network Reconstruction Using Single-Cell RNA-Sequencing. In Handbook of Computational Neurodegeneration (pp. 1–15). Springer International Publishing. https://doi.org/10.1007/978-3-319-75479-6_18-1

Kakkos, I., Miloulis, S.-T., Gkiatis, K., Dimitrakopoulos, G. N., & Matsopoulos, G. K. (2020). Human–Machine Interfaces for Motor Rehabilitation. In Studies in Computational Intelligence (pp. 1–16). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-61114-2_1

Karyotis, V., Vitoropoulou, M., Kalatzis, N., Roussaki, I., & Papavassiliou, S. (2019). Efficient and socio-aware recommendation approaches for big data networked systems. In Big Data Recommender Systems: Algorithms, Architectures, Big Data, Security and Trust (pp. 41–70). Institution of Engineering and Technology. https://doi.org/10.1049/PBPC035F_ch4

@incollection{karyotis2019efficient,

title = {Efficient and socio-aware recommendation approaches for big data networked systems},

author = {Vasileios Karyotis and Margarita Vitoropoulou and Nikos Kalatzis and Ioanna Roussaki and Symeon Papavassiliou},

url = {https://doi.org/10.1049/PBPC035F_ch4},

doi = {10.1049/PBPC035F_ch4},

isbn = {9781785619755},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {Big Data Recommender Systems: Algorithms, Architectures, Big Data, Security and Trust},

pages = {41--70},

publisher = {Institution of Engineering and Technology},

abstract = {In this chapter, we present several approaches designed for providing efficient recommendations in large web systems characterized by bigdata scales. The key feature of the considered approaches is that they all rely on different elements and properties of social/complex network analysis for addressing various deficiencies of legacy and current recommendation systems when very large operational scales emerge. The main challenges of recommendations addressed by the presented approaches are the diversity (novelty) of recommendations, the cold-start problem, scalability and noise filtering issues, as well as the efficiency of developing these approaches and integrating them in operational systems. This chapter aspires to provide an educated overview, leading to a solid fundamental background on how social/complex network analysis can be exploited for more effective recommendations in stringent environments characterized by large scales ofusers, items and associated data, cumulatively referred to as big network data. Furthermore, our work aims at highlighting the design principles that are more interesting for enabling the extension of the presented approaches and their combination with other current state-of-the-art techniques, thus leading to more socioaware and efficient recommendation approaches in the near and longer term future. © The Institution of Engineering and Technology 2020.},

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Belias, D., Velissariou, E., Chondrogiannis, M., Kyriakou, D., Sdrolias, L., Aspridis, G., & Koustelios, A. (2018). Ethical Tourism: The Theory Vs. The Hedonistic Reality in Popular Greek Tourism Resorts. In Papathanassis, A., Katsios, S., & Dinu, N. R. (Ed.), Yellow Tourism: Crime and Corruption in the Holiday Sector (pp. 71–81). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-94664-1_4

Belias, D., Velissariou, E., Chondrogiannis, M., Kyriakou, D., Varsanis, K., Vasiliadis, L., & Koustelios, A. (2018). Exploring Insurance Fraud and Tourists’ Misbehaviour. In Papathanassis, A., Katsios, S., & Dinu, N. R. (Ed.), Yellow Tourism: Crime and Corruption in the Holiday Sector (pp. 147–156). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-94664-1_9

Chondrogiannis, M., Katsios, S., Belias, D., Velissariou, E., Papadimopoulos, I., & Koustelios, A. (2018). Reputation Management in the Tourism Industry. In Papathanassis, A., Katsios, S., & Dinu, N. R. (Ed.), Yellow Tourism: Crime and Corruption in the Holiday Sector (pp. 235–245). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-94664-1_15

Belias, D., Velissariou, E., Roditis, A., Chondrogiannis, M., Katsios, S., Kyriakou, D., Varsanis, K., & Koustelios, A. (2018). The Unethical Practice of Hotel Review Ghost-Writers. In Papathanassis, A., Katsios, S., & Dinu, N. R. (Ed.), Yellow Tourism: Crime and Corruption in the Holiday Sector (pp. 157–166). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-94664-1_10

Karyotis, V., & Stai, E. (2017). Hyperbolic Big Data Analytics for Dynamic Network Management and Optimization. In Big Data and Computational Intelligence in Networking (pp. 177–208). CRC Press. https://doi.org/10.1201/b21278

@incollection{karyotis2017hyperbolic,

title = {Hyperbolic Big Data Analytics for Dynamic Network Management and Optimization},

author = {Vasileios Karyotis and Eleni Stai},

url = {https://doi.org/10.1201/b21278},

doi = {10.1201/b21278},

isbn = {9781498784870; 9781498784863},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {Big Data and Computational Intelligence in Networking},

pages = {177--208},

publisher = {CRC Press},

abstract = {178Massive numbers of devices, growing user populations and voluminous amounts of produced/exchanged information are expected in the complex cyber-physical networks of the future. The new scales of operation give rise to a big network data era, where multi-layer networks form and users become content producers/consumers (prosumers). The challenges associated with the forthcoming networking environments require radical rethinking of current network analysis, management and operation practices. This chapter will focus on this effort, and more specifically on reinventing the machinery for computing key network metrics that allow improving network management/operation, while also developing more efficient overlay applications within demanding operational environments. Special attention is given to the impact of network evolution on the computation of such metrics, an aspect relatively neglected until recently. In order to efficiently compute key metrics associated with social or structural features of the network and track them when the infrastructure evolves, a big data analytics methodology, denoted as Hyperbolic Data Analytics (HDA), is applied. HDA is based on the embedding of graphs in the hyperbolic space, leading to more efficient management/operation, typically by exploiting hidden network structure. HDA is a characteristic example of developing computational intelligence over big network data and exploiting them for improving/optimizing both infrastructures and applications/services. HDA will provide the means for computing efficiently network analysis metrics, the evolution of which indicates the evolution of the corresponding network’s structure. © 2018 by Taylor and Francis Group, LLC.},

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Kourouthanassis, P. E., & Giaglis, G. M. (2016). The design challenge of pervasive information systems. In Pervasive information systems (pp. 29–85). Routledge.

Kourouthanassis, P. E., & Giaglis, G. M. (2016). Toward pervasiveness: Four eras of information systems development. In Pervasive Information Systems (pp. 19–42). Routledge.

Kourouthanassis, P. E., & Giaglis, G. M. (2016). Pervasive Electronic Services in Health Care Ilias Maglogiannis and Stathes Hadjiefthymiades. In Pervasive Information Systems (pp. 196–210). Routledge.

Stai, E., Karyotis, V., Katsinis, G., Tsiropoulou, E. E., & Papavassiliou, S. (2016). A Hyperbolic Big Data Analytics Framework within Complex and Social Networks. In Big Data in Complex and Social Networks (pp. 3–36). Chapman and Hall/CRC. https://doi.org/10.1201/9781315396705

Pateli, A. G., Mikalef, P., & Mylonas, P. (2016). Greece. In Closing the Innovation Gap in the Adriatic Region. Trieste: EUT Edizioni Università di Trieste.

Kourouthanassis, P. E., Mikalef, P., Ioannidou, M., & Pateli, A. (2015). Exploring the online satisfaction gap of medical doctors: an expectation-confirmation investigation of information needs. In Genedis 2014: Computational Biology and Bioinformatics (pp. 217–228). Springer International Publishing Cham. https://doi.org/10.1007/978-3-319-09012-2_15

Karyotis, V., Anifantis, E., & Papavassiliou, S. (2014). Cross-layer based resource management frameworks for mobile cognitive radio networks. In Resource management in mobile computing environments (pp. 285–322). Springer, Cham. https://doi.org/10.1007/978-3-319-06704-9-13

@incollection{karyotis2014cross,

title = {Cross-layer based resource management frameworks for mobile cognitive radio networks},

author = {Vasileios Karyotis and Evangelos Anifantis and Symeon Papavassiliou},

url = {https://doi.org/10.1007/978-3-319-06704-9-13},

doi = {10.1007/978-3-319-06704-9-13},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

booktitle = {Resource management in mobile computing environments},

pages = {285--322},

publisher = {Springer, Cham},

abstract = {Resource management has been one of the most important aspects of Mobile Cognitive Radio Networks (MCRNs) since their inception. It engulfs fundamental network operations such as spectrum management, bandwidth allocation, channel access, data routing, transmission power assignment and energy management. All such mechanisms are necessary for achieving the delay/throughput requirements desired by modern network services and users. Compared to traditional wireless mobile networks, the MCRN environment poses additional challenges and complicated requirements to the above functions and resource management in general, which eventually lead to an urgent need for reconsideration of the employed wireless protocol stack. The objective in MCRNs would be to apply more efficient protocol hierarchies and achieve better management of the common available resources for both secondary and primary users, at the minimum possible cost and by leaving the minimum possible resource footprint, as required for all modern communications systems. The cross-layer design approach has been proven extremely useful towards this direction, especially for network types such as sensor, mesh and cognitive radios. It allowed exploiting the most appropriate network mechanisms, while bypassing monolithic and outdated requirements when possible. In this chapter, we focus exactly on this aspect of protocol architectures and network management, and present state-of-the-art cross-layer based resource management approaches for MCRNs. Initially, we present three important resource management frameworks, and then we provide directions for developing generic and adaptive resource management protocol architectures for MCRNs, based on cross-layer and component-based design principles emerging from the previously presented approaches. This is the first attempt to highlight the suitability of combining cross-layer with component-based network design, and this work will also sketch qualitatively the potential benefits of such approach. Finally, some practical considerations for the proposed architecture, along with directions for future research conclude the chapter. © 2014 Springer International Publishing Switzerland.},

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Resource management has been one of the most important aspects of Mobile Cognitive Radio Networks (MCRNs) since their inception. It engulfs fundamental network operations such as spectrum management, bandwidth allocation, channel access, data routing, transmission power assignment and energy management. All such mechanisms are necessary for achieving the delay/throughput requirements desired by modern network services and users. Compared to traditional wireless mobile networks, the MCRN environment poses additional challenges and complicated requirements to the above functions and resource management in general, which eventually lead to an urgent need for reconsideration of the employed wireless protocol stack. The objective in MCRNs would be to apply more efficient protocol hierarchies and achieve better management of the common available resources for both secondary and primary users, at the minimum possible cost and by leaving the minimum possible resource footprint, as required for all modern communications systems. The cross-layer design approach has been proven extremely useful towards this direction, especially for network types such as sensor, mesh and cognitive radios. It allowed exploiting the most appropriate network mechanisms, while bypassing monolithic and outdated requirements when possible. In this chapter, we focus exactly on this aspect of protocol architectures and network management, and present state-of-the-art cross-layer based resource management approaches for MCRNs. Initially, we present three important resource management frameworks, and then we provide directions for developing generic and adaptive resource management protocol architectures for MCRNs, based on cross-layer and component-based design principles emerging from the previously presented approaches. This is the first attempt to highlight the suitability of combining cross-layer with component-based network design, and this work will also sketch qualitatively the potential benefits of such approach. Finally, some practical considerations for the proposed architecture, along with directions for future research conclude the chapter. © 2014 Springer International Publishing Switzerland.

Yfantis, V., Vassilopoulou, K., & Pateli, A. G. (2014). Government as a Service in Communities. In Advances in Information Quality and Management (pp. 3236–3244). IGI Global. https://doi.org/10.4018/978-1-4666-5888-2.ch318

Pateli, A. G., & Lioukas, S. (2012). Antecedents to Value Creation and Value Appropriation Outcomes of Strategic Alliances: The Moderating Role of Governance Mode. In Management Dynamics in Strategic Alliances (pp. 53–72). Emerald Publishing Limited. https://doi.org/10.1108/978-1-61735-756-520251004

Karyotis, V., & Papavassiliou, S. (2009). Topology Control in Cooperative Wireless Ad Hoc Networks. In Cooperative Wireless Communications (pp. 167–189). CRC Press. https://doi.org/10.1201/9781420064704-13

Karaiskos, D. C., & Kourouthanassis, P. E. (2008). Determinants of User Acceptance for RFID Ticketing Systems. In Advances in Ubiquitous Computing: Future Paradigms and Directions (pp. 150–170). IGI Global.

Philippidou, S., & Pateli, A. G. (2008). Οργανωσιακοί παράγοντες που επιδρούν και επηρεάζουν την αφομοίωση της συνεχούς αλλαγής. In Διοίκηση των Αλλαγών & Επιχειρηματική Ευφυΐα (pp. 9–22). Athens, Greece: Εκδόσεις Νέων Τεχνολογιών.

Giaglis, G. M., Kourouthanassis, P., & Tsamakos, A. (2003). Towards a classification framework for mobile location based services. In Mobile commerce: technology, theory and applications (pp. 67–85). IGI Global.