Dr. Vasileios A. Memos
Dept. of Applied Informatics, University of Macedonia, Thessaloníki, Kentrikí Makedonía, Greece
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Academic Career Length
13Years (2014-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 June 2018 in Future Generation Computer Systems
Journal Article
Vasileios A. Memos
Kostas PsannisYIYutaka Ishibashi
Yutaka Ishibashi
Byung-Gyu Kim
Brij GuptaJournal Article
GMGeorgios Minopoulos
Georgios MinopoulosVasileios A. MemosKSKonstantinos D Stergiou
Konstantinos D Stergiou
Christos L. StergiouKostas PsannisA lesson learned during the pandemic is that social distancing saves lives. As it was shown recently, the healthcare industry is structured in a way that cannot protect medical staff from possible infectious diseases, such as COVID-19. Today’s healthcare services seem anachronistic and not convenient for both doctors and patients. Although there have been several advances in recent years, especially in developed countries, the need for a holistic change is imperative. Evidently, future technologies should be introduced in the health sector, where Virtual Reality, Augmented Reality, Artificial Intelligence, and Tactile Internet can have vast applications. Thus, the healthcare industry could take advantage of the great evolution of pervasive computing. In this paper, we point out the challenges from the current visualization techniques and present a novel visualization technique assisted with haptics which is enhanced with artificial intelligent algorithms in order to offer remote patient examination and treatment through robotics. Such an approach provides a more detailed method of medical image data visualization and eliminates the possibility of diseases spreading, while reducing the workload of the medical staff.
Journal Article
GMGeorgios Minopoulos
Georgios MinopoulosVasileios A. MemosChristos L. StergiouKSKonstantinos D Stergiou
Konstantinos D Stergiou
Andreas PlagerasKostas Psannis +1
Current medical methods still confront numerous limitations and barriers to detect and fight against illnesses and disorders. The introduction of emerging technologies in the healthcare industry is anticipated to enable novel medical techniques for an efficient and effective smart healthcare system. Internet of Things (IoT), Wireless Sensor Networks (WSN), Big Data Analytics (BDA), and Cloud Computing (CC) can play a vital role in the instant detection of illnesses, diseases, viruses, or disorders. Complicated techniques such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) could provide acceleration in drug and antibiotics discovery. Moreover, the integration of visualization techniques such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) with Tactile Internet (TI), can be applied from the medical staff to provide the most accurate diagnosis and treatment for the patients. A novel system architecture, which combines several future technologies, is proposed in this paper. The objective is to describe the integration of a mixture of emerging technologies in assistance with advanced networks to provide a smart healthcare system that may be established in hospitals or medical centers. Such a system will be able to deliver immediate and accurate data to the medical stuff in order to aim them in order to provide precise patient diagnosis and treatment.
21 May 2015 in Journal of Real-Time Image Processing
Journal Article
Vasileios A. MemosKostas Psannis29 March 2022 in IEEE Transactions on Industrial Informatics
Journal Article
The new Industry 4.0 standard has offered many advantages to the industries improving their production rate since it evaluates novel cutting-edge technologies like artificial intelligence, machine learning, cyber-physical systems, and Internet of Things (IoTs) to automate manufacturing processes so as to minimize time and economical costs while improving the quality of products. However, this rapid industrial transition carries risks in terms of security and privacy issues that arise. In this article, we propose a novel secure network model to enhance network security and employees’ privacy in the edge-enabled industrial IoTs. Experimental results demonstrate encouraging performance rates in terms of accuracy, precision, recall, fall-out, F-measure, and Matthews correlation coefficient against known and unknown bot attacks.
Journal Article
Christos L. StergiouAndreas PlagerasVasileios A. MemosMKMaria P. Koidou
Maria P. KoidouKostas PsannisEven though the field of medicine has made great strides in recent years, infectious diseases caused by novel viruses that damage the respiratory system continue to plague people all over the world. This type of virus is very dangerous, especially for people with serious long-term breathing problems like asthma, pneumonia, or bronchitis infections. Thus, this paper demonstrates a new secure machine learning monitoring system for a model for virus detection. Our proposed model makes use of four basic emerging technologies, the Internet of Things (IoT), Wireless Sensor Networks (WSN), Cloud Computing (CC), and Machine Learning (ML), to detect dangerous types of viruses that infect people or animals causing panic worldwide and deregulating human daily life. The proposed system is a robust system that could be established in various buildings, like hospitals, entertainment halls, universities, etc., and will provide accuracy, speed, and privacy for data collected in the detection of viruses.
About Me
Vasileios A. Memos received the BSc degree in Technology Management and the MSc degree in Applied Informatics with specialization in Computer Systems and Network Technologies from the School of Information Sciences, University of Macedonia (Thessaloniki, Greece), in 2012 and 2017 respectively. In July 2022, he received his Ph.D. degree specializing in “Novel Algorithms, Models and Architectures for Enhanced Cyber Security and Energy Efficiency in Internet of Things Environments” from the Dept. of Applied Informatics, University of Macedonia (Thessaloniki, Greece), where he had teaching experience for three academic years (2018-2021). He has also teaching experience in higher education in departments of the International University of Greece (2022-2023), the University of Western Macedonia (2023-2024), and the Democritus University of Thrace (2024-current). From 2021 until today, he has been working with the University of Derby (UK) as a Lecturer in the Departments of Computer Science, Computer Networks & Security, and Cyber Security of the School of Computing at Mediterranean College in Thessaloniki. His research interests include Computer Networks, Cloud Computing (CC), Big Data Analytics (BDA), Wireless Sensor Networks (WSN), Internet of Things (IoT), Artificial Intelligence (AI), Cyber Security, Cryptography, Privacy and Security Software Testing. He is also a member of the Research Group Mobility2Net Lab JAPAN-EU R&D of the Director Dr. Konstantinos Psannis.
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