Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Christos Karras"'
Publikováno v:
Future Internet, Vol 16, Iss 10, p 370 (2024)
Federated learning enables model training on multiple clients locally, without the need to transfer their data to a central server, thus ensuring data privacy. In this paper, we investigate the impact of Non-Independent and Identically Distributed (n
Externí odkaz:
https://doaj.org/article/a9d76eceff6a484798ef3f2012c5d56d
Publikováno v:
Information, Vol 15, Iss 8, p 450 (2024)
The mode is a fundamental descriptive statistic in data analysis, signifying the most frequent element within a dataset. The range mode query (RMQ) problem expands upon this concept by preprocessing an array A containing n natural numbers. This allow
Externí odkaz:
https://doaj.org/article/e764481c857d49c79e1d153bb027cab4
Autor:
Anastasios Giannaros, Aristeidis Karras, Leonidas Theodorakopoulos, Christos Karras, Panagiotis Kranias, Nikolaos Schizas, Gerasimos Kalogeratos, Dimitrios Tsolis
Publikováno v:
Journal of Cybersecurity and Privacy, Vol 3, Iss 3, Pp 493-543 (2023)
Autonomous vehicles (AVs), defined as vehicles capable of navigation and decision-making independent of human intervention, represent a revolutionary advancement in transportation technology. These vehicles operate by synthesizing an array of sophist
Externí odkaz:
https://doaj.org/article/e1bcb3f53d08491c85ce60507ae93dc1
Autor:
Aristeidis Karras, Anastasios Giannaros, Christos Karras, Leonidas Theodorakopoulos, Constantinos S. Mammassis, George A. Krimpas, Spyros Sioutas
Publikováno v:
Future Internet, Vol 16, Iss 2, p 42 (2024)
In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introdu
Externí odkaz:
https://doaj.org/article/a918eb932df64d318e9a8e5baf3e4f9c
Autor:
Eleni Vlachou, Aristeidis Karras, Christos Karras, Leonidas Theodorakopoulos, Constantinos Halkiopoulos, Spyros Sioutas
Publikováno v:
Big Data and Cognitive Computing, Vol 8, Iss 1, p 1 (2023)
In this work, we present a Distributed Bayesian Inference Classifier for Large-Scale Systems, where we assess its performance and scalability on distributed environments such as PySpark. The presented classifier consistently showcases efficient infer
Externí odkaz:
https://doaj.org/article/9830756321bf4e8b910f0ebdd7b9dd4e
Autor:
Christos-Panagiotis Balatsouras, Aristeidis Karras, Christos Karras, Ioannis Karydis, Spyros Sioutas
Publikováno v:
Sensors, Vol 23, Iss 23, p 9486 (2023)
In the evolving landscape of Industry 4.0, the convergence of peer-to-peer (P2P) systems, LoRa-enabled wireless sensor networks (WSNs), and distributed hash tables (DHTs) represents a major advancement that enhances sustainability in the modern agric
Externí odkaz:
https://doaj.org/article/94d057f741ba43a3a714047441ed91a9
Autor:
Aristeidis Karras, Christos Karras, Spyros Sioutas, Christos Makris, George Katselis, Ioannis Hatzilygeroudis, John A. Theodorou, Dimitrios Tsolis
Publikováno v:
Information, Vol 14, Iss 11, p 583 (2023)
This study explores the design and capabilities of a Geographic Information System (GIS) incorporated with an expert knowledge system, tailored for tracking and monitoring the spread of dangerous diseases across a collection of fish farms. Specifical
Externí odkaz:
https://doaj.org/article/4025562608b84921bd54ce038b5076eb
Publikováno v:
Information, Vol 14, Iss 8, p 451 (2023)
In this work, we introduce an innovative Markov Chain Monte Carlo (MCMC) classifier, a synergistic combination of Bayesian machine learning and Apache Spark, highlighting the novel use of this methodology in the spectrum of big data management and en
Externí odkaz:
https://doaj.org/article/6ff9f1dcb1214578a3fc4a56759dae69
Autor:
Aristeidis Karras, Christos Karras, Konstantinos C. Giotopoulos, Dimitrios Tsolis, Konstantinos Oikonomou, Spyros Sioutas
Publikováno v:
Information, Vol 14, Iss 7, p 414 (2023)
Federated learning (FL) has emerged as a promising technique for preserving user privacy and ensuring data security in distributed machine learning contexts, particularly in edge intelligence and edge caching applications. Recognizing the prevalent c
Externí odkaz:
https://doaj.org/article/55ffe8c229d84bb8bf3d0afd8b8d51b7
Autor:
Christos Karras, Aristeidis Karras, Konstantinos C. Giotopoulos, Markos Avlonitis, Spyros Sioutas
Publikováno v:
Algorithms, Vol 16, Iss 5, p 245 (2023)
In the context of big-data analysis, the clustering technique holds significant importance for the effective categorization and organization of extensive datasets. However, pinpointing the ideal number of clusters and handling high-dimensional data c
Externí odkaz:
https://doaj.org/article/e63e3c88731341b4bb13d8960df77a7f