Zobrazeno 1 - 10
of 231
pro vyhledávání: '"Spyros Sioutas"'
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:
Sensors, Vol 24, Iss 19, p 6211 (2024)
In the last few years, the agricultural field has undergone a digital transformation, incorporating artificial intelligence systems to make good employment of the growing volume of data from various sources and derive value from it. Within artificial
Externí odkaz:
https://doaj.org/article/7313373110334a6a8651dbd31621606a
Autor:
Konstantinos C. Giotopoulos, Dimitrios Michalopoulos, Gerasimos Vonitsanos, Dimitris Papadopoulos, Ioanna Giannoukou, Spyros Sioutas
Publikováno v:
Information, Vol 15, Iss 6, p 335 (2024)
Workload management is a cornerstone of contemporary human resource management with widespread applications in private and public sectors. The challenges in human resource management are particularly pronounced within the public sector: particularly
Externí odkaz:
https://doaj.org/article/0c2bf7d9766f46fb8140533bb0e46e37
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