Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Efraimidis, P. S."'
Autor:
Pavlidis, Nikolaos, Perifanis, Vasileios, Yilmaz, Selim F., Wilhelmi, Francesc, Miozzo, Marco, Efraimidis, Pavlos S., Koutsiamanis, Remous-Aris, Mulinka, Pavol, Dini, Paolo
The increasing demand for efficient resource allocation in mobile networks has catalyzed the exploration of innovative solutions that could enhance the task of real-time cellular traffic prediction. Under these circumstances, federated learning (FL)
Externí odkaz:
http://arxiv.org/abs/2412.04081
Autor:
Pavlidis, Nikolaos, Perifanis, Vasileios, Briola, Eleni, Nikolaidis, Christos-Chrysanthos, Katsiri, Eleftheria, Efraimidis, Pavlos S., Filippidou, Despina Elisabeth
Early identification of Autism Spectrum Disorder (ASD) is considered critical for effective intervention to mitigate emotional, financial and societal burdens. Although ASD belongs to a group of neurodevelopmental disabilities that are not curable, r
Externí odkaz:
http://arxiv.org/abs/2410.20003
Autor:
Pavlidis, Nikolaos, Perifanis, Vasileios, Chatzinikolaou, Theodoros Panagiotis, Sirakoulis, Georgios Ch., Efraimidis, Pavlos S.
Federated Learning (FL) has emerged as a promising solution for privacy-enhancement and latency minimization in various real-world applications, such as transportation, communications, and healthcare. FL endeavors to bring Machine Learning (ML) down
Externí odkaz:
http://arxiv.org/abs/2310.00627
Autor:
Perifanis, Vasileios, Pavlidis, Nikolaos, Yilmaz, Selim F., Wilhelmi, Francesc, Guerra, Elia, Miozzo, Marco, Efraimidis, Pavlos S., Dini, Paolo, Koutsiamanis, Remous-Aris
Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network design, resource allocation and anomaly mitigation. Although machine
Externí odkaz:
http://arxiv.org/abs/2309.10645
Autor:
Nikolaidis, Christos Chrysanthos, Perifanis, Vasileios, Pavlidis, Nikolaos, Efraimidis, Pavlos S.
The provision of social care applications is crucial for elderly people to improve their quality of life and enables operators to provide early interventions. Accurate predictions of user dropouts in healthy ageing applications are essential since th
Externí odkaz:
http://arxiv.org/abs/2309.04311
Autor:
Perifanis, Vasileios, Michailidi, Ioanna, Stamatelatos, Giorgos, Drosatos, George, Efraimidis, Pavlos S.
In this work, we present a machine learning approach for predicting early dropouts of an active and healthy ageing app. The presented algorithms have been submitted to the IFMBE Scientific Challenge 2022, part of IUPESM WC 2022. We have processed the
Externí odkaz:
http://arxiv.org/abs/2308.00539
Publikováno v:
Computer Networks, 109950, 2023
Cellular traffic prediction is of great importance on the path of enabling 5G mobile networks to perform intelligent and efficient infrastructure planning and management. However, available data are limited to base station logging information. Hence,
Externí odkaz:
http://arxiv.org/abs/2211.15220
With the growing number of Location-Based Social Networks, privacy preserving location prediction has become a primary task for helping users discover new points-of-interest (POIs). Traditional systems consider a centralized approach that requires th
Externí odkaz:
http://arxiv.org/abs/2112.11134
This paper presents the development of a new class of algorithms that accurately implement the preferential attachment mechanism of the Barab\'asi-Albert (BA) model to generate scale-free graphs. Contrary to existing approximate preferential attachme
Externí odkaz:
http://arxiv.org/abs/2110.00287
In this work, we present a federated version of the state-of-the-art Neural Collaborative Filtering (NCF) approach for item recommendations. The system, named FedNCF, enables learning without requiring users to disclose or transmit their raw data. Da
Externí odkaz:
http://arxiv.org/abs/2106.04405