3 papers
cs.LG2025
Evaluation of Bio-Inspired Models under Different Learning Settings For Energy Efficiency in Network Traffic Prediction
Theodoros Tsiolakis, Nikolaos Pavlidis, Vasileios Perifanis +1
Cellular traffic forecasting is a critical task that enables network operators to efficiently allocate resources and address anomalies in rapidly evolving environments. The exponen…
cs.LG2024
Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting
Nikolaos Pavlidis, Vasileios Perifanis, Selim F. Yilmaz +6
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 t…
cs.CY2024
Federated Anomaly Detection for Early-Stage Diagnosis of Autism Spectrum Disorders using Serious Game Data
Nikolaos Pavlidis, Vasileios Perifanis, Eleni Briola +4
Early identification of Autism Spectrum Disorder (ASD) is considered critical for effective intervention to mitigate emotional, financial and societal burdens. Although ASD belongs…