Fog-Supported Delay-Constrained Energy-Saving Live Migration of VMs Over MultiPath TCP/IP 5G Connections. - Enzo Baccarelli; Michele Scarpiniti; Alireza Momenzadeh (IEEE Access, 2018)

EcoMobiFog-Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications. - Enzo Baccarelli; Michele Scarpiniti; Alireza Momenzadeh (IEEE Access, 2019)

Optimized training and scalable implementation of Conditional Deep Neural Networks with early exits for Fog-supported IoT applications. - Enzo Baccarelli; Simone Scardapane; Michele Scarpiniti; Alireza Momenzadeh; Aurelio Uncini (Inf. Sci., 2020)

Learning-in-the-Fog (LiFo): Deep Learning Meets Fog Computing for the Minimum-Energy Distributed Early-Exit of Inference in Delay-Critical IoT Realms. - Enzo Baccarelli; Michele Scarpiniti; Alireza Momenzadeh; Sima Sarv Ahrabi (IEEE Access, 2021)

An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease. - Sima Sarv Ahrabi; Michele Scarpiniti; Enzo Baccarelli; Alireza Momenzadeh (Comput., 2021)

AFAFed - Asynchronous Fair Adaptive Federated learning for IoT stream applications. - Enzo Baccarelli; Michele Scarpiniti; Alireza Momenzadeh; Sima Sarv Ahrabi (Comput. Commun., 2022)

AFAFed - Protocol analysis. - Enzo Baccarelli; Michele Scarpiniti; Alireza Momenzadeh; Sima Sarv Ahrabi (CoRR, 2022)

A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection. - Michele Scarpiniti; Sima Sarv Ahrabi; Enzo Baccarelli; Lorenzo Piazzo; Alireza Momenzadeh (Expert Syst. Appl., 2022)

Exploiting probability density function of deep convolutional autoencoders' latent space for reliable COVID-19 detection on CT scans. - Sima Sarv Ahrabi; Lorenzo Piazzo; Alireza Momenzadeh; Michele Scarpiniti; Enzo Baccarelli (J. Supercomput., 2022)

How much BiGAN and CycleGAN-learned hidden features are effective for COVID-19 detection from CT images? A comparative study. - Sima Sarv Ahrabi; Alireza Momenzadeh; Enzo Baccarelli; Michele Scarpiniti; Lorenzo Piazzo (J. Supercomput., 2023)

Twinned Residual Auto-Encoder (TRAE) - A new DL architecture for denoising super-resolution and task-aware feature learning from COVID-19 CT images. - Enzo Baccarelli; Michele Scarpiniti; Alireza Momenzadeh (Expert Syst. Appl., 2023)

Energy-minimizing 3D circular trajectory optimization of rotary-wing UAV under probabilistic path-loss in constrained hotspot environments. - Enzo Baccarelli; Michele Scarpiniti; Alireza Momenzadeh (Veh. Commun., 2024)

Multi-resolution Twinned Residual Auto-Encoders (MR-TRAE) - A Novel DL Model for Image Multi-resolution. - Alireza Momenzadeh; Enzo Baccarelli; Michele Scarpiniti; Sima Sarv Ahrabi (Cogn. Comput., 2024)