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ORCIDEnzo Baccarelli
- > Home > Persons > Enzo Baccarelli
Publications
- 2024
[j83]Alireza Momenzadeh, Enzo Baccarelli, Michele Scarpiniti, Sima Sarv Ahrabi
:
Multi-resolution Twinned Residual Auto-Encoders (MR-TRAE) - A Novel DL Model for Image Multi-resolution. Cogn. Comput. 16(4): 1447-1469 (2024)- [j82]Enzo Baccarelli, Michele Scarpiniti, Alireza Momenzadeh:
Energy-minimizing 3D circular trajectory optimization of rotary-wing UAV under probabilistic path-loss in constrained hotspot environments. Veh. Commun. 46: 100730 (2024) - 2023
- [j81]Enzo Baccarelli, Michele Scarpiniti, Alireza Momenzadeh:
Twinned Residual Auto-Encoder (TRAE) - A new DL architecture for denoising super-resolution and task-aware feature learning from COVID-19 CT images. Expert Syst. Appl. 225: 120104 (2023) - [j80]Sima Sarv Ahrabi, Alireza Momenzadeh, Enzo Baccarelli, Michele Scarpiniti, Lorenzo Piazzo:
How much BiGAN and CycleGAN-learned hidden features are effective for COVID-19 detection from CT images? A comparative study. J. Supercomput. 79(3): 2850-2881 (2023) - 2022
- [j79]Enzo Baccarelli, Michele Scarpiniti, Alireza Momenzadeh, Sima Sarv Ahrabi:
AFAFed - Asynchronous Fair Adaptive Federated learning for IoT stream applications. Comput. Commun. 195: 376-402 (2022) - [j78]Michele Scarpiniti, Sima Sarv Ahrabi, Enzo Baccarelli, Lorenzo Piazzo, Alireza Momenzadeh:
A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection. Expert Syst. Appl. 192: 116366 (2022) - [j77]Sima Sarv Ahrabi, Lorenzo Piazzo, Alireza Momenzadeh, Michele Scarpiniti, Enzo Baccarelli:
Exploiting probability density function of deep convolutional autoencoders' latent space for reliable COVID-19 detection on CT scans. J. Supercomput. 78(9): 12024-12045 (2022) - [i6]Enzo Baccarelli, Michele Scarpiniti, Alireza Momenzadeh, Sima Sarv Ahrabi:
AFAFed - Protocol analysis. CoRR abs/2206.14927 (2022) - 2021
- [j76]Enzo Baccarelli, Michele Scarpiniti, Alireza Momenzadeh, Sima Sarv Ahrabi:
Learning-in-the-Fog (LiFo): Deep Learning Meets Fog Computing for the Minimum-Energy Distributed Early-Exit of Inference in Delay-Critical IoT Realms. IEEE Access 9: 25716-25757 (2021) - [j75]Sima Sarv Ahrabi, Michele Scarpiniti, Enzo Baccarelli, Alireza Momenzadeh:
An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease. Comput. 9(1): 3 (2021) - 2020
- [j73]Enzo Baccarelli, Simone Scardapane, Michele Scarpiniti, Alireza Momenzadeh, Aurelio Uncini:
Optimized training and scalable implementation of Conditional Deep Neural Networks with early exits for Fog-supported IoT applications. Inf. Sci. 521: 107-143 (2020) - 2019
- [j72]Enzo Baccarelli, Michele Scarpiniti, Alireza Momenzadeh:
EcoMobiFog-Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications. IEEE Access 7: 55565-55608 (2019) - [i3]Enzo Baccarelli, Michele Scarpiniti, Alireza Momenzadeh:
EcoMobiFog - Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications. CoRR abs/1906.07578 (2019) - 2018
- [j70]Enzo Baccarelli, Michele Scarpiniti, Alireza Momenzadeh:
Fog-Supported Delay-Constrained Energy-Saving Live Migration of VMs Over MultiPath TCP/IP 5G Connections. IEEE Access 6: 42327-42354 (2018) - [i2]Enzo Baccarelli, Michele Scarpiniti, Alireza Momenzadeh:
Fog-supported delay-constrained energy-saving live migration of VMs over MultiPath TCP/IP 5G connections. CoRR abs/1805.12509 (2018)
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