Alireza Momenzadeh

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 15

Top Topics

Dynamic Optimization

Top Venues

Expert Syst. Appl.

J. Supercomput.

Publications

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) (2024)
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 (2024)
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 (2023)
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) (2023)
Enzo Baccarelli, Michele Scarpiniti, Alireza Momenzadeh, Sima Sarv Ahrabi
erated learning for IoT stream applications. Comput. Commun. 195 (2022)
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) (2022)
Enzo Baccarelli, Michele Scarpiniti, Alireza Momenzadeh, Sima Sarv Ahrabi
AFAFed - Protocol analysis. CoRR (2022)
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 (2022)
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 (2021)
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) (2021)
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 (2020)
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 (2019)
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 (2019)
Enzo Baccarelli, Michele Scarpiniti, Alireza Momenzadeh
Fog-supported delay-constrained energy-saving live migration of VMs over MultiPath TCP/IP 5G connections. CoRR (2018)
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 (2018)