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Sima Sinaei
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2020 – today
- 2024
- [j6]Samaneh Mohammadi, Ali Balador, Sima Sinaei, Francesco Flammini:
Balancing privacy and performance in federated learning: A systematic literature review on methods and metrics. J. Parallel Distributed Comput. 192: 104918 (2024) - [j5]Rakesh Shrestha, Mohammadreza Mohammadi, Sima Sinaei, Alberto Salcines, David Pampliega, Raul Clemente, Ana Lourdes Sanz, Ehsan Nowroozi, Anders Lindgren:
Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid. J. Parallel Distributed Comput. 193: 104951 (2024) - [c18]Sima Sinaei, Mohammadreza Mohammadi, Rakesh Shrestha, Mina Alibeigi, David Eklund:
PRIV-DRIVE: Privacy-Ensured Federated Learning using Homomorphic Encryption for Driver Fatigue Detection. DSD 2024: 427-434 - [c17]Mohammadreza Mohammadi, Rakesh Shrestha, Sima Sinaei:
Integrating Federated Learning and Differential Privacy for Secure Anomaly Detection in Smart Grids. ICCBDC 2024: 60-66 - 2023
- [c16]Samaneh Mohammadi, Sima Sinaei, Ali Balador, Francesco Flammini:
Optimized Paillier Homomorphic Encryption in Federated Learning for Speech Emotion Recognition. COMPSAC 2023: 1021-1022 - [c15]Mohammadreza Mohammadi, Roberto Allocca, David Eklund, Rakesh Shrestha, Sima Sinaei:
Privacy-preserving Federated Learning System for Fatigue Detection. CSR 2023: 624-629 - [c14]Samaneh Mohammadi, Mohammadreza Mohammadi, Sima Sinaei, Ali Balador, Ehsan Nowroozi, Francesco Flammini, Mauro Conti:
Balancing Privacy and Accuracy in Federated Learning for Speech Emotion Recognition. FedCSIS 2023: 191-199 - [c13]Kateryna Mishchenko, Samaneh Mohammadi, Mohammadreza Mohammadi, Sima Sinaei:
Hyperparameters Optimization for Federated Learning System: Speech Emotion Recognition Case Study. FMEC 2023: 80-86 - [c12]Mohammadreza Mohammadi, Rakesh Shrestha, Sima Sinaei, Alberto Salcines, David Pampliega, Raul Clemente, Ana Lourdes Sanz:
Anomaly Detection Using LSTM-Autoencoder in Smart Grid: A Federated Learning Approach. ICCBDC 2023: 48-54 - [c11]Samaneh Mohammadi, Sima Sinaei, Ali Balador, Francesco Flammini:
Secure and Efficient Federated Learning by Combining Homomorphic Encryption and Gradient Pruning in Speech Emotion Recognition. ISPEC 2023: 1-16 - 2022
- [j4]Seyed Ahmad Mirsalari, Najmeh Nazari, Sima Sinaei, Mostafa E. Salehi, Masoud Daneshtalab:
FaCT-LSTM: Fast and Compact Ternary Architecture for LSTM Recurrent Neural Networks. IEEE Des. Test 39(3): 45-53 (2022) - [i4]Ali Balador, Sima Sinaei, Mats Pettersson:
Artificial Intelligence Enabled Distributed Edge Computing for Internet of Things. ERCIM News 2022(129): 0 (2022) - 2021
- [c10]Vahid Geraeinejad, Sima Sinaei, Mehdi Modarressi, Masoud Daneshtalab:
RoCo-NAS: Robust and Compact Neural Architecture Search. IJCNN 2021: 1-8 - [c9]Seyed Ahmad Mirsalari, Najmeh Nazari, Seyed Ali Ansarmohammadi, Sima Sinaei, Mostafa E. Salehi, Masoud Daneshtalab:
ELC-ECG: Efficient LSTM Cell for ECG Classification Based on Quantized Architecture. ISCAS 2021: 1-5 - [i3]Ali Sedaghatbaf, Sima Sinaei, Perttu Ranta-aho, Marko Koskinen:
DenDrift: A Drift-Aware Algorithm for Host Profiling. CoRR abs/2110.01221 (2021) - 2020
- [j3]Mohammad Loni, Sima Sinaei, Ali Zoljodi, Masoud Daneshtalab, Mikael Sjödin:
DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems. Microprocess. Microsystems 73: 102989 (2020) - [c8]Seyed Ahmad Mirsalari, Sima Sinaei, Mostafa E. Salehi, Masoud Daneshtalab:
MuBiNN: Multi-Level Binarized Recurrent Neural Network for EEG Signal Classification. ISCAS 2020: 1-5 - [c7]Najmeh Nazari, Seyed Ahmad Mirsalari, Sima Sinaei, Mostafa E. Salehi, Masoud Daneshtalab:
Multi-level Binarized LSTM in EEG Classification for Wearable Devices. PDP 2020: 175-181 - [i2]Seyed Ahmad Mirsalari, Sima Sinaei, Mostafa E. Salehi, Masoud Daneshtalab:
MuBiNN: Multi-Level Binarized Recurrent Neural Network for EEG signal Classification. CoRR abs/2004.08914 (2020) - [i1]Najmeh Nazari, Seyed Ahmad Mirsalari, Sima Sinaei, Mostafa E. Salehi, Masoud Daneshtalab:
Multi-level Binarized LSTM in EEG Classification for Wearable Devices. CoRR abs/2004.11206 (2020)
2010 – 2019
- 2019
- [j2]Sima Sinaei, Omid Fatemi:
Multi-objective algorithms for the application mapping problem in heterogeneous multiprocessor embedded system design. J. Supercomput. 75(8): 4150-4176 (2019) - [c6]Mohammad Loni, Ali Zoljodi, Sima Sinaei, Masoud Daneshtalab, Mikael Sjödin:
NeuroPower: Designing Energy Efficient Convolutional Neural Network Architecture for Embedded Systems. ICANN (1) 2019: 208-222 - 2018
- [j1]Sima Sinaei, Omid Fatemi:
Run-time mapping algorithm for dynamic workloads using association rule mining. J. Syst. Archit. 91: 1-10 (2018) - [c5]Sima Sinaei, Omid Fatemi:
Run-time Mapping Algorithm for Dynamic Workloads on Heterogeneous MPSoCs Platforms. DSD 2018: 373-380 - 2017
- [c4]Sima Sinaei, Andy D. Pimentel, Omid Fatemi:
Run-time resource allocation for embedded Multiprocessor System-on-Chip using tree-based design space exploration. DTIS 2017: 1-6 - [c3]Sima Sinaei, Omid Fatemi, Andy D. Pimentel:
Run-time mapping algorithm for dynamic workloads using process merging transformations. SAMOS 2017: 188-195 - 2016
- [c2]Sima Sinaei, Omid Fatemi:
Novel Heuristic Mapping Algorithms for Design Space Exploration of Multiprocessor Embedded Architectures. PDP 2016: 801-804 - 2011
- [c1]Sima Sinaei, Omid Fatemi:
An MOPSO method for mapping multimedia applications onto MP-SOC architectures. CCECE 2011: 1361-1364
Coauthor Index
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last updated on 2024-12-10 20:46 CET by the dblp team
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