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Armin Salimi-Badr
Armin Salimi-Badr
Assistant Professor of Computer Science and Engineering, Shahid Beheshti University
Verified email at sbu.ac.ir - Homepage
Title
Cited by
Cited by
Year
IC-FNN: a novel fuzzy neural network with interpretable, intuitive, and correlated-contours fuzzy rules for function approximation
MM Ebadzadeh, A Salimi-Badr
IEEE Transactions on Fuzzy Systems 26 (3), 1288-1302, 2017
612017
CFNN: Correlated fuzzy neural network
MM Ebadzadeh, A Salimi-Badr
Neurocomputing 148, 430-444, 2015
512015
A novel self-organizing fuzzy neural network to learn and mimic habitual sequential tasks
A Salimi-Badr, MM Ebadzadeh
IEEE Transactions on Cybernetics 52 (1), 323 - 332, 2022
342022
IT2CFNN: An interval type-2 correlation-aware fuzzy neural network to construct non-separable fuzzy rules with uncertain and adaptive shapes for nonlinear function approximation
A Salimi-Badr
Applied Soft Computing 115, 108258, 2022
192022
A novel learning algorithm based on computing the rules’ desired outputs of a TSK fuzzy neural network with non-separable fuzzy rules
A Salimi-Badr, MM Ebadzadeh
Neurocomputing 470, 139-153, 2022
182022
Fuzzy neuronal model of motor control inspired by cerebellar pathways to online and gradually learn inverse biomechanical functions in the presence of delay
A Salimi-Badr, MM Ebadzadeh, C Darlot
Biological cybernetics 111, 421-438, 2017
182017
A system-level mathematical model of Basal Ganglia motor-circuit for kinematic planning of arm movements
A Salimi-Badr, MM Ebadzadeh, C Darlot
Computers in biology and medicine 92, 78-89, 2018
112018
A possible correlation between the basal ganglia motor function and the inverse kinematics calculation
A Salimi-Badr, MM Ebadzadeh, C Darlot
Journal of Computational Neuroscience 43, 295-318, 2017
102017
A type-2 neuro-fuzzy system with a novel learning method for Parkinson’s disease diagnosis
A Salimi-Badr, M Hashemi, H Saffari
Applied Intelligence 53 (12), 15656-15682, 2023
92023
A Neural-Based Approach to Aid Early Parkinson's Disease Diagnosis
A Salimi-Badr, M Hashemi
2020 11th international conference on information and knowledge technology …, 2020
72020
A systematic embedded software design flow for robotic applications
N Mahdian, SH Attarzadeh-Niaki, A Salimi-Badr
2021 11th International Conference on Computer Engineering and Knowledge …, 2021
42021
ENF-S: An Evolutionary-Neuro-Fuzzy Multi-Objective Task Scheduler for Heterogeneous Multi-core Processors
A Abdi, A Salimi-Badr
IEEE Transactions on Sustainable Computing 8 (3), 479 - 491, 2023
32023
Ros-based co-simulation for formal cyber-physical robotic system design
M Vazirpanah, SH Attarzadeh-Niaki, A Salimi-Badr
2022 27th International Computer Conference, Computer Society of Iran (CSICC …, 2022
32022
Backpropagation-free learning method for correlated fuzzy neural networks
A Salimi-Badr, MM Ebadzadeh
arXiv preprint arXiv:2012.01935, 2020
32020
An efficient planning method for autonomous navigation of a wheeled-robot based on deep reinforcement learning
AS Sadr, MS Khojasteh, H Malek, A Salimi-Badr
2022 12th International Conference on Computer and Knowledge Engineering …, 2022
22022
An Explainable Deep Learning-Based Method For Schizophrenia Diagnosis Using Generative Data-Augmentation
M Saadatinia, A Salimi-Badr
arXiv preprint arXiv:2310.16867, 2023
12023
Early Parkinson's Disease Diagnosis Based on Sequence Analysis
A Salimi-Badr, M Hashemi
Journal of Innovations in Computer Science and Engineering (JICSE), 1-10, 2023
12023
A novel evolutionary-based neuro-fuzzy task scheduling approach to jointly optimize the main design challenges of heterogeneous MPSoCs
A Abdi, A Salimi-Badr
arXiv preprint arXiv:2203.14717, 2022
12022
Integrative Deep Learning Framework for Parkinson's Disease Early Detection using Gait Cycle Data Measured by Wearable Sensors: A CNN-GRU-GNN Approach
A Rashnu, A Salimi-Badr
arXiv preprint arXiv:2404.15335, 2024
2024
A data-driven implicit deep adaptive neuro-fuzzy inference system capable of manifold learning for function approximation
A Salimi-Badr
Applied Soft Computing, 111458, 2024
2024
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