7 citations · 17 across the 6 of their papers we have counts for
6 papers
Hybrid Indoor Localization via Reinforcement Learning-based Information Fusion
Mohammad Salimibeni, Arash Mohammadi
The paper is motivated by the importance of the Smart Cities (SC) concept for future management of global urbanization. Among all Internet of Things (IoT)-based communication techn…
AKF-SR: Adaptive Kalman Filtering-based Successor Representation
Parvin Malekzadeh, Mohammad Salimibeni, Ming Hou +2
Recent studies in neuroscience suggest that Successor Representation (SR)-based models provide adaptation to changes in the goal locations or reward function faster than model-free…
Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation
Mohammad Salimibeni, Arash Mohammadi, Parvin Malekzadeh +1
Distributed Multi-Agent Reinforcement Learning (MARL) algorithms has attracted a surge of interest lately mainly due to the recent advancements of Deep Neural Networks (DNNs). Conv…
Online Dynamic Window (ODW) Assisted Two-stage LSTM Frameworks for Indoor Localization
Mohammadamin Atashi, Mohammad Salimibeni, Arash Mohammadi
Internet of Things (IoT)-based indoor localization has gained significant popularity recently to satisfy the ever-increasing requirements of indoor Location-based Services (LBS). I…
TB-ICT: A Trustworthy Blockchain-Enabled System for Indoor COVID-19 Contact Tracing
Mohammad Salimibeni, Zohreh Hajiakhondi-Meybodi, Arash Mohammadi +1
Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased to prevent spread of this highly contagious virus and be…
MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement Learning
Parvin Malekzadeh, Mohammad Salimibeni, Arash Mohammadi +2
There has been an increasing surge of interest on development of advanced Reinforcement Learning (RL) systems as intelligent approaches to learn optimal control policies directly f…