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Found 7 papers, 1 papers with code
Hybrid Indoor Localization via Reinforcement Learning-based Information Fusion
The paper is motivated by the importance of the Smart Cities (SC) concept for future management of global urbanization.
AKF-SR: Adaptive Kalman Filtering-based Successor Representation
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 algorithms, together with lower computational cost compared to that of model-based algorithms.
Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation
The proposed MAK-TD/SR frameworks consider the continuous nature of the action-space that is associated with high dimensional multi-agent environments and exploit Kalman Temporal Difference (KTD) to address the parameter uncertainty.
Online Dynamic Window (ODW) Assisted Two-stage LSTM Frameworks for Indoor Localization
The second framework is developed based on a Signal Processing Dynamic Windowing (SP-DW) approach to further reduce the required processing time of the two-stage LSTM-based model.
TB-ICT: A Trustworthy Blockchain-Enabled System for Indoor COVID-19 Contact Tracing
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 prepared for the potential future ones.
COVID19-HPSMP: COVID-19 Adopted Hybrid and Parallel Deep Information Fusion Framework for Stock Price Movement Prediction
Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction (COVID19-HPSMP), innovative fusion strategies are used to combine scattered social media news related to COVID-19 with historical mark data.
MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement Learning
As a result, the proposed MM-KTD framework can learn the optimal policy with significantly reduced number of samples as compared to its DNN-based counterparts.
