Optimal inference of hidden Markov models through expert-acquired data
This paper focuses on inferring a general class of hidden Markov models (HMMs) using data
acquired from experts. Expert-acquired data contain decisions/actions made by …
acquired from experts. Expert-acquired data contain decisions/actions made by …
An optimal Bayesian intervention policy in response to unknown dynamic cell stimuli
SH Hosseini, M Imani - Information Sciences, 2024 - Elsevier
Interventions in gene regulatory networks (GRNs) aim to restore normal functions of cells
experiencing abnormal behavior, such as uncontrolled cell proliferation. The dynamic …
experiencing abnormal behavior, such as uncontrolled cell proliferation. The dynamic …
Deep reinforcement learning sensor scheduling for effective monitoring of dynamical systems
Advances in technology have enabled the use of sensors with varied modalities to monitor
different parts of systems, each providing diverse levels of information about the underlying …
different parts of systems, each providing diverse levels of information about the underlying …
[PDF][PDF] Optimal detection for Bayesian attack graphs under uncertainty in monitoring and reimaging
A Kazeminajafabadi, SF Ghoreishi… - 2023 American Control …, 2024 - researchgate.net
Bayesian attack graphs (BAGs) are powerful models to capture the time-varying progression
of attacks in complex interconnected networks. Network elements are modeled by graph …
of attacks in complex interconnected networks. Network elements are modeled by graph …
[PDF][PDF] Implicit human perception learning in complex and unknown environments
Autonomy through humans and autonomous agents becomes more prevalent in many
complex domains, including time-sensitive and unknown environments. Examples include …
complex domains, including time-sensitive and unknown environments. Examples include …
Optimal Joint Defense and Monitoring for Networks Security under Uncertainty: A POMDP‐Based Approach
A Kazeminajafabadi, M Imani - IET Information Security, 2024 - Wiley Online Library
The increasing interconnectivity in our infrastructure poses a significant security challenge,
with external threats having the potential to penetrate and propagate throughout the …
with external threats having the potential to penetrate and propagate throughout the …
Structure-based inverse reinforcement learning for quantification of biological knowledge
Gene regulatory networks (GRNs) play crucial roles in various cellular processes, including
stress response, DNA repair, and the mechanisms involved in complex diseases such as …
stress response, DNA repair, and the mechanisms involved in complex diseases such as …
Learning to fight against cell stimuli: A game theoretic perspective
SH Hosseini, M Imani - 2023 IEEE conference on artificial …, 2023 - ieeexplore.ieee.org
Current genomics interventions have limitations in accounting for cell stimuli and the
dynamic response to intervention. Although genomic sequencing and analysis have led to …
dynamic response to intervention. Although genomic sequencing and analysis have led to …
Modeling Defensive Response of Cells to Therapies: Equilibrium Interventions for Regulatory Networks
SH Hosseini, M Imani - IEEE/ACM Transactions on …, 2024 - ieeexplore.ieee.org
A major objective in genomics is to design interventions that can shift undesirable behaviors
of such systems (ie, those associated with cancers) into desirable ones. Several intervention …
of such systems (ie, those associated with cancers) into desirable ones. Several intervention …