AIMC Topic: Markov Chains

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Reinforcement Learning for Bioretrosynthesis.

ACS synthetic biology
Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are requi...

Finite-time nonfragile time-varying proportional retarded synchronization for Markovian Inertial Memristive NNs with reaction-diffusion items.

Neural networks : the official journal of the International Neural Network Society
The issue of synchronization for a class of inertial memristive neural networks over a finite-time interval is investigated in this paper. Specifically, reaction-diffusion items and Markovian jump parameters are both considered in the system model, m...

Finite-time and fixed-time anti-synchronization of Markovian neural networks with stochastic disturbances via switching control.

Neural networks : the official journal of the International Neural Network Society
This paper proposes a unified theoretical framework to study the problem of finite/fixed-time drive-response anti-synchronization for a class of Markovian stochastic neural networks. State feedback switching controllers without the sign function are ...

Quasi-Synchronization of Time Delay Markovian Jump Neural Networks With Impulsive-Driven Transmission and Fading Channels.

IEEE transactions on cybernetics
The problem of quasi-synchronization (QS) for the Markovian jump master-slave neural networks with time-varying delay is studied in this article, where the mismatch parameters and unreliable communication channels are considered as well. A set of sto...

Centralized/decentralized event-triggered pinning synchronization of stochastic coupled networks with noise and incomplete transitional rate.

Neural networks : the official journal of the International Neural Network Society
This paper studies the synchronous problem of Markovian switching complex networks associated with partly unknown transitional rates, stochastic noise, and randomly coupling strength. In order to achieve the synchronization for these array networks, ...

Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery.

Circulation. Cardiovascular quality and outcomes
BACKGROUND: The ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scal...

Hierarchical Hidden Markov models enable accurate and diverse detection of antimicrobial resistance sequences.

Communications biology
The characterization of antimicrobial resistance genes from high-throughput sequencing data has become foundational in public health research and regulation. This requires mapping sequence reads to databases of known antimicrobial resistance genes to...

Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unpreceden...

Exponential synchronization of semi-Markovian coupled neural networks with mixed delays via tracker information and quantized output controller.

Neural networks : the official journal of the International Neural Network Society
In this paper, exponential synchronization of semi-Markovian coupled neural networks (NNs) with bounded time-varying delay and infinite-time distributed delay (mixed delays) is investigated. Since semi-Markov switching occurs by time-varying probabil...

An Automatic Approach Using ELM Classifier for HFpEF Identification Based on Heart Sound Characteristics.

Journal of medical systems
Heart failure with preserved ejection fraction (HFpEF) is a complex and heterogeneous clinical syndrome. For the purpose of assisting HFpEF diagnosis, a non-invasive method using extreme learning machine and heart sound (HS) characteristics was provi...