AIMC Topic: Computer Simulation

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Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study.

Reproductive biology and endocrinology : RB&E
BACKGROUND: To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to imp...

Bipartite Synchronization of Multiple Memristor-Based Neural Networks With Antagonistic Interactions.

IEEE transactions on neural networks and learning systems
In this article, by introducing a signed graph to describe the coopetition interactions among network nodes, the mathematical model of multiple memristor-based neural networks (MMNNs) with antagonistic interactions is established. Since the cooperati...

Finite-Time Tracking Control for Nonlinear Systems via Adaptive Neural Output Feedback and Command Filtered Backstepping.

IEEE transactions on neural networks and learning systems
This article is concerned with the tracking control problem for uncertain high-order nonlinear systems in the presence of input saturation. A finite-time control strategy combined with neural state observer and command filtered backstepping is propos...

Impulsive Synchronization of Unbounded Delayed Inertial Neural Networks With Actuator Saturation and Sampled-Data Control and its Application to Image Encryption.

IEEE transactions on neural networks and learning systems
The article considers the impulsive synchronization for inertial neural networks with unbounded delay and actuator saturation via sampled-data control. Based on an impulsive differential inequality, the difficulties caused by unbounded delay and impu...

A deep learning based surrogate model for the parameter identification problem in probabilistic cellular automaton epidemic models.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: an accurate estimation of the epidemiological model coefficients helps understand the basic principles of disease spreading. Some studies showed that dozens of hours are needed to simulate the traditional probabilistic cellu...

Balancing Data on Deep Learning-Based Proteochemometric Activity Classification.

Journal of chemical information and modeling
In silico analysis of biological activity data has become an essential technique in pharmaceutical development. Specifically, the so-called proteochemometric models aim to share information between targets in machine learning ligand-target activity p...

Automated optimization of double heater convective polymerase chain reaction devices based on CFD simulation database and artificial neural network model.

Biomedical microdevices
This paper presents a framework for automated optimization of double-heater convective PCR (DH-cPCR) devices by developing a computational fluid dynamics (CFD) simulation database and artificial neural network (ANN) model. The optimization parameter ...

A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data.

Journal of biomedical informatics
BACKGROUND: Machine learning methodologies are gaining popularity for developing medical prediction models for datasets with a large number of predictors, particularly in the setting of clustered and longitudinal data. Binary Mixed Model (BiMM) fores...

Synchronization of memristive neural networks with unknown parameters via event-triggered adaptive control.

Neural networks : the official journal of the International Neural Network Society
This paper considers the drive-response synchronization of memristive neural networks (MNNs) with unknown parameters, where the unbounded discrete and bounded distributed time-varying delays are involved. Aiming at the unknown parameters of MNNs, the...

Deep Artificial Neural Networks Reveal a Distributed Cortical Network Encoding Propositional Sentence-Level Meaning.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Understanding how and where in the brain sentence-level meaning is constructed from words presents a major scientific challenge. Recent advances have begun to explain brain activation elicited by sentences using vector models of word meaning derived ...