AI Medical Compendium Topic

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Innovative Metaheuristic Optimization Approach with a Bi-Triad for Rehabilitation Exoskeletons.

Sensors (Basel, Switzerland)
The present work proposes a comprehensive metaheuristic methodology for the development of a medical robot for the upper limb rehabilitation, which includes the topological optimization of the device, kinematic models (5 DOF), human-robot interface, ...

Operating under the influence: the effect of alcohol on operative performance using a virtual robotic training platform-an experimental comparative cohort study.

Journal of robotic surgery
An elevated percentage of medical personnel reports using alcohol to relieve stress. Levels of alcohol addiction are almost double that of the general population. Robotic surgery is becoming more widespread. The purpose of this study is to evaluate t...

Observer-based resilient dissipativity control for discrete-time memristor-based neural networks with unbounded or bounded time-varying delays.

Neural networks : the official journal of the International Neural Network Society
This work focuses on the issue of observer-based resilient dissipativity control of discrete-time memristor-based neural networks (DTMBNNs) with unbounded or bounded time-varying delays. Firstly, the Luenberger observer is designed, and additionally ...

Design and implementation of origami robot ROS-based SLAM and autonomous navigation.

PloS one
In this study an innovative parameterized water-bomb wheel modeling method based on recursive solving are introduced, significantly reducing the modeling workload compared to traditional methods. A multi-link supporting structure is designed upon the...

Elevator block brake structural optimization design based on an approximate model.

PloS one
An Aquila optimizer-back propagation (AO-BP) neural network was used to establish an approximate model of the relationship between the design variables and the optimization objective to improve elevator block brake capabilities and achieve a lightwei...

Neural Q-learning for discrete-time nonlinear zero-sum games with adjustable convergence rate.

Neural networks : the official journal of the International Neural Network Society
In this paper, an adjustable Q-learning scheme is developed to solve the discrete-time nonlinear zero-sum game problem, which can accelerate the convergence rate of the iterative Q-function sequence. First, the monotonicity and convergence of the ite...

Evaluation of machine learning models for cytochrome P450 3A4, 2D6, and 2C9 inhibition.

Journal of applied toxicology : JAT
Cytochrome P450 (CYP) enzymes are involved in the metabolism of approximately 75% of marketed drugs. Inhibition of the major drug-metabolizing P450s could alter drug metabolism and lead to undesirable drug-drug interactions. Therefore, it is of great...

A lightweight and gradient-stable neural layer.

Neural networks : the official journal of the International Neural Network Society
To enhance resource efficiency and model deployability of neural networks, we propose a neural-layer architecture based on Householder weighting and absolute-value activating, called Householder-absolute neural layer or simply Han-layer. Compared to ...

Model-agnostic explanations for survival prediction models.

Statistics in medicine
Advanced machine learning methods capable of capturing complex and nonlinear relationships can be used in biomedical research to accurately predict time-to-event outcomes. However, these methods have been criticized as "black boxes" that are not inte...

Security challenges by AI-assisted protein design : The ability to design proteins in silico could pose a new threat for biosecurity and biosafety.

EMBO reports
Scientists and security experts are concerned that the increasing power of AI-assisted protein design and synthesis could be abused by various actors for terrorist or criminal purposes. [Image: see text]