AIMC Topic: Algorithms

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Numerical learning of deep features from drug-exposed cell images to calculate IC50 without staining.

Scientific reports
To facilitate rapid determination of cellular viability caused by the inhibitory effect of drugs, numerical deep learning algorithms was used for unlabeled cell culture images captured by a light microscope as input. In this study, A549, HEK293, and ...

An Intelligent Optimization for Building Design Based on BP Neural Network and SPEA-II Multiobjective Algorithm.

Computational intelligence and neuroscience
With the continuous development of the field of building optimization, more and more optimization methods have sprung up, among which there are many kinds of intelligent optimization algorithms. This kind of intelligent optimization algorithm usually...

Application Optimization of University Aesthetic Education Resources Based on Few-Shot Learning from the Perspective of Ecological Aesthetic Education.

Computational intelligence and neuroscience
The idea of EAE (Ecological Aesthetic Education) is put forward on the basis of the mature development of ecological aesthetics and AE theory. Starting with EAE, establishing people's aesthetic attitude and improving people's spiritual realm will hel...

Computed Tomography Images under Artificial Intelligence Algorithms on the Treatment Evaluation of Intracerebral Hemorrhage with Minimally Invasive Aspiration.

Computational and mathematical methods in medicine
The aim of this study was to investigate the therapeutic effect of minimally invasive aspiration on intracerebral hemorrhage (ICH) and the value of artificial intelligence algorithm combined with computed tomography (CT) image evaluation. Ninety-two ...

Subjective machines: Probabilistic risk assessment based on deep learning of soft information.

Risk analysis : an official publication of the Society for Risk Analysis
For several years machine learning methods have been proposed for risk classification. While machine learning methods have also been used for failure diagnosis and condition monitoring, to the best of our knowledge, these methods have not been used f...

Compound FAT-based prespecified performance learning control of robotic manipulators with actuator dynamics.

ISA transactions
In the framework of the backstepping algorithm, this article proposes a new function approximation technique (FAT)-based compound learning control law for electrically-driven robotic manipulators with output constraint. The Fourier series expansion i...

Developed multiple-layer perceptron neural network based on developed search and rescue optimizer to predict iron ore price volatility: A case study.

ISA transactions
In economic investment, the role of forecasting is very important because in an economic project, the investor must carefully examine the dimensions of the work such that one of the most important and perhaps the main factor of a future investor and ...

Event-triggered integral reinforcement learning for nonzero-sum games with asymmetric input saturation.

Neural networks : the official journal of the International Neural Network Society
In this paper, an event-triggered integral reinforcement learning (IRL) algorithm is developed for the nonzero-sum game problem with asymmetric input saturation. First, for each player, a novel non-quadratic value function with a discount factor is d...

Optimistic reinforcement learning by forward Kullback-Leibler divergence optimization.

Neural networks : the official journal of the International Neural Network Society
This paper addresses a new interpretation of the traditional optimization method in reinforcement learning (RL) as optimization problems using reverse Kullback-Leibler (KL) divergence, and derives a new optimization method using forward KL divergence...

Multistability analysis of delayed recurrent neural networks with a class of piecewise nonlinear activation functions.

Neural networks : the official journal of the International Neural Network Society
This paper studies the multistability of delayed recurrent neural networks (DRNNs) with a class of piecewise nonlinear activation functions. The coexistence as well as the stability of multiple equilibrium points (EPs) of DRNNs are proved. With the B...