AIMC Topic: Neural Networks, Computer

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Fixed-Time Synchronization of Complex Dynamical Networks: A Novel and Economical Mechanism.

IEEE transactions on cybernetics
Fixed-time synchronization of complex networks is investigated in this article. First, a completely novel lemma is introduced to prove the fixed-time stability of the equilibrium of a general ordinary differential system, which is less conservative a...

Novel Fixed-Time Stability Criteria for Discontinuous Nonautonomous Systems: Lyapunov Method With Indefinite Derivative.

IEEE transactions on cybernetics
This article considers a general class of nonautonomous discontinuous ordinary differential equations (ODE). By constructing the Filippov multimap, the fixed-time stability (FTS) problem of discontinuous ODE is transformed into that of differential i...

Disturbance Observer-Based Minimum Entropy Control for a Class of Disturbed Non-Gaussian Stochastic Systems.

IEEE transactions on cybernetics
In this article, a novel control algorithm is developed for a class of nonlinear stochastic systems subject to multiple disturbances, including exogenous dynamic disturbance and general non-Gaussian noise. An observer is designed to estimate the exog...

A New Settling-time Estimation Protocol to Finite-time Synchronization of Impulsive Memristor-Based Neural Networks.

IEEE transactions on cybernetics
In this article, the issues of finite-time synchronization and finite-time adaptive synchronization for the impulsive memristive neural networks (IMNNs) with discontinuous activation functions (DAFs) and hybrid impulsive effects are probed into and e...

Global Stability of Bidirectional Associative Memory Neural Networks With Multiple Time-Varying Delays.

IEEE transactions on cybernetics
This article investigates the global stability of bidirectional associative memory neural networks with discrete and distributed time-varying delays (DBAMNNs). By employing the comparison strategy and inequality techniques, global asymptotic stabilit...

Perception Exploration on Robustness Syndromes With Pre-processing Entities Using Machine Learning Algorithm.

Frontiers in public health
The majority of the current-generation individuals all around the world are dealing with a variety of health-related issues. The most common cause of health problems has been found as depression, which is caused by intellectual difficulties. However,...

Deep Learning-Based CT Imaging for the Diagnosis of Liver Tumor.

Computational intelligence and neuroscience
The objective of this research was to investigate the application value of deep learning-based computed tomography (CT) images in the diagnosis of liver tumors. Fifty-eight patients with liver tumors were selected, and their CT images were segmented ...

Improved Analysis of COVID-19 Influenced Pneumonia from the Chest X-Rays Using Fine-Tuned Residual Networks.

Computational intelligence and neuroscience
COVID-19 has remained a threat to world life despite a recent reduction in cases. There is still a possibility that the virus will evolve and become more contagious. If such a situation occurs, the resulting calamity will be worse than in the past if...

Impact of Artificial Intelligence Assistance on Chest CT Interpretation Times: A Prospective Randomized Study.

AJR. American journal of roentgenology
Deep learning-based convolutional neural networks have enabled major advances in development of artificial intelligence (AI) software applications. Modern AI applications offer comprehensive multiorgan evaluation. The purpose of this article was to...

Training much deeper spiking neural networks with a small number of time-steps.

Neural networks : the official journal of the International Neural Network Society
Spiking Neural Network (SNN) is a promising energy-efficient neural architecture when implemented on neuromorphic hardware. The Artificial Neural Network (ANN) to SNN conversion method, which is the most effective SNN training method, has successfull...