AIMC Topic: Algorithms

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Neural network for a class of sparse optimization with L-regularization.

Neural networks : the official journal of the International Neural Network Society
Sparse optimization involving the L-norm function as the regularization in objective function has a wide application in many fields. In this paper, we propose a projected neural network modeled by a differential equation to solve a class of these opt...

Explainable artificial intelligence in skin cancer recognition: A systematic review.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However, decision-making by such algorithms is essentially a black-box process that renders it difficul...

Nonsynchronized State Estimation for Fuzzy Markov Jump Affine Systems With Switching Region Partitions.

IEEE transactions on cybernetics
This article investigates the state estimation issue of discrete-time Takagi-Sugeno fuzzy Markov jump affine systems that cover both traditional fuzzy Markov jump systems and fuzzy affine systems as two special cases. The original system is transform...

Some Novel Results on Stability Analysis of Generalized Neural Networks With Time-Varying Delays via Augmented Approach.

IEEE transactions on cybernetics
This article proposes three new methods to enlarge the feasible region for guaranteeing stability for generalized neural networks having time-varying delays based on the Lyapunov method. First, two new zero equalities in which three states are augmen...

Dynamic Event-Triggering Neural Learning Control for Partially Unknown Nonlinear Systems.

IEEE transactions on cybernetics
This article presents an event-sampled integral reinforcement learning algorithm for partially unknown nonlinear systems using a novel dynamic event-triggering strategy. This is a novel attempt to introduce the dynamic triggering into the adaptive le...

Learning With Selected Features.

IEEE transactions on cybernetics
The coming big data era brings data of unprecedented size and launches an innovation of learning algorithms in statistical and machine-learning communities. The classical kernel-based regularized least-squares (RLS) algorithm is excluded in the innov...

Modified BBO-Based Multivariate Time-Series Prediction System With Feature Subset Selection and Model Parameter Optimization.

IEEE transactions on cybernetics
Multivariate time-series prediction is a challenging research topic in the field of time-series analysis and modeling, and is continually under research. The echo state network (ESN), a type of efficient recurrent neural network, has been widely used...

Pre-processing methods in chest X-ray image classification.

PloS one
BACKGROUND: The SARS-CoV-2 pandemic began in early 2020, paralyzing human life all over the world and threatening our security. Thus, the need for an effective, novel approach to diagnosing, preventing, and treating COVID-19 infections became paramou...

Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU.

Cardiology in the young
BACKGROUND: Although serum lactate levels are widely accepted markers of haemodynamic instability, an alternative method to evaluate haemodynamic stability/instability continuously and non-invasively may assist in improving the standard of patient ca...

An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality.

Academic radiology
RATIONALE AND OBJECTIVES: The burden of coronavirus disease 2019 (COVID-19) airspace opacities is time consuming and challenging to quantify on computed tomography. The purpose of this study was to evaluate the ability of a deep convolutional neural ...