AIMC Topic: Humans

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Efficient Computation Reduction in Bayesian Neural Networks Through Feature Decomposition and Memorization.

IEEE transactions on neural networks and learning systems
The Bayesian method is capable of capturing real-world uncertainties/incompleteness and properly addressing the overfitting issue faced by deep neural networks. In recent years, Bayesian neural networks (BNNs) have drawn tremendous attention to artif...

A Survey on Learning-Based Approaches for Modeling and Classification of Human-Machine Dialog Systems.

IEEE transactions on neural networks and learning systems
With the rapid development from traditional machine learning (ML) to deep learning (DL) and reinforcement learning (RL), dialog system equipped with learning mechanism has become the most effective solution to address human-machine interaction proble...

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...

Iterative Dynamic Generic Learning for Face Recognition From a Contaminated Single-Sample Per Person.

IEEE transactions on neural networks and learning systems
This article focuses on a new and practical problem in single-sample per person face recognition (SSPP FR), i.e., SSPP FR with a contaminated biometric enrolment database (SSPP-ce FR), where the SSPP-based enrolment database is contaminated by nuisan...

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...

Machine learning approaches to constructing predictive models of vitamin D deficiency in a hypertensive population: a comparative study.

Informatics for health & social care
Given the association between vitamin D deficiency and risk for cardiovascular disease, we used machine learning approaches to establish a model to predict the probability of deficiency. Determination of serum levels of 25-hydroxy vitamin D (25(OH)D...

Deep learning: A primer for psychologists.

Psychological methods
Deep learning has revolutionized predictive modeling in topics such as computer vision and natural language processing but is not commonly applied to psychological data. In an effort to bring the benefits of deep learning to psychologists, we provide...

Deep ANC: A deep learning approach to active noise control.

Neural networks : the official journal of the International Neural Network Society
Traditional active noise control (ANC) methods are based on adaptive signal processing with the least mean square algorithm as the foundation. They are linear systems and do not perform satisfactorily in the presence of nonlinear distortions. In this...

The Use of Artificial Neural Networks to Determine In-Hospital Mortality After Coronary Artery Bypass Surgery.

Journal of cardiothoracic and vascular anesthesia
OBJECTIVES: The aim of this study was to present an artificial neural network (ANN) model for the accurate estimation of in-hospital mortality and to demonstrate the validity of the model with real data and a comparison with conventional multiple lin...