most citedScoring and Assessment in Medical VR Training Simulators with Dynamic Time Series Classification

31 citations · 44 across the 6 of their papers we have counts for

collaborators

11 papers

cs.LG20201 cited

An Online Learning Algorithm for a Neuro-Fuzzy Classifier with Mixed-Attribute Data

Thanh Tung Khuat, Bogdan Gabrys

General fuzzy min-max neural network (GFMMNN) is one of the efficient neuro-fuzzy systems for data classification. However, one of the downsides of its original learning algorithms…

cs.LG2020

An in-depth comparison of methods handling mixed-attribute data for general fuzzy min-max neural network

Thanh Tung Khuat, Bogdan Gabrys

A general fuzzy min-max (GFMM) neural network is one of the efficient neuro-fuzzy systems for classification problems. However, a disadvantage of most of the current learning algor…

cs.LG20205 cited

A Review of Meta-level Learning in the Context of Multi-component, Multi-level Evolving Prediction Systems

Abbas Raza Ali, Marcin Budka, Bogdan Gabrys

The exponential growth of volume, variety and velocity of data is raising the need for investigations of automated or semi-automated ways to extract useful patterns from the data.…

eess.SP202031 cited

Scoring and Assessment in Medical VR Training Simulators with Dynamic Time Series Classification

Neil Vaughan, Bogdan Gabrys

This research proposes and evaluates scoring and assessment methods for Virtual Reality (VR) training simulators. VR simulators capture detailed n-dimensional human motion data whi…

cs.SI20207 cited

Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction

Hongxu Chen, Hongzhi Yin, Xiangguo Sun +3

Cross-platform account matching plays a significant role in social network analytics, and is beneficial for a wide range of applications. However, existing methods either heavily r…

cs.LG2020

Accelerated learning algorithms of general fuzzy min-max neural network using a novel hyperbox selection rule

Thanh Tung Khuat, Bogdan Gabrys

This paper proposes a method to accelerate the training process of a general fuzzy min-max neural network. The purpose is to reduce the unsuitable hyperboxes selected as the potent…