Fuzzy min–max neural networks: a bibliometric and social network analysis

ÖN Kenger, E Özceylan - Neural Computing and Applications, 2023 - Springer
The amount of digital data in the universe is growing at an exponential rate with the rapid
development of digital information, and this reveals new machine learning methods …

Evaluation of feature extraction and recognition for activity monitoring and fall detection based on wearable sEMG sensors

X **, M Tang, SM Miran, Z Luo - Sensors, 2017 - mdpi.com
As an essential subfield of context awareness, activity awareness, especially daily activity
monitoring and fall detection, plays a significant role for elderly or frail people who need …

Hyperbox-based machine learning algorithms: a comprehensive survey

TT Khuat, D Ruta, B Gabrys - Soft Computing, 2021 - Springer
With the rapid development of digital information, the data volume generated by humans
and machines is growing exponentially. Along with this trend, machine learning algorithms …

A scalable dynamic ensemble selection using fuzzy hyperboxes

R Davtalab, RMO Cruz, R Sabourin - Information Fusion, 2024 - Elsevier
Dynamic ensemble selection (DES) systems work by estimating the level of competence of
each classifier from a pool of classifiers and selecting the most competent ones for the …

Deep fuzzy min–max neural network: Analysis and design

W Huang, M Sun, L Zhu, SK Oh… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Fuzzy min-max neural network (FMNN) is one kind of three-layer models based on
hyperboxes that are constructed in a sequential way. Such a sequential mechanism …

Detection and multi-class classification of falling in elderly people by deep belief network algorithms

A Jahanjoo, M Naderan, MJ Rashti - Journal of Ambient Intelligence and …, 2020 - Springer
According to the reports on aging population, the number of elderly people without a
caregiver has increased. These people are always at high risks of adverse incidents such as …

A study of neural-network-based classifiers for material classification

HK Lam, U Ekong, H Liu, B **ao, H Araujo, SH Ling… - Neurocomputing, 2014 - Elsevier
In this paper, the performance of the commonly used neural-network-based classifiers is
investigated on solving a classification problem which aims to identify the object nature …

A comparative study of general fuzzy min-max neural networks for pattern classification problems

TT Khuat, B Gabrys - Neurocomputing, 2020 - Elsevier
General fuzzy min-max (GFMM) neural network is a generalization of fuzzy neural networks
formed by hyperbox fuzzy sets for classification and clustering problems. Two principle …

Accurate fall detection using 3-axis accelerometer sensor and MLF algorithm

A Jahanjoo, MN Tahan… - 2017 3rd International …, 2017 - ieeexplore.ieee.org
Nowadays, with the growing population of elderly people, the number of elderly without
caregivers at home has also increased. It is clear that an elderly living alone at home is at …

A game-theoretic approach to design secure and resilient distributed support vector machines

R Zhang, Q Zhu - IEEE transactions on neural networks and …, 2018 - ieeexplore.ieee.org
Distributed support vector machines (DSVMs) have been developed to solve large-scale
classification problems in networked systems with a large number of sensors and control …