David Charte

About David Charte

David Charte, With an exceptional h-index of 11 and a recent h-index of 11 (since 2020), a distinguished researcher at Universidad de Granada, specializes in the field of machine learning, data science, artificial intelligence, neural networks, deep learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges

Revisiting Data Complexity Metrics Based on Morphology for Overlap and Imbalance: Snapshot, New Overlap Number of Balls Metrics and Singular Problems Prospect

Reducing Data Complexity using Autoencoders with Class-informed Loss Functions

Slicer: feature learning for class separability with least-squares support vector machine loss and COVID-19 chest X-ray case study

COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on chest X-ray images

Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges

David Charte Information

University

Position

___

Citations(all)

1112

Citations(since 2020)

1042

Cited By

339

hIndex(all)

11

hIndex(since 2020)

11

i10Index(all)

11

i10Index(since 2020)

11

Email

University Profile Page

Google Scholar

David Charte Skills & Research Interests

machine learning

data science

artificial intelligence

neural networks

deep learning

Top articles of David Charte

A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges

2022/2/1

Revisiting Data Complexity Metrics Based on Morphology for Overlap and Imbalance: Snapshot, New Overlap Number of Balls Metrics and Singular Problems Prospect

Knowledge and Information Systems

2021/7

Reducing Data Complexity using Autoencoders with Class-informed Loss Functions

IEEE Transactions in Pattern Analysis and Machine Intelligence

2022/12

Slicer: feature learning for class separability with least-squares support vector machine loss and COVID-19 chest X-ray case study

2021

COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on chest X-ray images

IEEE journal of biomedical and health informatics

2020/11/10

An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges

Neurocomputing

2020/9/3

See List of Professors in David Charte University(Universidad de Granada)