SciTePress - Affiliation Profile

Research.Publish.Connect.

*Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search *Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search *Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search *Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Affiliation

CMI Lab, School of Engineering and Informatics, University of Wolverhampton, Wolverhampton, England, U.K.

Context (please use complete words only)

Field

Sort by

2 Papers

A Multi-Class Probabilistic Optimum-Path Forest

Topic: Machine Learning Technologies for Vision

Authors: Silas E. Nachif Fernandes ^{1

;

3} , Leandro Passos ² , Danilo Jodas ¹ , André Souza ³ and João Papa ¹

Affiliations: ¹ Department of Computing, São Paulo State University, Bauru, Brazil , ² School of Engineering and Informatics, University Wolverhampton, Wolverhampton, England, U.K. , ³ Department of Electrical Engineering, São Paulo State University, Bauru, Brazil

Digital Object Identifier: 10.5220/0011597700003417

Proceedings of VISAPP 2023/VISIGRAPP, ISBN: 978-989-758-634-7

Quick Abstract

The advent of machine learning provided numerous benefits to humankind, impacting fields such as medicine, military, and entertainment, to cite a few. In most cases, given some instances from a previously known domain, the intelligent algorithm is encharged of predicting a label that categorizes such samples in some learned context. Among several techniques capable of accomplishing such classification tasks, one may refer to Support Vector Machines, Neural Networks, or graph-based classifiers, such as the Optimum-Path Forest (OPF). Even though such a paradigm satisfies a wide sort of problems, others require the predicted class label and the classifier’s confidence, i.e., how sure the model is while attributing labels. Recently, an OPF-based variant was proposed to tackle this problem, i.e., the Probabilistic Optimum-Path Forest. Despite its satisfactory results over a considerable number of datasets, it was conceived to deal with binary classification only, thus lacking in the context of multi-class problems. Therefore, this paper proposes the Multi-Class Probabilistic Optimum-Path Forest, an extension designed to outdraw limitations observed in the standard Probabilistic OPF.

FakeRecogna Anomaly: Fake News Detection in a New Brazilian Corpus

Topics: Machine Learning Technologies for Vision; Document Imaging in Business

Authors: Gabriel Garcia ¹ , Luis Afonso ¹ , Leandro Passos ² , Danilo Jodas ¹ , Kelton A. P. da Costa ¹ and João Papa ¹

Affiliations: ¹ School of Sciences, São Paulo State University, Bauru, Brazil , ² CMI Lab, School of Engineering and Informatics, University of Wolverhampton, Wolverhampton, England, U.K.

Digital Object Identifier: 10.5220/0011660700003417

Proceedings of VISAPP 2023/VISIGRAPP, ISBN: 978-989-758-634-7

Quick Abstract

The advances in technology have allowed digital content to be shared in a very short time and reach thousands of people. Fake news is one of the content shared among people and it has a negative impact on our society. Therefore, its detection has become a research topic of great importance in the natural language processing and machine learning communities. Besides the techniques employed for detection, it is also important a good corpus so that machine learning techniques can learn to differentiate between real and fake news. One can find corpora in Brazilian Portuguese; however, they are either outdated or balanced, which does not reflect a real-life situation. This work presents a new updated and imbalanced corpus for the detection of fake news where the detection can be treated as an anomaly detection problem. This work also evaluates the proposed corpus by using classifiers designed for anomaly detection purposes.