ConvSegNet: Automated Polyp Segmentation From Colonoscopy Using Context Feature Refinement With Multiple Convolutional Kernel Sizes

Autor: Ayokunle Olalekan Ige, Nikhil Kumar Tomar, Felix Ola Aranuwa, Oluwafemi Oriola, Alaba O. Akingbesote, Mohd Halim Mohd Noor, Manuel Mazzara, Benjamin Segun Aribisala

Publikováno v: IEEE Access, Vol 11, Pp 16142-16155 (2023)

Colorectal cancer occurs in the rectal of humans, and early detection has been proved to reduce its mortality rate. Colonoscopy is the standard used in detecting the presence of polyps in the rectal, and accurate segmentation of the polyps from colon

Externí odkaz: https://doaj.org/article/dc5805b5a5124272815a4ac2c8082de7

Improved semi-supervised learning technique for automatic detection of South African abusive language on Twitter

Autor: Oluwafemi Oriola, Eduan Kotzé

Publikováno v: South African Computer Journal, Vol 32, Iss 2 (2020)

Semi-supervised learning is a potential solution for improving training data in low-resourced abusive language detection contexts such as South African abusive language detection on Twitter. However, the existing semi-supervised learning methods have

Externí odkaz: https://doaj.org/article/48c0b98baeec4b88be30f5bf0745215e

Exploring N-gram, Word Embedding and Topic Models for Content-based Fake News Detection in FakeNewsNet Evaluation

Autor: Oluwafemi Oriola

Publikováno v: International Journal of Computer Applications. 176:24-29

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ac26abe76a30e3fbe012b85d5f208ae8
https://doi.org/10.5120/ijca2020920503

Exploring Neural Embeddings and Transformers for Isolation of Offensive and Hate Speech in South African Social Media Space

Autor: Oluwafemi Oriola, Eduan Kotzé

Publikováno v: Computational Science and Its Applications – ICCSA 2022 ISBN: 9783031105210

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ae420939743bdb419bbf631c5ba2ae92
https://doi.org/10.1007/978-3-031-10522-7_44

Improved semi-supervised learning technique for automatic detection of South African abusive language on Twitter

Autor: Eduan Kotzé, Oluwafemi Oriola

Publikováno v: South African Computer Journal, Vol 32, Iss 2 (2020)
South African Computer Journal, Volume: 32, Issue: 2, Pages: 56-79, Published: DEC 2020

Semi-supervised learning is a potential solution for improving training data in low-resourced abusive language detection contexts such as South African abusive language detection on Twitter. However, the existing semi-supervised learning methods have

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d6a83e786776dcd66995ec7452e3afa
https://sacj.cs.uct.ac.za/index.php/sacj/article/view/847