Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Safa Alsafari"'
Autor:
Eman A. Al-Shahari, Marwa Obayya, Faiz Abdullah Alotaibi, Safa Alsafari, Ahmed S. Salama, Mohammed Assiri
Publikováno v:
AIMS Mathematics, Vol 9, Iss 3, Pp 5905-5924 (2024)
Biomedical image segmentation is a vital task in the analysis of medical imaging, including the detection and delineation of pathological regions or anatomical structures within medical images. It has played a pivotal role in a variety of medical app
Externí odkaz:
https://doaj.org/article/a7d31ddd1c6f4f8e9bf739e25e2764e9
Autor:
Safa Alsafari, Samira Sadaoui
Publikováno v:
Applied Artificial Intelligence, Vol 35, Iss 15, Pp 1621-1645 (2021)
Improving Offensive and Hate Speech (OHS) classifiers’ performances requires a large, confidently labeled textual training dataset. Our study devises a semi-supervised classification approach with self-training to leverage the abundant social media
Externí odkaz:
https://doaj.org/article/b733397d0e934e43bd40aa7315decb79
Autor:
Tehreem Ashfaq, Rabiya Khalid, Adamu Sani Yahaya, Sheraz Aslam, Ahmad Taher Azar, Safa Alsafari, Ibrahim A. Hameed
Publikováno v:
Sensors, Vol 22, Iss 19, p 7162 (2022)
In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud and anomalies. Moreover, block
Externí odkaz:
https://doaj.org/article/0503a04c4da84e89b45b4b11d7cd3343
Autor:
null Jos�Escorcia-Gutierrez, Margarita Gamarra, Roosvel Soto-Diaz, Safa Alsafari, Ayman Yafoz, Romany F. Mansour
Publikováno v:
Computers, Materials & Continua. 75:5255-5270
Autor:
Samira Sadaoui, Safa Alsafari
Publikováno v:
Applied Artificial Intelligence. 35:1621-1645
Improving Offensive and Hate Speech (OHS) classifiers’ performances requires a large, confidently labeled textual training dataset. Our study devises a semi-supervised classification approach with ...
Autor:
Safa Alsafari, Samira Sadaoui
Publikováno v:
2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
Autor:
Safa Alsafari, Samira Sadaoui
Publikováno v:
FLAIRS Conference
Large and accurately labeled textual corpora are vital to developing efficient hate speech classifiers. This paper introduces an ensemble-based semi-supervised learning approach to leverage the availability of abundant social media content. Starting
Publikováno v:
ICTAI
Our study explores offensive and hate speech detection for the Arabic language, as previous studies are minimal. Based on two-class, three-class, and six-class Arabic-Twitter datasets, we develop single and ensemble CNN and BiLSTM classifiers that we
Publikováno v:
Online Social Networks and Media. 19:100096
We are witnessing an increasing proliferation of hate speech on social media targeting individuals for their protected characteristics. Our study aims to devise an effective Arabic hate and offensive speech detection framework to address this serious