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12 results on '"Elhadad, Mohamed K."'

1. An Ensemble Deep Learning Technique to Detect COVID-19 Misleading Information

Author

Elhadad, Mohamed K., Li, Kin Fun, Gebali, Fayez, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Li, Kin Fun, editor, Enokido, Tomoya, editor, and Takizawa, Makoto, editor

Published
2021
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2. COVID-19-FAKES: A Twitter (Arabic/English) Dataset for Detecting Misleading Information on COVID-19

Author

Elhadad, Mohamed K., Li, Kin Fun, Gebali, Fayez, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Li, Kin Fun, editor, and Miwa, Hiroyoshi, editor

Published
2021
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3. A Novel Approach for Selecting Hybrid Features from Online News Textual Metadata for Fake News Detection

Author

Elhadad, Mohamed K., Li, Kin Fun, Gebali, Fayez, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Hellinckx, Peter, editor, and Natwichai, Juggapong, editor

Published
2020
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4. Sentiment Analysis of Arabic and English Tweets

Author

Elhadad, Mohamed K., Li, Kin Fun, Gebali, Fayez, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Takizawa, Makoto, editor, Xhafa, Fatos, editor, and Enokido, Tomoya, editor

Published
2019
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9. Detecting Misleading Information on COVID-19

Author

Elhadad, Mohamed K., Li, Kin Fun, and Gebali, Fayez

Subjects
social networks, text classification, General Computer Science, Coronavirus disease 2019 (COVID-19), Computer science, social media, media_common.quotation_subject, Feature extraction, Computers and Information Processing, text mining, 02 engineering and technology, Machine learning, computer.software_genre, 01 natural sciences, World health, WHO, infodemic, Voting, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, 010306 general physics, fake news detection, media_common, SARS-CoV-2, business.industry, pandemic, General Engineering, COVID-19, Ensemble learning, misleading information, TK1-9971, Coronavirus, web mining, Web mining, Computational and Artificial Intelligence, 020201 artificial intelligence & image processing, Electrical engineering. Electronics. Nuclear engineering, Artificial intelligence, business, computer, Classifier (UML)
Abstract

This article addresses the problem of detecting misleading information related to COVID-19. We propose a misleading-information detection model that relies on the World Health Organization, UNICEF, and the United Nations as sources of information, as well as epidemiological material collected from a range of fact-checking websites. Obtaining data from reliable sources should assure their validity. We use this collected ground-truth data to build a detection system that uses machine learning to identify misleading information. Ten machine learning algorithms, with seven feature extraction techniques, are used to construct a voting ensemble machine learning classifier. We perform 5-fold cross-validation to check the validity of the collected data and report the evaluation of twelve performance metrics. The evaluation results indicate the quality and validity of the collected ground-truth data and their effectiveness in constructing models to detect misleading information.

Published
2020

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