Available on-line at https://www.mdpi.com/article/10 .3390/vaccines9111305/s1, File S1: ROB Excellent Assessment.Vaccines 2021, 9,16 ofAuthor Contributions: K.H. envisioned the principle Alvelestat Epigenetic Reader Domain conceptual suggestions associated to COVID-19 vaccines efficacies against variants, and proof outline, contributed to the writing of your manuscript, and supervised the study and was in charge with the all round path and arranging. S.S. contributed towards the design and style and implementation of all investigation components, for the evaluation of your benefits, and for the writing on the manuscript. M.A.S. contributed to the methodology, the writing with the manuscript, the validation and also the analysis of your final results and their implications. M.M.M. and H.A. provided important feedbacks and helped shape the analysis, evaluation, and conclusions. All authors have read and agreed for the published version of your manuscript. Funding: This perform was supported by each Zayed University under the study grant RIF R20132 and Zayed Center for Wellness Science, UAE University under grant # 12R005. Institutional Assessment Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. Ethical Approval: Ethical approval was not necessary for the reason that no personal information have been applied. Any analysis presented was aggregated.
applied sciencesArticleAn Explainable Artificial Intelligence Model for Detecting Xenophobic TweetsGabriel Ichcanziho P ez-Landa 1 , Octavio Loyola-Gonz ez two, and Miguel Angel Medina-P ez 1,School of Science and Engineering, Tecnologico de Monterrey, Carretera al Lago de Guadalupe Km. 3.5, Atizap 52926, Mexico; [email protected] (G.I.P.-L.); [email protected] (M.A.M.-P.) Altair Management Consultants, Calle de JosOrtega y Gasset 22-24, 5th Floor, 28006 Madrid, Spain Correspondence: [email protected]: P ez-Landa, G.I.; Loyola-Gonz ez, O.; Medina-P ez, M.A. An Explainable Artificial Intelligence Model for Detecting Xenophobic Tweets. Appl. Sci. 2021, 11, 10801. https://doi.org/10.3390/ app112210801 Academic Editors: Mar Paz Sesmero Lorente, Plamen Angelov and Jose Antonio Iglesias Martinez Received: 23 September 2021 Accepted: 27 October 2021 Published: 16 NovemberAbstract: Xenophobia is actually a social and political behavior that has been present in our societies because the starting of humanity. The feeling of hatred, worry, or resentment is present prior to folks from diverse communities from ours. With the rise of social networks like Twitter, hate speeches had been swift because of the pseudo feeling of anonymity that these platforms give. Sometimes this violent behavior on social networks that begins as threats or insults to third parties breaks the world wide web barriers to develop into an act of actual physical violence. Hence, this proposal aims to 20(S)-Hydroxycholesterol Biological Activity correctly classify xenophobic posts on social networks, specifically on Twitter. In addition, we collected a xenophobic tweets database from which we also extracted new attributes by utilizing a Organic Language Processing (NLP) strategy. Then, we supply an Explainable Artificial Intelligence (XAI) model, enabling us to know better why a post is considered xenophobic. Consequently, we provide a set of contrast patterns describing xenophobic tweets, which could assistance decision-makers avoid acts of violence caused by xenophobic posts on Twitter. Lastly, our interpretable outcomes based on our new feature representation method jointly with a.
