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Titlebook: Combating Fake News with Computational Intelligence Techniques; Mohamed Lahby,Al-Sakib Khan Pathan,Wael Mohamed Sh Book 2022 The Editor(s)

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发表于 2025-3-21 19:07:01 | 显示全部楼层 |阅读模式
书目名称Combating Fake News with Computational Intelligence Techniques
编辑Mohamed Lahby,Al-Sakib Khan Pathan,Wael Mohamed Sh
视频video
概述Proposes new models, practical solutions, and technological advances related to the detection and analysis of fake news.Combines theory and practice so that readers find a description of the concepts
丛书名称Studies in Computational Intelligence
图书封面Titlebook: Combating Fake News with Computational Intelligence Techniques;  Mohamed Lahby,Al-Sakib Khan Pathan,Wael Mohamed Sh Book 2022 The Editor(s)
描述.This book presents the latest cutting-edge research, theoretical methods, and novel applications in the field of computational intelligence techniques and methods for combating fake news.. .Fake news is everywhere. Despite the efforts of major social network players such as Facebook and Twitter to fight disinformation, miracle cures and conspiracy theories continue to rain down on the net. Artificial intelligence can be a bulwark against the diversity of fake news on the Internet and social networks...This book discusses new models, practical solutions, and technological advances related to detecting and analyzing fake news based on computational intelligence models and techniques, to help decision-makers, managers, professionals, and researchers design new paradigms considering the unique opportunities associated with computational intelligence techniques. Further, the book helps readers understand computational intelligence techniques combating fake news in a systematic and straightforward way..
出版日期Book 2022
关键词Computational Intelligence; Fake News; Data Science; Artificial Intelligence; Machine Learning/ Deep Lea
版次1
doihttps://doi.org/10.1007/978-3-030-90087-8
isbn_softcover978-3-030-90089-2
isbn_ebook978-3-030-90087-8Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Stable Images and Discriminants of fake news on the Internet, analyzing the most important Deep learning algorithms and studies on their effectiveness in detecting distrustful information. The research methodology employed was bibliographic, documentary and descriptive. The information was collected from several scientific articl
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David Mond,Juan J. Nuño-Ballesterosia (SM). The broad dissemination of misinformation and FN can create harmful societal repercussions. Despite current improvements, spotting fake news remains difficult owing to its intricacy, multiplicity, multi-modality, and fact-scrutiny or annotation expenses. Therefore, there is a necessity for
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Numerical Computation of Singularity Sets,dia there is widespread fake news with misleading information leading to the chances where the reputation of the company is threatened. The influence of media has led to heights of depression and mental health issues as they don’t find the real cause of it. This makes it an important issue that need
发表于 2025-3-22 20:45:40 | 显示全部楼层
Oriol Bohigas,Montserrat Manubens,Lluís Rostechnology and thus, contributes to the increase of spreading of fake news. The current study experiments and investigates machine learning models that classify news as either fake or real. Five classifiers were implemented using Random Forest, Support Vector Machine, Gradient Boosting, Logistic Reg
发表于 2025-3-22 22:10:35 | 显示全部楼层
Introduction to Sato’s microlocal analysishe plethora of content choices. Taking this into account, fake news detection/classification is definitely becoming of paramount importance in order to avoid the so-called reality vertigo, preclude misinformation and protect actual reality. This chapter presents a comprehensive performance evaluatio
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Introduction to Sato’s microlocal analysisedibility and reliability, to check and be aware of the news is needed. Based on the artificial intelligence and suitable k-means grouping method, the existed and proved fake news could be used to train the proposed system. The features of the fake news could be classified and identified according t
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