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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音樂戲劇

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

COST

David Mond,Juan J. Nu?o-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

Vasodilation

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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

recede

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

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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

Acupressure

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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