A Meta-Heuristic Based Approach for Optimal Fake News Detection Using Supervised Learning DOI Creative Commons
Arunima Jaiswal,

Himika Verma,

Nitin Sachdeva

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 14, 2023

Abstract The rapid dissemination of fake news in the digital age has raised concernsregarding authenticity and credibility online information. In this study, we propose an innovative approach for detecting by combining power RoBERTa, a state-of-the-art language model, with Firefly Optimization Algorithm (FOA) Long Short-Term Memory (LSTM) neural networks. Our model leverages RoBERTa’s contextual understanding text to extract informative features from articles, including content, user engagement, source characteristics. FOA is employed optimize selection these features, enhancing model’s ability discern between genuine deceptive news. selected are then used as inputs LSTM network, which learns temporal dependencies within data accurate classification. To validate effectiveness our approach, conduct experiments using benchmark datasets such ISOT fakenewsnet specifically designed detection tasks. experimental results demonstrate that proposed outperforms existing methods terms accuracy other performance metrics. By integrating FOA, LSTM, effectively combines understanding, feature optimization, sequence modeling, enabling identification articles. This research contributes advancement techniques offers promising solution address challenges associated misinformation, ultimately fostering trust reliability information landscape.

Language: Английский

Sleep stage classification using fire hawk optimization based wavelet packet transform and Convolution Neural Network DOI

Anjali W. Pise,

Priti P. Rege

International Journal of Information Technology, Journal Year: 2023, Volume and Issue: 16(4), P. 2675 - 2691

Published: Oct. 9, 2023

Language: Английский

Citations

1
Solving the Green Economic Load Dispatch by Applying the Novel Meta-heuristic Algorithm DOI Creative Commons

Nguyen Anh Tang,

Nguyen Minh Duc Cuong

Journal of Computing Theories and Applications, Journal Year: 2023, Volume and Issue: 1(2), P. 129 - 139

Published: Nov. 23, 2023

This study focuses on solving the green economic load dispatch problem by considering presence of energy sources, including wind and solar power plants. The main objective function whole is to minimize total fuel cost (TFC) all thermal generating sources (TGSs) in system. Moreover, multiple selection TGSs also evaluated. Fire hawk optimization (FHO) Zebra algorithm (ZOA) are applied solve achieving best TFC value satisfying constraints involved. results indicated that ZOA can achieve a better optimal solution compared FHO. Particularly, obtained completely superior FHO comparison criteria at two demand levels, such as Best (Best.Cost), Average (Aver.Cost), Maximum (Max.Cost). On top that, only ones providing fast convergence capability functions cases levels. Therefore, an efficient search method deal with GELD problems.

Language: Английский

Citations

1
Legend at ArAIEval Shared Task: Persuasion Technique Detection using a Language-Agnostic Text Representation Model DOI Creative Commons
Olumide Ebenezer Ojo, Olaronke Oluwayemisi Adebanji, Hiram Calvo

et al.

Published: Jan. 1, 2023

In this paper, we share our best performing submission to the Arabic AI Tasks Evaluation Challenge (ArAIEval) at ArabicNLP 2023. Our focus was on Task 1, which involves identifying persuasion techniques in excerpts from tweets and news articles. The technique texts detected using a training loop with XLM-RoBERTa, language-agnostic text representation model. This approach proved be potent, leveraging fine-tuning of multilingual language evaluation test set, achieved micro F1 score 0.64 for subtask A competition.

Language: Английский

Citations

1
EHF-FNDM: An efficient hybrid features fake news detection methodology on social media DOI Creative Commons

Haidy Samir Fahim,

Asmaa Mohamed Al-saied,

Ahmed Shaban Samra

et al.

مجلة کلية دار العلوم, Journal Year: 2023, Volume and Issue: 48(6)

Published: Oct. 20, 2023

People are increasingly using social media to consume and share news. The inherent benefits of over traditional news include its low cost ease access. In addition, publishing a article requires less content censorship on media. rapid spread "fake news" media, that is, contains intentionally false information, has significant negative impact society. For instance, information about the coronavirus disease "2019" around world like virus. Therefore, developing effective methods detect fake early great importance. this paper, (Efficient Hybrid Features Fake News Detection Methodology) EHF-FNDM model was proposed. It is classification for detection based hybrid features. This developed identify user profiles, tweets, replies. can realize whether spreading or not. essential in determining not tweet fake.

Language: Английский

Citations

0
A Meta-Heuristic Based Approach for Optimal Fake News Detection Using Supervised Learning DOI Creative Commons
Arunima Jaiswal,

Himika Verma,

Nitin Sachdeva

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 14, 2023

Abstract The rapid dissemination of fake news in the digital age has raised concernsregarding authenticity and credibility online information. In this study, we propose an innovative approach for detecting by combining power RoBERTa, a state-of-the-art language model, with Firefly Optimization Algorithm (FOA) Long Short-Term Memory (LSTM) neural networks. Our model leverages RoBERTa’s contextual understanding text to extract informative features from articles, including content, user engagement, source characteristics. FOA is employed optimize selection these features, enhancing model’s ability discern between genuine deceptive news. selected are then used as inputs LSTM network, which learns temporal dependencies within data accurate classification. To validate effectiveness our approach, conduct experiments using benchmark datasets such ISOT fakenewsnet specifically designed detection tasks. experimental results demonstrate that proposed outperforms existing methods terms accuracy other performance metrics. By integrating FOA, LSTM, effectively combines understanding, feature optimization, sequence modeling, enabling identification articles. This research contributes advancement techniques offers promising solution address challenges associated misinformation, ultimately fostering trust reliability information landscape.

Language: Английский

Citations

0