Google pagerank algorithm: using efficient damping factor

Saber, Ali Ali; Omer, Aso Kamaran; Hamid, Noor Kaylan

doi:10.11591/ijeecs.v28.i3.pp1633-1639

Cited by 2 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, SVM is employed to best locate the separating hyperplane soensure the test's provided categorization error is kept to a minimum sample. An SVM may find the best hyperplane by increasing the distance from the hyperplane and linearly classifying (separating) the majority of the training data points [26], [27]. The margin is defined as twice this distance.…”

Section: Resultsmentioning

confidence: 99%

Machine learning based approach for detection of fake banknotes using support vector machine

Easa¹,

Saber²,

Hamid³

et al. 2023

IJEECS

View full text Add to dashboard Cite

Currency counterfeiting is a significant offense that has an impact on a nation's finances. Due to the enormous progress in printing technology, it is now quite simple to create fake currency that resembles real currency in both appearance and texture, making it nearly difficult to manually tell them apart. The suggested approach will be helpful in identifying fake currency in financial systems. Because of the rise of fake currency in the market, numerous false note detecting techniques are available globally to address this issue, however the most of them rely on expensive technology. In this paper, we'll introduce a revolutionary way for separating fake banknotes from real ones using the support vector machine (SVM) approach. To categorize bank notes as authentic or counterfeit utilizing the data retrieved from the photos of the bank notes, SVM performs better overall and is more effective, particularly when it comes to pattern categorization. Finally, the results of our experiment will demonstrate that the suggested algorithm does really yield extremely good performance.

show abstract

Section: Resultsmentioning

confidence: 99%

Machine learning based approach for detection of fake banknotes using support vector machine

Easa¹,

Saber²,

Hamid³

et al. 2023

IJEECS

View full text Add to dashboard Cite

show abstract

“…There are types of fake news and patterns that help in detection, like visual-based fake news, some fake news items feature more visuals than actual content [11], [12], which may be doctored video, altered photos, or a combination of the two. Also known as user-generated news, this type of fake news is produced by phony sources and directed at certain audiences that may represent particular age groups, genders, cultures, or political affiliations.…”

Section: Methodsmentioning

confidence: 99%

Detecting fake news spreaders on twitter

Ali Saber,

Khalil Easa,

Raoof Ismael

et al. 2023

IJEECS

View full text Add to dashboard Cite

<span>Nowadays, fake news is prevalent and too simple to propagate through social media, particularly during elections and pandemics like COVID-19. Several fake news stories have appeared on social media sites like LINE, Facebook, and Twitter after the COVID-19 epidemic throughout the world. Also, a lot of older individuals simply forward these communications without checking their veracity, which speeds up the dissemination of fake information. So, our goal is to identify fake news using machine learning. In this paper, we describe a supervised method that automatically gathers a sizable but noisy training dataset made up of a significant number of tweets. We will categorize tweets during collection into trustworthy and untrustworthy sources, then using the dataset to train a classifier. The categorization of fake and real tweets is the next classification objective for which we apply that classifier. We first demonstrate that real news is larger in size, shared on Twitter for a longer length of time, and shared by people with more followers than following. Second, we employed machine learning models like support vector machine (SVM), random forest (RF), and decision tree (DT), and we found out that the SVM is the best of all the models due to its best results and 99% accuracy.</span>

show abstract

Google pagerank algorithm: using efficient damping factor

Cited by 2 publications

References 21 publications

Machine learning based approach for detection of fake banknotes using support vector machine

Machine learning based approach for detection of fake banknotes using support vector machine

Detecting fake news spreaders on twitter

Contact Info

Product

Resources

About