Supervised Machine Learning Algorithms for Fake News Detection

Kesarwani, Ankit; Chauhan, Sunita; Nair, Anil Ramachandra; Verma, Gaurav

doi:10.1007/978-981-15-5341-7_58

Cited by 10 publications

(6 citation statements)

References 15 publications

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“…In paper [5], authors created a fake news detection model based on headlines, as well as data on user social site traffic.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, Support vector machine algorithm that gives out accurate classification of linear data. In case the given data is of non-linear form we can make use of kernel trick to avoid complex transformations of dimensions [5] into a linear model. This algorithm develops a hyper plane in Ndimension space (dependent on the dimension of input fed) during the training period of the model.…”

Section: Support Vector Machinementioning

confidence: 99%

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Fake News Detection from Online media using Machine learning Classifiers

Pandey¹,

Prabhakaran²,

Reddy³

et al. 2022

J. Phys.: Conf. Ser.

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With the advancement in technology, the consumption of news has shifted from Print media to social media. The convenience and accessibility are major factors that have contributed to this shift in consumption of the news. However, this change has bought upon a new challenge in the form of “Fake news” being spread with not much supervision available on the net. In this paper, this challenge has been addressed through a Machine learning concept. The algorithms such as K-Nearest Neighbor, Support Vector Machine, Decision Tree, Naïve Bayes and Logistic regression Classifiers to identify the fake news from real ones in a given dataset and also have increased the efficiency of these algorithms by pre-processing the data to handle the imbalanced data more appropriately. Additionally, comparison of the working of these classifiers is presented along with the results. The model proposed has achieved an accuracy of 89.98% for KNN, 90.46% for Logistic Regression, 86.89% for Naïve Bayes, 73.33% for Decision Tree and 89.33% for SVM in our experiment.

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“…In paper [5], authors created a fake news detection model based on headlines, as well as data on user social site traffic.…”

Section: Related Workmentioning

confidence: 99%

Section: Support Vector Machinementioning

confidence: 99%

Fake News Detection from Online media using Machine learning Classifiers

Pandey¹,

Prabhakaran²,

Reddy³

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…The endeavor to automate the detection and prevention of false news presents formidable challenges, particularly concerning the assessment of content legitimacy [5,6]. Contemporary efforts predominantly rely on machine learning techniques to identify and mitigate fake news articles, as evidenced by numerous recent scholarly works [7][8][9][10][11][12][13][14][15][16]. The fusion of AI with blockchain technology emerges as a promising avenue for combating fake news [17].…”

Section: Introductionmentioning

confidence: 99%

A Quantum Approach to News Verification from the Perspective of a News Aggregator

Andronikos,

Sirokofskich

2024

Information

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In the dynamic landscape of digital information, the rise of misinformation and fake news presents a pressing challenge. This paper takes a completely new approach to verifying news, inspired by how quantum actors can reach agreement even when they are spatially spread out. We propose a radically new—to the best of our knowledge—algorithm that uses quantum “entanglement” (think of it as a special connection) to help news aggregators “sniff out” bad actors, whether they are other news sources or even fact-checkers trying to spread misinformation. This algorithm does not rely on quantum signatures; it merely uses basic quantum technology which we already have, in particular, special pairs of particles called “EPR pairs” that are much easier to create than other options. More elaborate entangled states are like juggling too many balls—they are difficult to make and slow things down, especially when many players are involved. So, we adhere to Bell states, the simplest form of entanglement, which are easy to generate no matter how many players are involved. This means that our algorithm is faster to set up, works for any number of participants, and is more practical for real-world use. Additionally, as a “bonus point”, it finishes in a fixed number of steps, regardless of how many players are involved, making it even more scalable. This new approach may lead to a powerful and efficient way to fight misinformation in the digital age, using the weird and wonderful world of quantum mechanics.

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“…The method for OD segmentation, as described by Tham et al [ 19 ], involves the use of direction-based supervised learning between OD boundary coordinates, as well as a visual representation of OD, to show how it works. Kesarwani et al [ 20 ] present a method for segmentation using a combination of CNN and dense-net methods. Convolutional blocks are densely connected together to form the pattern.…”

Section: Introductionmentioning

confidence: 99%

Glaucoma Detection Using Image Processing and Supervised Learning for Classification

Joshi

Partibane²,

Hatamleh

et al. 2022

Journal of Healthcare Engineering

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A difficult challenge in the realm of biomedical engineering is the detection of physiological changes occurring inside the human body, which is a difficult undertaking. At the moment, these irregularities are graded manually, which is very difficult, time-consuming, and tiresome due to the many complexities associated with the methods involved in their identification. In order to identify illnesses at an early stage, the use of computer-assisted diagnostics has acquired increased attention as a result of the requirement of a disease detection system. The major goal of this proposed work is to build a computer-aided design (CAD) system to help in the early identification of glaucoma as well as the screening and treatment of the disease. The fundus camera is the most affordable image analysis modality available, and it meets the financial needs of the general public. The extraction of structural characteristics from the segmented optic disc and the segmented optic cup may be used to characterize glaucoma and determine its severity. For this study, the primary goal is to estimate the potential of the image analysis model for the early identification and diagnosis of glaucoma, as well as for the evaluation of ocular disorders. The suggested CAD system would aid the ophthalmologist in the diagnosis of ocular illnesses by providing a second opinion as a judgment made by human specialists in a controlled environment. An ensemble-based deep learning model for the identification and diagnosis of glaucoma is in its early stages now. This method’s initial module is an ensemble-based deep learning model for glaucoma diagnosis, which is the first of its kind ever developed. It was decided to use three pretrained convolutional neural networks for the categorization of glaucoma. These networks included the residual network (ResNet), the visual geometry group network (VGGNet), and the GoogLeNet. It was necessary to use five different data sets in order to determine how well the proposed algorithm performed. These data sets included the DRISHTI-GS, the Optic Nerve Segmentation Database (DRIONS-DB), and the High-Resolution Fundus (HRF). Accuracy of 91.11% for the PSGIMSR data set and the sensitivity of 85.55% and specificity of 95.20% for the suggested ensemble architecture on the PSGIMSR data set were achieved. Similarly, accuracy rates of 95.63%, 98.67%, 95.64%, and 88.96% were achieved using the DRIONS-DB, HRF, DRISHTI-GS, and combined data sets, respectively.

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Supervised Machine Learning Algorithms for Fake News Detection

Cited by 10 publications

References 15 publications

Fake News Detection from Online media using Machine learning Classifiers

Fake News Detection from Online media using Machine learning Classifiers

A Quantum Approach to News Verification from the Perspective of a News Aggregator

Glaucoma Detection Using Image Processing and Supervised Learning for Classification

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