Classification of fake news using multi-layer perceptron

Jehad, Reham; Yousif, Suhad A.

doi:10.1063/5.0042264

Cited by 17 publications

(5 citation statements)

References 5 publications

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“…Reham Jehada and Suhad A.Yousif. [9], 2022, They suggested that fake news be found. FNs using the multi-layer perception algorithm (MLP) as a class and the term inverted frequency document (TF-IDF) as a feature extraction method.…”

Section: Related Workmentioning

confidence: 99%

The Detection of Fake Text News using a Dense-based 1D-CNN Deep Learning Algorithm

2024

Acad. Sci. J.

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There are a lot of problems with fake news, which can make people think of things that ‎aren't true. Social media is one of the fastest ways to get information out there because it has ‎a big impact and can manipulate information in both good and bad ways. The goal of this ‎paper is the optimal use of deep learning algorithms to solve the problem of the paper. The research problem is how ‎accurately and to what extent can an individual distinguish between fake news articles using ‎natural language processing and classification algorithms. What are the steps that can be ‎taken to provide a solution?‎‎compared to the previous different methods to solve this problem, including some common ‎deep-learning methods. In this paper, we can find fake news can be found by using the term ‎inverse frequency document (TF-IDF) for feature extraction and a hybrid algorithm of One ‎Dimensional-Convolutional Neural Network (1D-CNN) and Dense as the classifier. The ‎experiments that the proposed dense-based 1D-CNN algorithm substantially outperforms ‎other up-to-date related algorithms with an accuracy of 100%.

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Section: Related Workmentioning

confidence: 99%

The Detection of Fake Text News using a Dense-based 1D-CNN Deep Learning Algorithm

2024

Acad. Sci. J.

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“…The four major categories are supervised, unsupervised, semisupervised, and reinforcement ML. Methods such as k-nearest neighbors (k-NN) [13], linear regression (LR) [14], logistic regression (LR) [15], support vector machines (SVM) [16], decision tree (DT) [17], random forest (RF) [18], Gradient boosting classifier [19], and neural network (NN) [20] are some of the most popular supervised learning algorithms. Unsupervised learning algorithms are also helpful in clustering methods like k-means [21], [22], hierarchical cluster analysis [23], and principal component analysis (PCA) [24].…”

Section: Machine Learningmentioning

confidence: 99%

An automated machine learning model for diagnosing coronavirus disease 2019 (COVID-19) infection

Maher

Yousif

2023

IJ-AI

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The coronavirus disease 2019 (COVID-19) epidemic still impacts every facet of life and necessitates a fast and accurate diagnosis. The need for an effective, rapid, and precise way to reduce radiologists' workload in diagnosing suspected cases has emerged. This study used the tree-based pipeline optimization tool (TPOT) and many machine learning (ML) algorithms. TPOT is an open-source genetic programming-based AutoML system that optimizes a set of feature preprocessors and ML models to maximize classification accuracy on a supervised classification problem. A series of trials and comparisons with the results of ML and earlier studies discovered that most of the AutoML beat traditional ML in terms of accuracy. A blood test dataset that has 111 variables and 5644 cases were used. In TPOT, 450 pipelines were used, and the best pipeline selected consisted of radial basis function (RBF) Sampler preprocessing and Gradient boosting classifier as the best algorithm with a 99% accuracy rate.

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“…Input, hidden, and output layers make up the Multilayer Perceptron (MLP), a deep neural network [4]. It begins with input nodes and ends with an output layer that represents the classes "cite" and "inproceedings".…”

Section: Multilayer Perceptronmentioning

confidence: 99%

An Empirical Comparative Study of Machine Learning Algorithms for Telugu News Classification

Dhanush,

Kumar,

Rohith

et al. 2023

Atlantis Highlights in Computer Sciences

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Amidst escalating data growth, effective classification in diverse domains, including the news industry, is imperative. However, relying solely on human intervention for classification is unfeasible. Addressing the complexities of the Telugu language and leveraging Natural Language Processing (NLP), this study employs classification techniques. Custom Machine Learning and Deep Learning models are developed, utilizing various word embeddings, aiming to enhance accuracy and efficiency in categorizing newspaper articles. The research tackles challenges of unstructured text, attributes, NLP techniques, missing metadata, and algorithm selection. The proposed model offers both generality and efficiency, systematically classifying text documents and demonstrating significant improvements in accuracy through innovative techniques.

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Classification of fake news using multi-layer perceptron

Cited by 17 publications

References 5 publications

The Detection of Fake Text News using a Dense-based 1D-CNN Deep Learning Algorithm

The Detection of Fake Text News using a Dense-based 1D-CNN Deep Learning Algorithm

An automated machine learning model for diagnosing coronavirus disease 2019 (COVID-19) infection

An Empirical Comparative Study of Machine Learning Algorithms for Telugu News Classification

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