An effective fake news detection method using WOA-xgbTree algorithm and content-based features

Sheikhi, Saeid

doi:10.1016/j.asoc.2021.107559

Cited by 34 publications

(14 citation statements)

References 22 publications

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“…In many cases, machine learning techniques supported experts in diagnosing Parkinson's in patients. Machine learning methods have a wide variety of applications in different domains [ 8 ]. Many researchers applied machine learning algorithms to solve various problems in the medicine and biology fields [ 9 ].…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Rotation Forest-Based Ensemble Approach for Predicting Severity of Parkinson’s Disease

Sheikhi

Kheirabadi

2022

Journal of Healthcare Engineering

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Parkinson’s disease (PD) is a neurodegenerative nervous system disorder that mainly affects body movement, and it is one of the most common diseases, particularly in elderly individuals. This paper proposes a new machine learning approach to predict Parkinson’s disease severity using UCI’s Parkinson’s telemonitoring voice dataset. The proposed method analyses the patient's voice data and classifies them into “severe” and “nonsevere” classes. At first, a subset of features was selected, then a novel approach with a combination of Rotation Forest and Random Forest was applied on selected features to determine each patient’s disease severity. Analysis of the experimental results shows that the proposed approach can detect the severity of PD patients in the early stages. Moreover, the proposed model is compared with several algorithms, and the results indicate that the model is highly successful in classifying records and outperformed the other methods concerning classification accuracy and F1-measure rate.

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Section: Literature Reviewmentioning

confidence: 99%

An Efficient Rotation Forest-Based Ensemble Approach for Predicting Severity of Parkinson’s Disease

Sheikhi

Kheirabadi

2022

Journal of Healthcare Engineering

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“…The extensive experiments and the comparative analysis on the benchmark datasets showed the superiority of the linguistic model than the existing models. S. Sheikhi, [23] combined content based feature extraction techniques, Extreme Gradient Boosting Tree (xgbTree), and Whale Optimization Algorithm (WOA) for detecting fake news articles. The presented model majorly includes two phases: (i) used content based feature extraction techniques for extracting the informative feature vectors, and (ii) applied xgbTree-WOA model for classifying the news articles using the informative feature vectors.…”

Section: Related Workmentioning

confidence: 99%

Multi-stage Transfer Learning for Fake News Detection Using AWD-LSTM Network

Kiran¹,

Shashi²,

Madhuri³

2022

IJITCS

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In the recent decades, the automatic veracity verification of rumors is essential, since online social media platforms allow users to post news item or express opinion towards a circulating piece of information without much restriction. The intention of fake news is to make the readers believe in inaccurate information, where the detection of fake news by using content is a difficult task. So, the auxiliary information: user profile, social engagement of the users, and other user’s comments are useful in the detection of fake news. In this manuscript, a novel multi-stage transfer learning approach is introduced for an effective fake news detection, where it utilizes user’s comments as auxiliary information to detect whether the given tweet is true or false. The stances of the response tweets contain opinions on news/rumors are often used for verifying the veracity of the circulating information. In order to devastate the effects of the specific rumors at the earliest, the multi-stage transfer learning approach automatically predict veracity of rumors jointly with the stances of their response tweets. The proposed multi-stage transfer learning is an inductive transfer learning variation that is used to forecast the stance of responses, then to identify fake news. The proposed model’s effectiveness is evaluated on the two-benchmark datasets: semEval-2017 task 8 and PHEME. The proposed model outperformed the existing approaches by obtaining a classification accuracy of 64.30% and 65.30%, an F-measure of 65.95% and 63.90% on semEval-2017 task 8, and PHEME on event-wise datasets.

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“…Over the past years, researchers have used machine learning techniques to investigate intrusion detection systems and offer solutions to address the problems and limitations of conventional IDS methods [ 9 ]. Prior works have proposed various methods for identifying the data sample type for classifying cases into normal and anomaly classes.…”

Section: Related Workmentioning

confidence: 99%

A Novel Anomaly-Based Intrusion Detection Model Using PSOGWO-Optimized BP Neural Network and GA-Based Feature Selection

Sheikhi

Kostakos

2022

Sensors

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Intrusion detection systems (IDS) are crucial for network security because they enable detection of and response to malicious traffic. However, as next-generation communications networks become increasingly diversified and interconnected, intrusion detection systems are confronted with dimensionality difficulties. Prior works have shown that high-dimensional datasets that simulate real-world network data increase the complexity and processing time of IDS system training and testing, while irrelevant features waste resources and reduce the detection rate. In this paper, a new intrusion detection model is presented which uses a genetic algorithm (GA) for feature selection and optimization algorithms for gradient descent. First, the GA-based method is used to select a set of highly correlated features from the NSL-KDD dataset that can significantly improve the detection ability of the proposed model. A Back-Propagation Neural Network (BPNN) is then trained using the HPSOGWO method, a hybrid combination of the Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms. Finally, the hybrid HPSOGWO-BPNN algorithm is used to solve binary and multi-class classification problems on the NSL-KDD dataset. The experimental outcomes demonstrate that the proposed model achieves better performance than other techniques in terms of accuracy, with a lower error rate and better ability to detect different types of attacks.

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An effective fake news detection method using WOA-xgbTree algorithm and content-based features

Cited by 34 publications

References 22 publications

An Efficient Rotation Forest-Based Ensemble Approach for Predicting Severity of Parkinson’s Disease

An Efficient Rotation Forest-Based Ensemble Approach for Predicting Severity of Parkinson’s Disease

Multi-stage Transfer Learning for Fake News Detection Using AWD-LSTM Network

A Novel Anomaly-Based Intrusion Detection Model Using PSOGWO-Optimized BP Neural Network and GA-Based Feature Selection

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