Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

Dutta, Ashit Kumar; Qureshi, Basit; Albagory, Yasser; Alsanea, Majed; Faraj, Manal Al; Sait, Abdul Rahaman Wahab

doi:10.32604/csse.2023.027502

Cited by 10 publications

(3 citation statements)

References 20 publications

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“…The paper [23] addresses the pressing issue of fake news detection, emphasizing its broad implications from local to global scales. While various methods exist for identifying fake news, this study introduces a unique machine learning model, Chaotic Ant Swarm with Weighted Extreme Learning Machine (CAS-WELM), which is tailored explicitly for Cybersecurity Fake News Detection and Classification.…”

Section: Current Research Analysismentioning

confidence: 99%

Ensemble machine learning approaches for fake news classification

Padalko,

Chomko,

Yakovlev

et al. 2023

reks

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In today’s interconnected digital landscape, the proliferation of fake news has become a significant challenge, with far-reaching implications for individuals, institutions, and societies. The rapid spread of misleading information undermines the credibility of genuine news outlets and threatens informed decision-making, public trust, and democratic processes. Recognizing the profound relevance and urgency of addressing this issue, this research embarked on a mission to harness the power of machine learning to combat fake news menace. This study develops an ensemble machine learning model for fake news classification. The research is targeted at spreading fake news. The research subjects are machine learning methods for misinformation classification. Methods: we employed three state-of-the-art algorithms: LightGBM, XGBoost, and Balanced Random Forest (BRF). Each model was meticulously trained on a comprehensive dataset curated to encompass a diverse range of news articles, ensuring a broad representation of linguistic patterns and styles. A distinctive feature of the proposed approach is the emphasis on token importance. By leveraging specific tokens that exhibited a high degree of influence on classification outcomes, we enhanced the precision and reliability of the developed models. The empirical results were both promising and illuminating. The LightGBM model emerged as the top performer among the three, registering an impressive F1-score of 97.74% and an accuracy rate of 97.64%. Notably, all three of the proposed models consistently outperformed several existing models previously documented in academic literature. This comparative analysis underscores the efficacy and superiority of the proposed ensemble approach. In conclusion, this study contributes a robust, innovative, and scalable solution to the pressing challenge of fake news detection. By harnessing the capabilities of advanced machine learning techniques, the research findings pave the way for enhancing the integrity and veracity of information in an increasingly digitalized world, thereby safeguarding public trust and promoting informed discourse.

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Section: Current Research Analysismentioning

confidence: 99%

Ensemble machine learning approaches for fake news classification

Padalko,

Chomko,

Yakovlev

et al. 2023

reks

View full text Add to dashboard Cite

show abstract

“…The weights of the output layer are obtained through a one-step analytical calculation. ELM can avoid the complex training process of repeated iterations and greatly improves training efficiency [21]. The essence of ELM is to transform the training process into a linear least square's solution, and apply generalized inverse to calculate the weights of the output layer [22].…”

Section: Elmmentioning

confidence: 99%

NOx emissions estimation of boiler based on mutual information feature reconstruction and optimization of extreme learning machine

Jiang

Dong

Sun

et al. 2023

Meas. Sci. Technol.

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The measurement of NOx emissions in the selective catalytic reduction (SCR) system of boiler has problems with poor real-time performance and abnormal measurement during purging. It is necessary to accurately estimate NOx emissions. For this reason, the NOx emissions prediction method of boiler based on mutual information feature reconstruction and optimization of extreme learning machine (ELM) is proposed: Firstly, delay estimation and data space reconstruction of input features are performed based on mutual information; Then the conditional mutual information based on greedy selection strategy is adopted to rank and choose the input features; Finally, the hybrid quantum sparrow search algorithm (QSSA) was proposed by combining Lévy flight strategy and quantum strategy in the sparrow search algorithm (SSA), and QSSA is used to optimize the weights and biases of the ELM. Taking the operation data of the SCR system of a 1000MW thermal power unit as an example for verification. The results show that the proposed method can effectively improve the accuracy and generalization ability of the ELM, and provide a new method for NOx emissions estimation of boiler.

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“…After extracting fault information features, it is necessary to select appropriate algorithms for data classification. Echo network constructs a random network structure by randomly arranging large-scale sparsely connected analog neurons to form an efficient circular neural network, also known as reserve pool computing [7].…”

Section: Machine Learningmentioning

confidence: 99%

Ship Lock Electromagnetically Remote Fault Diagnosis Mode Based on Machine Learning

2022

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The traditional electromechanical fault diagnosis mode of the ship lock adopts the wired connection control intelligent control system, which cannot be remotely and wirelessly controlled through the control field and more strict control mode. The design of ship lock electromechanical remote fault diagnosis mode based on machine learning is proposed. The concept of machine learning and the characteristics of neural network are summarized. The fault diagnosis mode and fault diagnosis technology of electrical communication system are proposed. The experimental comparison of fault diagnosis accuracy of CNN SVM GA-SVM model shows that the deep learning bearing fault diagnosis model based on CNN network model has better performance.

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Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

Cited by 10 publications

References 20 publications

Ensemble machine learning approaches for fake news classification

Ensemble machine learning approaches for fake news classification

NOx emissions estimation of boiler based on mutual information feature reconstruction and optimization of extreme learning machine

Ship Lock Electromagnetically Remote Fault Diagnosis Mode Based on Machine Learning

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