Naïve Bayes Classifier-Assisted Least Loaded Routing for Circuit-Switched Networks

Recently, there is a rapid and wide increase in fake news, defined as provably incorrect information spread with the goal of fraud. The spread of this type of misinformation is a severe danger to social cohesiveness and well-being since it increases political polarisation and people's distrust of their leaders. Thus, fake news is a phenomenon that is having a significant impact on our social lives, particularly in politics. This paper proposes novel approaches based on Machine Learning (ML) and Deep Learning (DL) for the fake news detection system to address this phenomenon. The main aim of this paper is to find the optimal model that obtains high performance. Therefore, we propose an optimized Convolutional Neural Network model to detect fake news (OPCNN-FAKE). We compare the performance of the OPCNN-FAKE with Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and The six regular ML techniques: Decision Tree (DT), logistic Regression (LR), K Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB) using four fake news benchmark datasets. Grid search and hyperopt optimization techniques have been used to optimize the parameters of ML and DL, respectively. In addition, N-gram and Term Frequency-Inverse Document Frequency (TF-IDF) have been used to extract features from the benchmark datasets for regular ML, while Glove word embedding has been used to represent features as a feature matrix for DL models. To evaluate the performance of the OPCNN-FAKE, accuracy, precision, recall, F1-measure were applied to validate the results. The results show that OPCNN-FAKE model has achieved the best performance for each dataset compared with other models. Furthermore, the OPCNN-FAKE has a higher performance of cross-validation results and testing results than the other models, which indicates that the OPCNN-FAKE for fake news detection is significantly better than other models.

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“…Regular ML models Six Regular ML models: DT [47], LR [48], KNN [49], RF, SVM [50], and NB [51]) were used to compare with OPCNN-Fake.…”

Section: F Rnn and Lstm Modelsmentioning

confidence: 99%

OPCNN-FAKE: Optimized Convolutional Neural Network for Fake News Detection

Saleh¹,

Alharbi

Alsamhi

2021

IEEE Access

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“…One routing protocol in a circuit-switched network is the least loaded routing (LL) protocol. However, this protocol may have poor performance due to capacity overconsumption under high load situations, which affects its overall efficiency [112]. A novel online-based supervised NB classifier is then proposed in [112] to improve the performance of LL routing.…”

Section: Other Supervised Learning Ml-based Routing Algorithmmentioning

confidence: 99%

“…However, this protocol may have poor performance due to capacity overconsumption under high load situations, which affects its overall efficiency [112]. A novel online-based supervised NB classifier is then proposed in [112] to improve the performance of LL routing. The supervised NB classifier predicts the future circuit blocking probability between each node pair.…”

Section: Other Supervised Learning Ml-based Routing Algorithmmentioning

confidence: 99%

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

et al. 2021

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Communication networks are expanding rapidly and becoming increasingly complex. As a consequence, the conventional rule-based algorithms or protocols may no longer perform at their best efficiencies in these networks. Machine learning (ML) has recently been applied to solve complex problems in many fields, including finance, health care, and business. ML algorithms can offer computational models that can solve complex communication network problems and consequently improve performance. This paper reviews the recent trends in the application of ML models in communication networks for prediction, intrusion detection, route and path assignment, Quality of Service improvement, and resource management. A review of the recent literature reveals extensive opportunities for researchers to exploit the advantages of ML in solving complex performance issues in a network, especially with the advancement of softwaredefined networks and 5G.

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“…3) Naive bayes: Naive Bayes (NB) is a classification technique that makes use of the Bayes theory which is based on probability and statistical knowledge [29]. This technique was founded by Thomas Bayes in the 18th century.…”

Section: A Classification Techniques For Predictions 1) Random Foresmentioning

confidence: 99%

Feature Selection for Phishing Website Classification

Shabudin¹,

Samsiah²,

Akram³

et al. 2020

IJACSA

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Phishing is an attempt to obtain confidential information about a user or an organization. It is an act of impersonating a credible webpage to lure users to expose sensitive data, such as username, password and credit card information. It has cost the online community and various stakeholders hundreds of millions of dollars. There is a need to detect and predict phishing, and the machine learning classification approach is a promising approach to do so. However, it may take several phases to identify and tune the effective features from the dataset before the selected classifier can be trained to identify phishing sites correctly. This paper presents the performance of two feature selection techniques known as the Feature Selection by Omitting Redundant Features (FSOR) and Feature Selection by Filtering Method (FSFM) to the 'Phishing Websites' dataset from the University of California Irvine and evaluates the performance of phishing webpage detection via three different machine learning techniques: Random Forest (RF) tree, Multilayer Perceptron (MLP) and Naive Bayes (NB). The most effective classification performance of these machine learning algorithms is further rectified based on a selected subset of features set by various feature selection methods. The observational results have shown that the optimized Random Forest (RF PT) classifier with feature selection by the FSFM achieves the highest performance among all the techniques.

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Naïve Bayes Classifier-Assisted Least Loaded Routing for Circuit-Switched Networks

Cited by 21 publications

References 35 publications

OPCNN-FAKE: Optimized Convolutional Neural Network for Fake News Detection

OPCNN-FAKE: Optimized Convolutional Neural Network for Fake News Detection

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

Feature Selection for Phishing Website Classification

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