Automated Spam Detection Using Sandpiper Optimization Algorithm-Based Feature Selection with the Machine Learning Model

Amutha, T.; Geetha, S.

doi:10.1080/03772063.2023.2280663

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…As a result, processing the data comes first. To improve prediction, this model will eliminate punctuation [18]. String data types will be used to provide the data for this model.…”

Section: Data Pre-processingmentioning

confidence: 99%

An Efficient Mechanism of Modified Sand Cat Swarm Optimization and DCNN For Detection of Spam In Online Social Media Network

Nicholas,

Nirmalrani

2024

Preprint

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In recent years, social media has become an indispensable part of people's daily lives. The use of social media networks, particularly WhatsApp, Facebook, and email, has significantly surged worldwide due to their widespread popularity. These platforms serve as potent tools for users to share information and opinions, post photos and videos, and disseminate news. The growing popularity and importance of social media platforms attract spammers to a greater extent. Detecting social network spam is particularly challenging due to its brief content and constantly streaming nature. Many spam detection methods for social media rely on machine learning, utilizing a variety of classification and clustering algorithms. Deep learning techniques have achieved significant success globally in solving diverse problems across different domains. This research aims to develop a robust spam detection model for social media networks using deep CNN methods and to evaluate the effectiveness of the models using Modified Sand Cat Swarm (MCSO) based optimization models. N-gram and Bag of Words features are utilized for ensemble feature extraction and selection to rank and analyze the significance of features in spam detection. The performance of the proposed models is compared with other deep learning algorithms. A comparative analysis of spam detection models reveals that the random forest classifier outperforms when trained with randomly chosen data of varying sizes. A DCNN-based MSCSO successfully validates 99.02% of spam on social media networks, as confirmed by the classification. These unconventional pairings reinforce the original premise behind these classification findings.

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“…As a result, processing the data comes first. To improve prediction, this model will eliminate punctuation [18]. String data types will be used to provide the data for this model.…”

Section: Data Pre-processingmentioning

confidence: 99%

An Efficient Mechanism of Modified Sand Cat Swarm Optimization and DCNN For Detection of Spam In Online Social Media Network

Nicholas,

Nirmalrani

2024

Preprint

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Bitterling fish optimization (BFO) algorithm

Zareian,

Rahebi,

Shayegan

2024

Multimed Tools Appl

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The bitterling fish is a prime example of intelligent behavior in nature for survival. The bitterling fish uses the oyster spawning strategy as their babysitter. The female bitterling fish looks for a male fish stronger than other fish to find the right pair. In order to solve optimization issues, the Bitterling Fish Optimization (BFO) algorithm is modeled in this manuscript based on the mating behavior of these fish. The bitterling fish optimization algorithm is more accurate than the gray wolf optimization algorithm, whale optimization algorithm, butterfly optimization algorithm, Harris Hawks optimization algorithm, and black widow optimization algorithm, according to experiments and implementations on various benchmark functions. Data mining and machine learning are two areas where meta-heuristic techniques are frequently used. In trials, the MLP artificial neural network and a binary version of the BFO algorithm are used to lower the detection error for intrusion traffic. The proposed method's accuracy, precision, and sensitivity index for detecting network intrusion are 99.14%, 98.87%, and 98.85%, respectively, according to experiments on the NSL KDD data set. Compared to machine learning approaches like NNIA, DT, RF, XGBoot, and CNN, the proposed method is more accurate at detecting intrusion. The BFO algorithm is used for feature selection in the UNSW-NB15 dataset, and the tests showed that the accuracy of the proposed method is 96.72% in this dataset. The proposed method of the BFO algorithm is also used to improve Kmeans clustering, and the tests performed on the dataset of covid 19, diabetes, and kidney disease show that the proposed method performs better than iECA*, ECA*, GENCLUST + + (G + +) methods. Deep has KNN, LVQ, SVM, ANN, and KNN.

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Automated Spam Detection Using Sandpiper Optimization Algorithm-Based Feature Selection with the Machine Learning Model

Cited by 2 publications

References 23 publications

An Efficient Mechanism of Modified Sand Cat Swarm Optimization and DCNN For Detection of Spam In Online Social Media Network

An Efficient Mechanism of Modified Sand Cat Swarm Optimization and DCNN For Detection of Spam In Online Social Media Network

Bitterling fish optimization (BFO) algorithm

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