Smart material to build mail spam filtering technique using Naive Bayes and MRF methodologies

Daisy, S. Jancy Sickory; Begum, A. Rijuvana

doi:10.1016/j.matpr.2021.04.630

Cited by 6 publications

(3 citation statements)

References 43 publications

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“…The Naive Bayes algorithm is a simple probabilistic classifier that creates a set of probabilities using a dataset's frequency distribution and value arrangement. The Naive Bayes technique performs a straightforward probabilistic classification process after computing the probability and configuration of values in a dataset [41]. The Nave Bayes method is also known as an algorithm with relatively simple calculations and a fairly quick learning process, and it is particularly well-suited for classifying data formed by several given categories.…”

Section: Classificationmentioning

confidence: 99%

Text Mining for News Forecasting on The Turnback Hoax Website

Wirawan,

Krisnanik,

Arista

2024

JOIV : Int. J. Inform. Visualization

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News has been disseminated swiftly via the internet due to the rapid growth of information technology. The rapid spreading of news often confuses because the truth cannot be ascertained. Additionally, online social media is becoming increasingly popular, making it an excellent environment for propagating false information, including misinformation, phony reviews, advertising, rumors, political remarks, innuendo, etc. This study's specific goal is to classify data using a data mining approach model called text mining so that a system can automatically do the classification. As a result, the study will produce a dataset, which can then be used to create an application using data mining's ability to predict breaking news. An application was produced by employing data mining to forecast recent news. This study was able to classify data using a naive Bayes data mining approach model so that a system can automatically do the classification. The study produced an accuracy of 77% obtained with training data of 82%. From 994 contents, misleading reached 33.9%, false reached 24.85%, imitation reached 13.48%, fake reached 11.07%, manipulated was 9.86%, parody was 3.22%, satire was 2.31%, and connection content as many as 1.31%. This study then visualizes the results using bar charts and word clouds. This work also produced datasets with the naïve Bayes method of news data and news that has been valid. Afterward, the dataset will be used in making applications to produce prototypes of computer program applications.

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Section: Classificationmentioning

confidence: 99%

Text Mining for News Forecasting on The Turnback Hoax Website

Wirawan,

Krisnanik,

Arista

2024

JOIV : Int. J. Inform. Visualization

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“…Research by Raza et al [14] demonstrated the superiority of multi-algorithm systems over single-algorithm systems for spam detection, with supervised learning outperforming unsupervised learning. Consequently, researchers have proposed methods that combine different algorithms, such as naive Bayes and Markov random field, to enhance spam detection accuracy [15].…”

Section: Text-based Spam Detectionmentioning

confidence: 99%

MMTD: A Multilingual and Multimodal Spam Detection Model Combining Text and Document Images

Zhang,

Deng,

Zhang

et al. 2023

Applied Sciences

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Spam detection has been a topic of extensive research; however, there has been limited focus on multimodal spam detection. In this study, we introduce a novel approach for multilingual multimodal spam detection, presenting the Multilingual and Multimodal Spam Detection Model combining Text and Document Images (MMTD). Unlike previous methods, our proposed model incorporates a document image encoder to extract image features from the entire email, providing a holistic understanding of both textual and visual content through a single image. Additionally, we employ a multilingual text encoder to extract textual features, enabling our model to process multilingual text content found in emails. To fuse the multimodal features, we employ a multimodal fusion module. Addressing the challenge of scarce large multilingual multimodal spam datasets, we introduce a new multilingual multimodal spam detection dataset comprising over 30,000 samples, which stands as the largest dataset of its kind to date. This dataset facilitates a rigorous evaluation of our proposed method. Extensive experiments were conducted on this dataset, and the performance of our model was validated using a five-fold cross-validation approach. The experimental results demonstrate the superiority of our approach, with our model achieving state-of-the-art performance, boasting an accuracy of 99.8% when compared to other advanced methods in the field.

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“…There are two types of machine learning: supervised learning and unsupervised learning, both of which are extensively utilized in NLP applications. Jancy Sickory Daisy & Rijuvana Begum (2021) used the Nave Bayes method and the Markov Random Field to circumvent the limitations of other filtering algorithms. By combining two algorithms, this hybrid system was able to detect spam effectively while saving time and improving accuracy.…”

Section: Spam Text Classification Techniquesmentioning

confidence: 99%

A systematic literature review on spam content detection and classification

Kaddoura

Chandrasekaran²,

Popescu

et al. 2022

PeerJ Computer Science

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The presence of spam content in social media is tremendously increasing, and therefore the detection of spam has become vital. The spam contents increase as people extensively use social media, i.e., Facebook, Twitter, YouTube, and E-mail. The time spent by people using social media is overgrowing, especially in the time of the pandemic. Users get a lot of text messages through social media, and they cannot recognize the spam content in these messages. Spam messages contain malicious links, apps, fake accounts, fake news, reviews, rumors, etc. To improve social media security, the detection and control of spam text are essential. This paper presents a detailed survey on the latest developments in spam text detection and classification in social media. The various techniques involved in spam detection and classification involving Machine Learning, Deep Learning, and text-based approaches are discussed in this paper. We also present the challenges encountered in the identification of spam with its control mechanisms and datasets used in existing works involving spam detection.

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Smart material to build mail spam filtering technique using Naive Bayes and MRF methodologies

Cited by 6 publications

References 43 publications

Text Mining for News Forecasting on The Turnback Hoax Website

Text Mining for News Forecasting on The Turnback Hoax Website

MMTD: A Multilingual and Multimodal Spam Detection Model Combining Text and Document Images

A systematic literature review on spam content detection and classification

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