Oct 30, 2023 · This paper, presents a new model that extracts seven features from each message before applying a Multiple Linear Regression (MLR) to assign a weight to each of the extracted features. The message features are fed into the Extreme Learning Machine (ELM) to determine whether they are spam or ham.

Oct 1, 2009 · In this chapter, we discuss the challenging problems of Spam Recognition and then propose an anti-spam filtering framework; in which appropriate dimension reduction schemes and powerful classification models are employed.

Mar 22, 2024 · In this blog post, we’ll learn how machine learning can help us find and block spam emails, using easy-to-understand Python code and popular machine learning tools. So here, we start by...

Jun 1, 2019 · We present a systematic review of some of the popular machine learning based email spam filtering approaches. Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering.

Feb 20, 2023 · Spam is a kind of bulk or unsolicited email that contains an advertisement, phishing website link, malware, Trojan, etc. This research aims to classify spam emails using machine learning classifiers and evaluate the performance of classifiers. In the pre-processing step, the dataset has been analyzed in terms of attributes and instances.

Jul 31, 2023 · In this blog, we will explore the process of building a powerful spam email detection model using machine learning techniques. We will delve into data cleaning, exploratory data analysis,...

The proposed system of the project will effectively detect spam mails and the system will extract spam mails using some machine learning algorithms and it gives results with more accuracy and good performance.

Jul 21, 2023 · Abstract: The aim of the study is to detect SMS spam using Support Vector Machine (SVM) and linear regression (LR). The dataset used in the study contains 5573 sentences, and accuracy is measured for SMS spam detection.

2 days ago · Email spam detection is a critical task in modern communication systems, essential for maintaining productivity, security, and user experience. Traditional machine learning and deep learning approaches, while effective in static settings, face significant limitations in adapting to evolving spam tactics, addressing class imbalance, and managing data scarcity. These challenges necessitate ...

In this paper we implemented five Machine Learning Algorithms in the Python language using the scikit-learn library and we compared their performance against two publicly available spam email corpuses. The discussed algorithms are: Support Vector Machine, Random Forest, Logistic Regression, Multinomial Naive Bayes and Gaussian Naive Bayes.