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Email Spam Detection Using Machine Learning.edited

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Email Spam Detection Using Machine Learning
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Email Spam Detection Using Machine Learning
Organizational Need
Spam detection is one of the most effective and widely used applications of Machine
Learning. Neural networks use content-based filtering to classify unwanted emails as spam.
These neural networks, which are somewhat similar to the human brain, can identify spam
emails and tweets. Spam detection is one of the most fundamental applications of machine
learning. Our email providers automatically route unsolicited spam emails to an unsolicited,
bulk, or spam inbox in most of our inboxes. Spam detection is the method of identifying
spam filters in email or text messages that are unwanted, harmful, terrorizing, or virus-
infected (Marsono et al., 2017). Numerous emails on the internet have attachments of various
types, including photographs, videos, and sounds.
The problem is that none of the emails contain information that is pertinent to the
sender. In other words, from the viewpoint of the message recipient, the messages sent by the
sender are often unwanted, disturbing, and frightening. For instance, if we receive consistent
messages from a particular target over an extended period, it's difficult to filter them out of a
large volume of emails automatically. Additionally, it's worth noting that, even though the
emails originate from a reputable source, the conduct seems to be highly unappealing or
spam-like (Marsono et al., 2017). It is essential to detect spam in this situation by analyzing a
large volume of complex data, which machine learning algorithms can do.
Context and Background
Phishing attacks can be detected in a high proportion of spam that is sent to the
intended recipient. These attacks take advantage of several security flaws introduced by the
Human-Computer Interface (HCI). Both academia and industry are interested in HCI security
research. Several participants in the underground economy must attach new computers to

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1 Email Spam Detection Using Machine Learning Name Institution Date 2 Email Spam Detection Using Machine Learning Organizational Need Spam detection is one of the most effective and widely used applications of Machine Learning. Neural networks use content-based filtering to classify unwanted emails as spam. These neural networks, which are somewhat similar to the human brain, can identify spam emails and tweets. Spam detection is one of the most fundamental applications of machine learning. Our email providers automatically route unsolicited spam emails to an unsolicited, bulk, or spam inbox in most of our inboxes. Spam detection is the method of identifying spam filters in email or text messages that are unwanted, harmful, terrorizing, or virusinfected (Marsono et al., 2017). Numerous emails on the internet have attachments of various types, including photographs, videos, and sounds. The problem is that none of the emails contain information that is pertinent to the sender. In other words, from the viewpoint of the message recipient, the messages sent by the sender are often unwanted, disturbing, and frightening. For instance, if we receive consistent messages from a particular target over an extended period, it's difficult to filter them out of a large volume of emails automatically. Additionally, it's worth noting that, even though the emails originate from a reputable source, the conduct seems to be highly unappealing or spam-like (Marsono et al., 2017). It is essenti ...
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