Arithmetic Optimization Algorithm for Spam Detection

We've all dealt with spam emails, which regularly fill our inboxes and require just a few seconds of our time to remove them. When businesses are forced to develop spam filters and use filtering software, genuine emails may be mistakenly redirected to spam folders. When businesses take on spamming customers, it results in negative consequences for their network and IP reputation, as well as extra expenses associated with employing additional staff to deal with spam and abuse complaints exclusively. When you check off a list of emails that are spam and then delete them each time you log in, it may not seem like a major matter, but there are additional issues involved with sending and receiving spam communications. We do not often consider the expenses connected with spam concerns for organizations or Internet Service Providers, which might be significant (ISP). Non-stop email transmission is disrupted, and an increase in bandwidth utilization, a decrease in in-service performance, and decreased staff productivity are all consequences of this practice. This research paper will explain how the logistic regression linear model determines which emails are spam and which are not by using arithmetic optimization algorithms in machine learning.