Volume 6 , Issue 2 , PP: 56-64, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Mohamed Saber 1 * , El-Sayed M. El-Kenawy 2 , Abdelhameed Ibrahim 3 , Marwa M. Eid 4 , Abdelaziz A. Abdelhamid 5
Doi: https://doi.org/10.54216/IJWAC.060205
By use of electronic communication, we are able to communicate a message to the recipient. In this digital age, a collaboration between several people is possible thanks to a variety of digital technologies. This interaction may take place in a variety of media formats, including but not limited to text, images, sound, and language. Today, a person's primary means of communication is their smart gadget, most commonly a cell phone. Spam is another side effect of our increasingly text-based modes of communication. We received a bunch of spam texts on our phones, and we know they're not from anyone we know. The vast majority of businesses nowadays use spam texts to advertise their wares, even when recipients have explicitly requested not to receive such messages. As a rule, there are many more spam emails than genuine ones. We apply text classification approaches to define short messaging service (SMS) and spam filtering in this study, which effectively categorizes messages. In this paper, we use "machine learning algorithms" and metaheuristic optimization to determine what percentage of incoming SMS messages are spam. This is why we used the optimized models to evaluate and contrast many classification strategies for gathering data.
Support vector machine , Neural network , Computer Networks , Decision tree , Voting ensemble , Dipper throated optimization.
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