Over the last few years there has been offered diverse techniques and methods for removing email spam and some of them are intended to divide and classify them into different subgroups. A group of software engineers has developed several techniques, including a genetic algorithm, K-NN algorithm (which will find а set of K-nearest neighbors), and clustering method (classifying spam messages into several subclasses) to deal with these problems. However, the main function of all the above-mentioned techniques is to promote the user interface and experience of email spam messages by dividing them into subgroups or removing and blocking non-requested messages. This research project will explain algorithms related to eliminating email spam messages and put forward а new suggestions/methods to the problem.
| Mualliflar | Nurullaev, Azam, Нуруллаев, Азам, Нуруллаев, Азам |
|---|---|
| Jurnal | Жамият ва инновациялар / Общество и инновации / Society and innovations |
| Nashr sanasi | 2024-11-25 |
| Jild | 5 |
| Son | 11/S |
| Betlar | 166-175 |
| Til | Ingliz |
| DOI | 10.47689/2181-1415-vol5-iss11/s-pp166-175 |
DOI: 10.47689/2181-1415-vol5-iss11/s-pp166-175 · Maqolaning asl sahifasi
email spam, clustering algorithm, machine learning, spam message classification, genetic algorithm, спам в электронной почте, Алгоритм кластеризации, Машинное обучение, Классификация спам-сообщений, Генетический алгоритм, электрон почта спам, Кластерлаш алгоритми, Cпам хабарларини таснифлаш, Генетик алгоритм
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