Wyniki wyszukiwania - Biblioteka Nauki

Ten serwis zostanie wyłączony 2025-02-11.

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Scheduling using Convolutional Neural Network in GPU environment

100%

Świtalski P. , Siwiak K.

2024

tom vol. 1 (30)

81--96

Graphics processing units (GPU) have become the foundation of artificial intelligence. Machine learning was slow, inaccurate, and inadequate for many of today’s applications. The inclusion and utilization of GPUs made a remarkable difference in large neural networks. The numerous core processors on a GPU allow machine learning engineers to train complex models using many files relatively quickly. The ability to rapidly perform multiple computations in parallel is what makes them so effective; with a powerful processor, the model can make statistical predictions about very large amounts of data. GPUs are widely used in machine learning because they offer more power and speed than CPUs. In this paper, we show the use of GPU for solving a scheduling problem. The results show that this idea is useful, especially for large optimization problems.

Machine Learning Methods in E-mail Spam Classification

100%

Świtalski P. , Kopówka M.

tom Vol. 1-2(23)

57--76

Increasing number of unwanted e-mails has influence on users’ security in the Internet. Today spam e-mails can store potential malicious messages which e.g. can redirect user to fake sites. These messages recently appeared in social media. Filtering of this content is important due to minimize financial and branding costs. Traditional methods of spam filtering cannot be sufficient for present threats. We required new methods for constructing more dependable and robust antispam filters. Machine learning recently becomes very popular technique in classification methods. It has been successfully used in spam classification. In this paper we present some methods of machine learning for spam detecting. We would also like to introduce ways to solve the spam classification problem. We show that these methods can be useful in classification of malicious messages. We also compared developed methods and presented results in the experimental section.