- Tytuł:
- Usage of deep learning in recent applications
- Autorzy:
- Dubey, A.
- Powiązania:
- https://bibliotekanauki.pl/articles/24200557.pdf
- Data publikacji:
- 2022
- Wydawca:
- Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
- Tematy:
-
conceptual based information retrieval
ontology
semantic search
wyszukiwanie informacji oparte na pojęciach
ontologia
wyszukiwanie semantyczne - Opis:
- Purpose: Deep learning is a predominant branch in machine learning, which is inspired by the operation of the human biological brain in processing information and capturing insights. Machine learning evolved to deep learning, which helps to reduce the involvement of an expert. In machine learning, the performance depends on what the expert extracts manner features, but deep neural networks are self-capable for extracting features. Design/methodology/approach: Deep learning performs well with a large amount of data than traditional machine learning algorithms, and also deep neural networks can give better results with different kinds of unstructured data. Findings: Deep learning is an inevitable approach in real-world applications such as computer vision where information from the visual world is extracted, in the field of natural language processing involving analyzing and understanding human languages in its meaningful way, in the medical area for diagnosing and detection, in the forecasting of weather and other natural processes, in field of cybersecurity to provide a continuous functioning for computer systems and network from attack or harm, in field of navigation and so on. Practical implications: Due to these advantages, deep learning algorithms are applied to a variety of complex tasks. With the help of deep learning, the tasks that had been said as unachievable can be solved. Originality/value: This paper describes the brief study of the real-world application problems domain with deep learning solutions.
- Źródło:
-
Archives of Materials Science and Engineering; 2022, 115, 2; 49--57
1897-2764 - Pojawia się w:
- Archives of Materials Science and Engineering
- Dostawca treści:
- Biblioteka Nauki
Artykuł