Article: Systematic review of content analysis algorithms based on deep neural networks.
Multimedia tools and applications
2022 Volume 82, Issue 12, Page(s) 17879–17903
Abstract: Today according to social media, the internet, Etc. Data is rapidly produced and occupies a large space in systems that have resulted in enormous data warehouses; the progress in information technology has significantly increased the speed and ease of ... ...
| Abstract | Today according to social media, the internet, Etc. Data is rapidly produced and occupies a large space in systems that have resulted in enormous data warehouses; the progress in information technology has significantly increased the speed and ease of data flow.text mining is one of the most important methods for extracting a useful model through extracting and adapting knowledge from data sets. However, many studies have been conducted based on the usage of deep learning for text processing and text mining issues.The idea and method of text mining are one of the fields that seek to extract useful information from unstructured textual data that is used very today. Deep learning and machine learning techniques in classification and text mining and their type are discussed in this paper as well. Neural networks of various kinds, namely, ANN, RNN, CNN, and LSTM, are the subject of study to select the best technique. In this study, we conducted a Systematic Literature Review to extract and associate the algorithms and features that have been used in this area. Based on our search criteria, we retrieved 130 relevant studies from electronic databases between 1997 and 2021; we have selected 43 studies for further analysis using inclusion and exclusion criteria in Section 3.2. According to this study, hybrid LSTM is the most widely used deep learning algorithm in these studies, and SVM in machine learning method high accuracy in result shown. |
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| Language | English |
| Publishing date | 2022-10-24 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 1479928-5 |
| ISSN | 1573-7721 ; 1380-7501 |
| ISSN (online) | 1573-7721 |
| ISSN | 1380-7501 |
| DOI | 10.1007/s11042-022-14043-z |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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