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Automatic and intelligent content visualization system based on deep learning and genetic algorithm
Increasing demand in distance education, e-learning, web-based learning, and other digital sectors (e.g., entertainment) has led to excessive amounts of e-content. Learning objects (LOs) are among the most important components of electronic content (e-content) and are preserved in learning object re...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer London
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8760887/ https://www.ncbi.nlm.nih.gov/pubmed/35068702 http://dx.doi.org/10.1007/s00521-022-06887-1 |
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author | İnce, Murat |
author_facet | İnce, Murat |
author_sort | İnce, Murat |
collection | PubMed |
description | Increasing demand in distance education, e-learning, web-based learning, and other digital sectors (e.g., entertainment) has led to excessive amounts of e-content. Learning objects (LOs) are among the most important components of electronic content (e-content) and are preserved in learning object repositories (LORs). LORs produce different types of electronic content. In producing e-content, several visualization techniques are employed to attract users and ensure a better understanding of the provided information. Many of these visualization systems match images with corresponding text using methods such as semantic web, ontologies, natural language processing, statistical techniques, neural networks, and deep neural networks. Unlike these methods, in this study, an automatic and intelligent content visualization system is developed using deep learning and popular artificial intelligence techniques. The proposed system includes subsystems that segment images to panoptic image instances and use these image instances to generate new images using a genetic algorithm, an evolution-based technique that is one of the best-known artificial intelligence methods. This large-scale proposed system was used to test different amounts of LOs for various science fields. The results show that the developed system can be efficiently used to create visually enhanced content for digital use. |
format | Online Article Text |
id | pubmed-8760887 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-87608872022-01-18 Automatic and intelligent content visualization system based on deep learning and genetic algorithm İnce, Murat Neural Comput Appl Original Article Increasing demand in distance education, e-learning, web-based learning, and other digital sectors (e.g., entertainment) has led to excessive amounts of e-content. Learning objects (LOs) are among the most important components of electronic content (e-content) and are preserved in learning object repositories (LORs). LORs produce different types of electronic content. In producing e-content, several visualization techniques are employed to attract users and ensure a better understanding of the provided information. Many of these visualization systems match images with corresponding text using methods such as semantic web, ontologies, natural language processing, statistical techniques, neural networks, and deep neural networks. Unlike these methods, in this study, an automatic and intelligent content visualization system is developed using deep learning and popular artificial intelligence techniques. The proposed system includes subsystems that segment images to panoptic image instances and use these image instances to generate new images using a genetic algorithm, an evolution-based technique that is one of the best-known artificial intelligence methods. This large-scale proposed system was used to test different amounts of LOs for various science fields. The results show that the developed system can be efficiently used to create visually enhanced content for digital use. Springer London 2022-01-15 2022 /pmc/articles/PMC8760887/ /pubmed/35068702 http://dx.doi.org/10.1007/s00521-022-06887-1 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article İnce, Murat Automatic and intelligent content visualization system based on deep learning and genetic algorithm |
title | Automatic and intelligent content visualization system based on deep learning and genetic algorithm |
title_full | Automatic and intelligent content visualization system based on deep learning and genetic algorithm |
title_fullStr | Automatic and intelligent content visualization system based on deep learning and genetic algorithm |
title_full_unstemmed | Automatic and intelligent content visualization system based on deep learning and genetic algorithm |
title_short | Automatic and intelligent content visualization system based on deep learning and genetic algorithm |
title_sort | automatic and intelligent content visualization system based on deep learning and genetic algorithm |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8760887/ https://www.ncbi.nlm.nih.gov/pubmed/35068702 http://dx.doi.org/10.1007/s00521-022-06887-1 |
work_keys_str_mv | AT incemurat automaticandintelligentcontentvisualizationsystembasedondeeplearningandgeneticalgorithm |