Recursive neural networks: recent results and applications
Neural Network’s basic principles and functions are based on the nervous system of living organisms, they aim to simulate neurons of the human brain to solve complicated real-world problems by working in a forward-only manner. A recursive Neural Network on the other hand is based on a recursive desi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | SHS Web of Conferences |
| Online Access: | https://www.shs-conferences.org/articles/shsconf/pdf/2022/09/shsconf_etltc2022_03007.pdf |
| _version_ | 1811245901703806976 |
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| author | Zelios Andreas Grammenos Achilleas Papatsimouli Maria Asimopoulos Nikolaos Fragulis George |
| author_facet | Zelios Andreas Grammenos Achilleas Papatsimouli Maria Asimopoulos Nikolaos Fragulis George |
| author_sort | Zelios Andreas |
| collection | DOAJ |
| description | Neural Network’s basic principles and functions are based on the nervous system of living organisms, they aim to simulate neurons of the human brain to solve complicated real-world problems by working in a forward-only manner. A recursive Neural Network on the other hand is based on a recursive design principle over a given sequence input, to come up with a scalar assessment of the structured input. This means that is ideal for a given sequence of input data that is when processed dependent on its previous input sequence, which by default are used in various problems of our era. A common example could be devices such as Amazon Alexa, which uses speech recognition i.e., given an audio input source that receives audio signals, tries to predict logical expressions extracted from its different audio segments to form complete sentences. But RNNs do not come with no problems or difficulties. Today’s problems become more and more complex involving parameters in big data form, therefore a need for bigger and deeper RNNs is being created. This paper aims to explore these problems and ways to reduce them while also providing a description of RNN’s beneficial nature and listing different uses of the state-of-the-art RNNs and their use in different problems as those mentioned above. |
| first_indexed | 2024-04-12T14:46:03Z |
| format | Article |
| id | doaj.art-5feae39f74614ff29f7561574797f0e9 |
| institution | Directory Open Access Journal |
| issn | 2261-2424 |
| language | English |
| last_indexed | 2024-04-12T14:46:03Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | SHS Web of Conferences |
| spelling | doaj.art-5feae39f74614ff29f7561574797f0e92022-12-22T03:28:38ZengEDP SciencesSHS Web of Conferences2261-24242022-01-011390300710.1051/shsconf/202213903007shsconf_etltc2022_03007Recursive neural networks: recent results and applicationsZelios Andreas0Grammenos Achilleas1Papatsimouli Maria2Asimopoulos Nikolaos3Fragulis George4Department of Electrical and Computer Engineering, University of Western MacedoniaDepartment of Electrical and Computer Engineering, University of Western MacedoniaDepartment of Electrical and Computer Engineering, University of Western MacedoniaDepartment of Electrical and Computer Engineering, University of Western MacedoniaDepartment of Electrical and Computer Engineering, University of Western MacedoniaNeural Network’s basic principles and functions are based on the nervous system of living organisms, they aim to simulate neurons of the human brain to solve complicated real-world problems by working in a forward-only manner. A recursive Neural Network on the other hand is based on a recursive design principle over a given sequence input, to come up with a scalar assessment of the structured input. This means that is ideal for a given sequence of input data that is when processed dependent on its previous input sequence, which by default are used in various problems of our era. A common example could be devices such as Amazon Alexa, which uses speech recognition i.e., given an audio input source that receives audio signals, tries to predict logical expressions extracted from its different audio segments to form complete sentences. But RNNs do not come with no problems or difficulties. Today’s problems become more and more complex involving parameters in big data form, therefore a need for bigger and deeper RNNs is being created. This paper aims to explore these problems and ways to reduce them while also providing a description of RNN’s beneficial nature and listing different uses of the state-of-the-art RNNs and their use in different problems as those mentioned above.https://www.shs-conferences.org/articles/shsconf/pdf/2022/09/shsconf_etltc2022_03007.pdf |
| spellingShingle | Zelios Andreas Grammenos Achilleas Papatsimouli Maria Asimopoulos Nikolaos Fragulis George Recursive neural networks: recent results and applications SHS Web of Conferences |
| title | Recursive neural networks: recent results and applications |
| title_full | Recursive neural networks: recent results and applications |
| title_fullStr | Recursive neural networks: recent results and applications |
| title_full_unstemmed | Recursive neural networks: recent results and applications |
| title_short | Recursive neural networks: recent results and applications |
| title_sort | recursive neural networks recent results and applications |
| url | https://www.shs-conferences.org/articles/shsconf/pdf/2022/09/shsconf_etltc2022_03007.pdf |
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