Analyzing the impact of deep learning algorithms and fuzzy logic approach for remote English translation
A remote English translation is used for assisting with on-demand support for adaptable sentence conversion and language understanding. The problem with on-demand translations is the precision verification of the words used. This article addresses the precision problem by assimilating deep learning...
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| Published in: | Scientific reports Vol. 14; no. 1; pp. 14556 - 15 |
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| Format: | Journal Article |
| Language: | English |
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Nature Publishing Group UK
24.06.2024
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2045-2322, 2045-2322 |
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| Abstract | A remote English translation is used for assisting with on-demand support for adaptable sentence conversion and language understanding. The problem with on-demand translations is the precision verification of the words used. This article addresses the precision problem by assimilating deep learning and fuzzy decision algorithm for remote translation support. The method named Fusion-dependent Precision Translation Approach (FPTA) conducts a series of recurrent validations on word usage and sentence completion for the given inputs. First, the completed sentences are verified using the understandability and meaning intended using deep learning in two recurrent layers. The first layer is responsible for identifying word placement and understandability and the second is responsible for meaning verification. The recurrent training is tuned using a fuzzy decision algorithm by selecting the maximum best-afford solution. The constraint’s understandability and meaning are augmented for tuning the outputs by preventing errors consequently. In precise, the error sequences are identified from the first layer for fuzzification across various inputs. This process improves the word adaptability from different languages reducing errors (12.49%) and improves the understandability (11.57%) for various translated sentences. |
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| AbstractList | A remote English translation is used for assisting with on-demand support for adaptable sentence conversion and language understanding. The problem with on-demand translations is the precision verification of the words used. This article addresses the precision problem by assimilating deep learning and fuzzy decision algorithm for remote translation support. The method named Fusion-dependent Precision Translation Approach (FPTA) conducts a series of recurrent validations on word usage and sentence completion for the given inputs. First, the completed sentences are verified using the understandability and meaning intended using deep learning in two recurrent layers. The first layer is responsible for identifying word placement and understandability and the second is responsible for meaning verification. The recurrent training is tuned using a fuzzy decision algorithm by selecting the maximum best-afford solution. The constraint's understandability and meaning are augmented for tuning the outputs by preventing errors consequently. In precise, the error sequences are identified from the first layer for fuzzification across various inputs. This process improves the word adaptability from different languages reducing errors (12.49%) and improves the understandability (11.57%) for various translated sentences. Abstract A remote English translation is used for assisting with on-demand support for adaptable sentence conversion and language understanding. The problem with on-demand translations is the precision verification of the words used. This article addresses the precision problem by assimilating deep learning and fuzzy decision algorithm for remote translation support. The method named Fusion-dependent Precision Translation Approach (FPTA) conducts a series of recurrent validations on word usage and sentence completion for the given inputs. First, the completed sentences are verified using the understandability and meaning intended using deep learning in two recurrent layers. The first layer is responsible for identifying word placement and understandability and the second is responsible for meaning verification. The recurrent training is tuned using a fuzzy decision algorithm by selecting the maximum best-afford solution. The constraint’s understandability and meaning are augmented for tuning the outputs by preventing errors consequently. In precise, the error sequences are identified from the first layer for fuzzification across various inputs. This process improves the word adaptability from different languages reducing errors (12.49%) and improves the understandability (11.57%) for various translated sentences. A remote English translation is used for assisting with on-demand support for adaptable sentence conversion and language understanding. The problem with on-demand translations is the precision verification of the words used. This article addresses the precision problem by assimilating deep learning and fuzzy decision algorithm for remote translation support. The method named Fusion-dependent Precision Translation Approach (FPTA) conducts a series of recurrent validations on word usage and sentence completion for the given inputs. First, the completed sentences are verified using the understandability and meaning intended using deep learning in two recurrent layers. The first layer is responsible for identifying word placement and understandability and the second is responsible for meaning verification. The recurrent training is tuned using a fuzzy decision algorithm by selecting the maximum best-afford solution. The constraint's understandability and meaning are augmented for tuning the outputs by preventing errors consequently. In precise, the error sequences are identified from the first layer for fuzzification across various inputs. This process improves the word adaptability from different languages reducing errors (12.49%) and improves the understandability (11.57%) for various translated sentences.A remote English translation is used for assisting with on-demand support for adaptable sentence conversion and language understanding. The problem with on-demand translations is the precision verification of the words used. This article addresses the precision problem by assimilating deep learning and fuzzy decision algorithm for remote translation support. The method named Fusion-dependent Precision Translation Approach (FPTA) conducts a series of recurrent validations on word usage and sentence completion for the given inputs. First, the completed sentences are verified using the understandability and meaning intended using deep learning in two recurrent layers. The first layer is responsible for identifying word placement and understandability and the second is responsible for meaning verification. The recurrent training is tuned using a fuzzy decision algorithm by selecting the maximum best-afford solution. The constraint's understandability and meaning are augmented for tuning the outputs by preventing errors consequently. In precise, the error sequences are identified from the first layer for fuzzification across various inputs. This process improves the word adaptability from different languages reducing errors (12.49%) and improves the understandability (11.57%) for various translated sentences. |
| ArticleNumber | 14556 |
| Author | Han, Xiuying |
| Author_xml | – sequence: 1 givenname: Xiuying surname: Han fullname: Han, Xiuying email: xiuying_han@outlook.com organization: Shanghai Normal University Tianhua College |
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| Keywords | Deep learning Fuzzy algorithm Precision improvement English translation |
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| References | B Natarajan (64831_CR5) 2022; 10 Y Xia (64831_CR17) 2020; 102 64831_CR29 E Rajalakshmi (64831_CR27) 2023; 11 B Natarajan (64831_CR32) 2022; 26 NL Pham (64831_CR28) 2023; 11 Y Zhao (64831_CR21) 2023; 11 C Zhao (64831_CR2) 2021; 20 Y Zhang (64831_CR26) 2022; 28 Y Wang (64831_CR8) 2023; 52 X Shen (64831_CR24) 2021; 82 MM Zin (64831_CR1) 2022; 10 A Sobhy (64831_CR9) 2023 Y Zhao (64831_CR22) 2021; 30 M Yang (64831_CR18) 2020; 28 A Heilmann (64831_CR4) 2021; 261 Y Huang (64831_CR15) 2023; 11 PY Genest (64831_CR13) 2022; 142 M Araújo (64831_CR12) 2020; 512 M Abbaszade (64831_CR20) 2021; 9 J Kang (64831_CR16) 2021 W Zhang (64831_CR19) 2023 64831_CR30 Y Li (64831_CR25) 2022; 27 F Samha (64831_CR3) 2023; 10 J Hu (64831_CR7) 2023; 27 X Shi (64831_CR14) 2021; 420 T Tian (64831_CR23) 2022; 199 B Natarajan (64831_CR6) 2023; 14 RG Rodrigues (64831_CR10) 2020; 18 Y Li (64831_CR31) 2021; 463 B Zhang (64831_CR11) 2018; 42 |
| References_xml | – ident: 64831_CR30 doi: 10.1145/3530989 – year: 2021 ident: 64831_CR16 publication-title: J. Ambient Intell. Human. Comput. doi: 10.1007/s12652-021-03198-6 – volume: 10 start-page: 100117 year: 2023 ident: 64831_CR3 publication-title: Ampersand doi: 10.1016/j.amper.2023.100117 – volume: 11 start-page: 27519 year: 2023 ident: 64831_CR21 publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3247508 – volume: 102 start-page: 643 year: 2020 ident: 64831_CR17 publication-title: Computing doi: 10.1007/s00607-019-00752-1 – volume: 42 start-page: 154 issue: 1 year: 2018 ident: 64831_CR11 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2018.2876404 – volume: 18 start-page: 1264 issue: 07 year: 2020 ident: 64831_CR10 publication-title: IEEE Latin Am. Trans. doi: 10.1109/TLA.2020.9099768 – volume: 20 start-page: 1 issue: 5 year: 2021 ident: 64831_CR2 publication-title: Trans. Asian and Low-Resour. Lang. Inf. Process. doi: 10.1145/3439799 – year: 2023 ident: 64831_CR9 publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3267981 – volume: 14 start-page: 9807 issue: 8 year: 2023 ident: 64831_CR6 publication-title: J. Ambient Intell. Human. Comput. doi: 10.1007/s12652-021-03640-9 – volume: 10 start-page: 67047 year: 2022 ident: 64831_CR1 publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3185415 – volume: 420 start-page: 15 year: 2021 ident: 64831_CR14 publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.05.104 – volume: 512 start-page: 1078 year: 2020 ident: 64831_CR12 publication-title: Inf. Sci. doi: 10.1016/j.ins.2019.10.031 – volume: 52 start-page: 1 year: 2023 ident: 64831_CR8 publication-title: J. Psycholinguist. Res. doi: 10.1007/s10936-023-09960-5 – volume: 28 start-page: 2585 year: 2020 ident: 64831_CR18 publication-title: IEEE/ACM Trans. Audio Speech Lang. Process. doi: 10.1109/TASLP.2020.3021347 – volume: 199 start-page: 1438 year: 2022 ident: 64831_CR23 publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2022.01.182 – volume: 261 start-page: 102813 year: 2021 ident: 64831_CR4 publication-title: Lingua doi: 10.1016/j.lingua.2020.102813 – volume: 9 start-page: 130434 year: 2021 ident: 64831_CR20 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3108768 – volume: 11 start-page: 2226 year: 2023 ident: 64831_CR27 publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3233671 – volume: 11 start-page: 28034 year: 2023 ident: 64831_CR28 publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3252898 – volume: 10 start-page: 104358 year: 2022 ident: 64831_CR5 publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3210543 – volume: 28 start-page: 1 year: 2022 ident: 64831_CR26 publication-title: Mobile Netw. Appl. – year: 2023 ident: 64831_CR19 publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3280549 – volume: 30 start-page: 244 year: 2021 ident: 64831_CR22 publication-title: IEEE/ACM Trans. Audio Speech Lang. Process. doi: 10.1109/TASLP.2021.3138719 – ident: 64831_CR29 doi: 10.1109/TAI.2022.3187680 – volume: 26 start-page: 13153 issue: 23 year: 2022 ident: 64831_CR32 publication-title: Soft Computing doi: 10.1007/s00500-022-07014-x – volume: 27 start-page: 1 year: 2022 ident: 64831_CR25 publication-title: Soft Computing – volume: 82 start-page: 103895 year: 2021 ident: 64831_CR24 publication-title: Microprocess. Microsyst. doi: 10.1016/j.micpro.2021.103895 – volume: 27 start-page: 1 year: 2023 ident: 64831_CR7 publication-title: Soft Comput. doi: 10.1007/s00500-023-07857-y – volume: 142 start-page: 102099 year: 2022 ident: 64831_CR13 publication-title: Data Knowl. Eng. doi: 10.1016/j.datak.2022.102099 – volume: 11 start-page: 100084 year: 2023 ident: 64831_CR15 publication-title: J. Eng. Res. doi: 10.1016/j.jer.2023.100084 – volume: 463 start-page: 368 year: 2021 ident: 64831_CR31 publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.08.019 |
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| Title | Analyzing the impact of deep learning algorithms and fuzzy logic approach for remote English translation |
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