Smart assistance to dyslexia students using artificial intelligence based augmentative alternative communication
Abstract Dyslexia students frequently deal with multiple difficulties in daily life, involving social interactions throughout their lives. Sometimes they are quickly refused the chance to indulge in social events since they suffer difficulty in learning, reading, understanding, etc. AAC seems to be...
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| Autor*in: |
Wang, Min [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of speech technology - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 25(2021), 2 vom: 24. Nov., Seite 343-353 |
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| Übergeordnetes Werk: |
volume:25 ; year:2021 ; number:2 ; day:24 ; month:11 ; pages:343-353 |
| Links: |
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| DOI / URN: |
10.1007/s10772-021-09921-0 |
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| Katalog-ID: |
SPR047361921 |
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| 520 | |a Abstract Dyslexia students frequently deal with multiple difficulties in daily life, involving social interactions throughout their lives. Sometimes they are quickly refused the chance to indulge in social events since they suffer difficulty in learning, reading, understanding, etc. AAC seems to be a vital communication aid for dyslexia students by providing an augmented reality (AR) paradigm to effective learning. This paper enhances the existing learning assistance technologies with innovative Artificial Intelligence (AI) to reinvigorate the Augmentative Alternative Communication ($ A^{2} $C) model for dyslexia children. The AI-based Augmentative Alternative Communication Approach has been developed to enhance learning skills with dyslexia by adapting to practices, and learning models are cognitively considered. The work on the academic skills of dyslexia students has been improved through the AI-based alternative communication paradigm for the improvement of the students with reading and learning. The AI-based AAC (AI–$ A^{2} $C) integrates the hybrid AI classifier in AAC to classify unique questions and provide users with the most appropriate pictograms. In contrast to the standard application, the proposed classifier decreased the effort and time taken to interact by 36.56% and 66.34%. Furthermore, the proposed model's performance is evaluated by its accuracy and efficiency of the hybrid AI classifier and compared with other AI classifiers. | ||
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10.1007/s10772-021-09921-0 doi (DE-627)SPR047361921 (SPR)s10772-021-09921-0-e DE-627 ger DE-627 rakwb eng Wang, Min verfasserin aut Smart assistance to dyslexia students using artificial intelligence based augmentative alternative communication 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Dyslexia students frequently deal with multiple difficulties in daily life, involving social interactions throughout their lives. Sometimes they are quickly refused the chance to indulge in social events since they suffer difficulty in learning, reading, understanding, etc. AAC seems to be a vital communication aid for dyslexia students by providing an augmented reality (AR) paradigm to effective learning. This paper enhances the existing learning assistance technologies with innovative Artificial Intelligence (AI) to reinvigorate the Augmentative Alternative Communication ($ A^{2} $C) model for dyslexia children. The AI-based Augmentative Alternative Communication Approach has been developed to enhance learning skills with dyslexia by adapting to practices, and learning models are cognitively considered. The work on the academic skills of dyslexia students has been improved through the AI-based alternative communication paradigm for the improvement of the students with reading and learning. The AI-based AAC (AI–$ A^{2} $C) integrates the hybrid AI classifier in AAC to classify unique questions and provide users with the most appropriate pictograms. In contrast to the standard application, the proposed classifier decreased the effort and time taken to interact by 36.56% and 66.34%. Furthermore, the proposed model's performance is evaluated by its accuracy and efficiency of the hybrid AI classifier and compared with other AI classifiers. Artificial Intelligence (dpeaa)DE-He213 Augmentative Alternative Communication (AAC) (dpeaa)DE-He213 Classifiers (dpeaa)DE-He213 Dyslexia (dpeaa)DE-He213 Muthu, BalaAnand aut Sivaparthipan, C. B. aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 25(2021), 2 vom: 24. Nov., Seite 343-353 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:25 year:2021 number:2 day:24 month:11 pages:343-353 https://dx.doi.org/10.1007/s10772-021-09921-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2021 2 24 11 343-353 |
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10.1007/s10772-021-09921-0 doi (DE-627)SPR047361921 (SPR)s10772-021-09921-0-e DE-627 ger DE-627 rakwb eng Wang, Min verfasserin aut Smart assistance to dyslexia students using artificial intelligence based augmentative alternative communication 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Dyslexia students frequently deal with multiple difficulties in daily life, involving social interactions throughout their lives. Sometimes they are quickly refused the chance to indulge in social events since they suffer difficulty in learning, reading, understanding, etc. AAC seems to be a vital communication aid for dyslexia students by providing an augmented reality (AR) paradigm to effective learning. This paper enhances the existing learning assistance technologies with innovative Artificial Intelligence (AI) to reinvigorate the Augmentative Alternative Communication ($ A^{2} $C) model for dyslexia children. The AI-based Augmentative Alternative Communication Approach has been developed to enhance learning skills with dyslexia by adapting to practices, and learning models are cognitively considered. The work on the academic skills of dyslexia students has been improved through the AI-based alternative communication paradigm for the improvement of the students with reading and learning. The AI-based AAC (AI–$ A^{2} $C) integrates the hybrid AI classifier in AAC to classify unique questions and provide users with the most appropriate pictograms. In contrast to the standard application, the proposed classifier decreased the effort and time taken to interact by 36.56% and 66.34%. Furthermore, the proposed model's performance is evaluated by its accuracy and efficiency of the hybrid AI classifier and compared with other AI classifiers. Artificial Intelligence (dpeaa)DE-He213 Augmentative Alternative Communication (AAC) (dpeaa)DE-He213 Classifiers (dpeaa)DE-He213 Dyslexia (dpeaa)DE-He213 Muthu, BalaAnand aut Sivaparthipan, C. B. aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 25(2021), 2 vom: 24. Nov., Seite 343-353 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:25 year:2021 number:2 day:24 month:11 pages:343-353 https://dx.doi.org/10.1007/s10772-021-09921-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2021 2 24 11 343-353 |
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10.1007/s10772-021-09921-0 doi (DE-627)SPR047361921 (SPR)s10772-021-09921-0-e DE-627 ger DE-627 rakwb eng Wang, Min verfasserin aut Smart assistance to dyslexia students using artificial intelligence based augmentative alternative communication 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Dyslexia students frequently deal with multiple difficulties in daily life, involving social interactions throughout their lives. Sometimes they are quickly refused the chance to indulge in social events since they suffer difficulty in learning, reading, understanding, etc. AAC seems to be a vital communication aid for dyslexia students by providing an augmented reality (AR) paradigm to effective learning. This paper enhances the existing learning assistance technologies with innovative Artificial Intelligence (AI) to reinvigorate the Augmentative Alternative Communication ($ A^{2} $C) model for dyslexia children. The AI-based Augmentative Alternative Communication Approach has been developed to enhance learning skills with dyslexia by adapting to practices, and learning models are cognitively considered. The work on the academic skills of dyslexia students has been improved through the AI-based alternative communication paradigm for the improvement of the students with reading and learning. The AI-based AAC (AI–$ A^{2} $C) integrates the hybrid AI classifier in AAC to classify unique questions and provide users with the most appropriate pictograms. In contrast to the standard application, the proposed classifier decreased the effort and time taken to interact by 36.56% and 66.34%. Furthermore, the proposed model's performance is evaluated by its accuracy and efficiency of the hybrid AI classifier and compared with other AI classifiers. Artificial Intelligence (dpeaa)DE-He213 Augmentative Alternative Communication (AAC) (dpeaa)DE-He213 Classifiers (dpeaa)DE-He213 Dyslexia (dpeaa)DE-He213 Muthu, BalaAnand aut Sivaparthipan, C. B. aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 25(2021), 2 vom: 24. Nov., Seite 343-353 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:25 year:2021 number:2 day:24 month:11 pages:343-353 https://dx.doi.org/10.1007/s10772-021-09921-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2021 2 24 11 343-353 |
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10.1007/s10772-021-09921-0 doi (DE-627)SPR047361921 (SPR)s10772-021-09921-0-e DE-627 ger DE-627 rakwb eng Wang, Min verfasserin aut Smart assistance to dyslexia students using artificial intelligence based augmentative alternative communication 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Dyslexia students frequently deal with multiple difficulties in daily life, involving social interactions throughout their lives. Sometimes they are quickly refused the chance to indulge in social events since they suffer difficulty in learning, reading, understanding, etc. AAC seems to be a vital communication aid for dyslexia students by providing an augmented reality (AR) paradigm to effective learning. This paper enhances the existing learning assistance technologies with innovative Artificial Intelligence (AI) to reinvigorate the Augmentative Alternative Communication ($ A^{2} $C) model for dyslexia children. The AI-based Augmentative Alternative Communication Approach has been developed to enhance learning skills with dyslexia by adapting to practices, and learning models are cognitively considered. The work on the academic skills of dyslexia students has been improved through the AI-based alternative communication paradigm for the improvement of the students with reading and learning. The AI-based AAC (AI–$ A^{2} $C) integrates the hybrid AI classifier in AAC to classify unique questions and provide users with the most appropriate pictograms. In contrast to the standard application, the proposed classifier decreased the effort and time taken to interact by 36.56% and 66.34%. Furthermore, the proposed model's performance is evaluated by its accuracy and efficiency of the hybrid AI classifier and compared with other AI classifiers. Artificial Intelligence (dpeaa)DE-He213 Augmentative Alternative Communication (AAC) (dpeaa)DE-He213 Classifiers (dpeaa)DE-He213 Dyslexia (dpeaa)DE-He213 Muthu, BalaAnand aut Sivaparthipan, C. B. aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 25(2021), 2 vom: 24. Nov., Seite 343-353 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:25 year:2021 number:2 day:24 month:11 pages:343-353 https://dx.doi.org/10.1007/s10772-021-09921-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2021 2 24 11 343-353 |
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10.1007/s10772-021-09921-0 doi (DE-627)SPR047361921 (SPR)s10772-021-09921-0-e DE-627 ger DE-627 rakwb eng Wang, Min verfasserin aut Smart assistance to dyslexia students using artificial intelligence based augmentative alternative communication 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Dyslexia students frequently deal with multiple difficulties in daily life, involving social interactions throughout their lives. Sometimes they are quickly refused the chance to indulge in social events since they suffer difficulty in learning, reading, understanding, etc. AAC seems to be a vital communication aid for dyslexia students by providing an augmented reality (AR) paradigm to effective learning. This paper enhances the existing learning assistance technologies with innovative Artificial Intelligence (AI) to reinvigorate the Augmentative Alternative Communication ($ A^{2} $C) model for dyslexia children. The AI-based Augmentative Alternative Communication Approach has been developed to enhance learning skills with dyslexia by adapting to practices, and learning models are cognitively considered. The work on the academic skills of dyslexia students has been improved through the AI-based alternative communication paradigm for the improvement of the students with reading and learning. The AI-based AAC (AI–$ A^{2} $C) integrates the hybrid AI classifier in AAC to classify unique questions and provide users with the most appropriate pictograms. In contrast to the standard application, the proposed classifier decreased the effort and time taken to interact by 36.56% and 66.34%. Furthermore, the proposed model's performance is evaluated by its accuracy and efficiency of the hybrid AI classifier and compared with other AI classifiers. Artificial Intelligence (dpeaa)DE-He213 Augmentative Alternative Communication (AAC) (dpeaa)DE-He213 Classifiers (dpeaa)DE-He213 Dyslexia (dpeaa)DE-He213 Muthu, BalaAnand aut Sivaparthipan, C. B. aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 25(2021), 2 vom: 24. Nov., Seite 343-353 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:25 year:2021 number:2 day:24 month:11 pages:343-353 https://dx.doi.org/10.1007/s10772-021-09921-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2021 2 24 11 343-353 |
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Wang, Min |
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Smart assistance to dyslexia students using artificial intelligence based augmentative alternative communication Artificial Intelligence (dpeaa)DE-He213 Augmentative Alternative Communication (AAC) (dpeaa)DE-He213 Classifiers (dpeaa)DE-He213 Dyslexia (dpeaa)DE-He213 |
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smart assistance to dyslexia students using artificial intelligence based augmentative alternative communication |
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Smart assistance to dyslexia students using artificial intelligence based augmentative alternative communication |
| abstract |
Abstract Dyslexia students frequently deal with multiple difficulties in daily life, involving social interactions throughout their lives. Sometimes they are quickly refused the chance to indulge in social events since they suffer difficulty in learning, reading, understanding, etc. AAC seems to be a vital communication aid for dyslexia students by providing an augmented reality (AR) paradigm to effective learning. This paper enhances the existing learning assistance technologies with innovative Artificial Intelligence (AI) to reinvigorate the Augmentative Alternative Communication ($ A^{2} $C) model for dyslexia children. The AI-based Augmentative Alternative Communication Approach has been developed to enhance learning skills with dyslexia by adapting to practices, and learning models are cognitively considered. The work on the academic skills of dyslexia students has been improved through the AI-based alternative communication paradigm for the improvement of the students with reading and learning. The AI-based AAC (AI–$ A^{2} $C) integrates the hybrid AI classifier in AAC to classify unique questions and provide users with the most appropriate pictograms. In contrast to the standard application, the proposed classifier decreased the effort and time taken to interact by 36.56% and 66.34%. Furthermore, the proposed model's performance is evaluated by its accuracy and efficiency of the hybrid AI classifier and compared with other AI classifiers. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract Dyslexia students frequently deal with multiple difficulties in daily life, involving social interactions throughout their lives. Sometimes they are quickly refused the chance to indulge in social events since they suffer difficulty in learning, reading, understanding, etc. AAC seems to be a vital communication aid for dyslexia students by providing an augmented reality (AR) paradigm to effective learning. This paper enhances the existing learning assistance technologies with innovative Artificial Intelligence (AI) to reinvigorate the Augmentative Alternative Communication ($ A^{2} $C) model for dyslexia children. The AI-based Augmentative Alternative Communication Approach has been developed to enhance learning skills with dyslexia by adapting to practices, and learning models are cognitively considered. The work on the academic skills of dyslexia students has been improved through the AI-based alternative communication paradigm for the improvement of the students with reading and learning. The AI-based AAC (AI–$ A^{2} $C) integrates the hybrid AI classifier in AAC to classify unique questions and provide users with the most appropriate pictograms. In contrast to the standard application, the proposed classifier decreased the effort and time taken to interact by 36.56% and 66.34%. Furthermore, the proposed model's performance is evaluated by its accuracy and efficiency of the hybrid AI classifier and compared with other AI classifiers. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Dyslexia students frequently deal with multiple difficulties in daily life, involving social interactions throughout their lives. Sometimes they are quickly refused the chance to indulge in social events since they suffer difficulty in learning, reading, understanding, etc. AAC seems to be a vital communication aid for dyslexia students by providing an augmented reality (AR) paradigm to effective learning. This paper enhances the existing learning assistance technologies with innovative Artificial Intelligence (AI) to reinvigorate the Augmentative Alternative Communication ($ A^{2} $C) model for dyslexia children. The AI-based Augmentative Alternative Communication Approach has been developed to enhance learning skills with dyslexia by adapting to practices, and learning models are cognitively considered. The work on the academic skills of dyslexia students has been improved through the AI-based alternative communication paradigm for the improvement of the students with reading and learning. The AI-based AAC (AI–$ A^{2} $C) integrates the hybrid AI classifier in AAC to classify unique questions and provide users with the most appropriate pictograms. In contrast to the standard application, the proposed classifier decreased the effort and time taken to interact by 36.56% and 66.34%. Furthermore, the proposed model's performance is evaluated by its accuracy and efficiency of the hybrid AI classifier and compared with other AI classifiers. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Smart assistance to dyslexia students using artificial intelligence based augmentative alternative communication |
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https://dx.doi.org/10.1007/s10772-021-09921-0 |
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Muthu, BalaAnand Sivaparthipan, C. B. |
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Muthu, BalaAnand Sivaparthipan, C. B. |
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| doi_str |
10.1007/s10772-021-09921-0 |
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2024-07-04T02:52:06.440Z |
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