Ballet E-learning using fuzzy set induced posture recognition by piece-wise linear approximation of connected components
• The present work has aimed at recognition of ballet posture by a learner at home, while he/she is executing the posture following the same of a trained dancer in the video. • The optimal polygonal approximation of boundary is here realized using a modified version of the traditional artificial bee...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Saha, Sriparna [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
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| Schlagwörter: |
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| Umfang: |
23 |
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| Übergeordnetes Werk: |
Enthalten in: Atomic collapse in graphene quantum dots in a magnetic field - Eren, I. ELSEVIER, 2022, the official journal of the World Federation on Soft Computing (WFSC), Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:65 ; year:2018 ; pages:554-576 ; extent:23 |
| Links: |
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| DOI / URN: |
10.1016/j.asoc.2018.01.043 |
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ELV042380804 |
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| 520 | |a • The present work has aimed at recognition of ballet posture by a learner at home, while he/she is executing the posture following the same of a trained dancer in the video. • The optimal polygonal approximation of boundary is here realized using a modified version of the traditional artificial bee colony (ABC) algorithm. • The relative performance of all the algorithms has been compared on the basis of seven performance metrics, including recall, specificity, precision, accuracy, average error rate, receiver operating characteristic curve and area under curve. • The quality performance of the proposed algorithm is substantiated by the reported simulation results, with an accuracy of 91.23%. • Statistical significance of the results has been ascertained by Wilcoxon rank-sum test and the McNemar’s test. | ||
| 520 | |a • The present work has aimed at recognition of ballet posture by a learner at home, while he/she is executing the posture following the same of a trained dancer in the video. • The optimal polygonal approximation of boundary is here realized using a modified version of the traditional artificial bee colony (ABC) algorithm. • The relative performance of all the algorithms has been compared on the basis of seven performance metrics, including recall, specificity, precision, accuracy, average error rate, receiver operating characteristic curve and area under curve. • The quality performance of the proposed algorithm is substantiated by the reported simulation results, with an accuracy of 91.23%. • Statistical significance of the results has been ascertained by Wilcoxon rank-sum test and the McNemar’s test. | ||
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10.1016/j.asoc.2018.01.043 doi GBV00000000000625.pica (DE-627)ELV042380804 (ELSEVIER)S1568-4946(18)30049-8 DE-627 ger DE-627 rakwb eng 540 530 VZ 33.00 bkl Saha, Sriparna verfasserin aut Ballet E-learning using fuzzy set induced posture recognition by piece-wise linear approximation of connected components 2018transfer abstract 23 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • The present work has aimed at recognition of ballet posture by a learner at home, while he/she is executing the posture following the same of a trained dancer in the video. • The optimal polygonal approximation of boundary is here realized using a modified version of the traditional artificial bee colony (ABC) algorithm. • The relative performance of all the algorithms has been compared on the basis of seven performance metrics, including recall, specificity, precision, accuracy, average error rate, receiver operating characteristic curve and area under curve. • The quality performance of the proposed algorithm is substantiated by the reported simulation results, with an accuracy of 91.23%. • Statistical significance of the results has been ascertained by Wilcoxon rank-sum test and the McNemar’s test. • The present work has aimed at recognition of ballet posture by a learner at home, while he/she is executing the posture following the same of a trained dancer in the video. • The optimal polygonal approximation of boundary is here realized using a modified version of the traditional artificial bee colony (ABC) algorithm. • The relative performance of all the algorithms has been compared on the basis of seven performance metrics, including recall, specificity, precision, accuracy, average error rate, receiver operating characteristic curve and area under curve. • The quality performance of the proposed algorithm is substantiated by the reported simulation results, with an accuracy of 91.23%. • Statistical significance of the results has been ascertained by Wilcoxon rank-sum test and the McNemar’s test. E-learning Elsevier Type-1 fuzzy set Elsevier Modified artificial bee colony Elsevier Ballet Elsevier Piece-wise linear approximation Elsevier Rakshit, Pratyusha oth Konar, Amit oth Enthalten in Elsevier Science Eren, I. ELSEVIER Atomic collapse in graphene quantum dots in a magnetic field 2022 the official journal of the World Federation on Soft Computing (WFSC) Amsterdam [u.a.] (DE-627)ELV007866305 volume:65 year:2018 pages:554-576 extent:23 https://doi.org/10.1016/j.asoc.2018.01.043 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 65 2018 554-576 23 |
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Elektronische Aufsätze |
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Saha, Sriparna |
| doi_str_mv |
10.1016/j.asoc.2018.01.043 |
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540 530 |
| title_sort |
ballet e-learning using fuzzy set induced posture recognition by piece-wise linear approximation of connected components |
| title_auth |
Ballet E-learning using fuzzy set induced posture recognition by piece-wise linear approximation of connected components |
| abstract |
• The present work has aimed at recognition of ballet posture by a learner at home, while he/she is executing the posture following the same of a trained dancer in the video. • The optimal polygonal approximation of boundary is here realized using a modified version of the traditional artificial bee colony (ABC) algorithm. • The relative performance of all the algorithms has been compared on the basis of seven performance metrics, including recall, specificity, precision, accuracy, average error rate, receiver operating characteristic curve and area under curve. • The quality performance of the proposed algorithm is substantiated by the reported simulation results, with an accuracy of 91.23%. • Statistical significance of the results has been ascertained by Wilcoxon rank-sum test and the McNemar’s test. |
| abstractGer |
• The present work has aimed at recognition of ballet posture by a learner at home, while he/she is executing the posture following the same of a trained dancer in the video. • The optimal polygonal approximation of boundary is here realized using a modified version of the traditional artificial bee colony (ABC) algorithm. • The relative performance of all the algorithms has been compared on the basis of seven performance metrics, including recall, specificity, precision, accuracy, average error rate, receiver operating characteristic curve and area under curve. • The quality performance of the proposed algorithm is substantiated by the reported simulation results, with an accuracy of 91.23%. • Statistical significance of the results has been ascertained by Wilcoxon rank-sum test and the McNemar’s test. |
| abstract_unstemmed |
• The present work has aimed at recognition of ballet posture by a learner at home, while he/she is executing the posture following the same of a trained dancer in the video. • The optimal polygonal approximation of boundary is here realized using a modified version of the traditional artificial bee colony (ABC) algorithm. • The relative performance of all the algorithms has been compared on the basis of seven performance metrics, including recall, specificity, precision, accuracy, average error rate, receiver operating characteristic curve and area under curve. • The quality performance of the proposed algorithm is substantiated by the reported simulation results, with an accuracy of 91.23%. • Statistical significance of the results has been ascertained by Wilcoxon rank-sum test and the McNemar’s test. |
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| title_short |
Ballet E-learning using fuzzy set induced posture recognition by piece-wise linear approximation of connected components |
| url |
https://doi.org/10.1016/j.asoc.2018.01.043 |
| remote_bool |
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| author2 |
Rakshit, Pratyusha Konar, Amit |
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Rakshit, Pratyusha Konar, Amit |
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| up_date |
2024-07-06T22:38:53.503Z |
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