Autonomous penetration detection for bone cutting tool using demonstration-based learning
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: 2014DOI: 10.1109/EMBC.2014.6944578
Keyword(s): Big Data,biomedical equipment,data analysis,decision support systems,feature extraction,feature selection,health care,knowledge acquisition,medical computing,patient care,patient monitoring,surgery,ICU datasets,analytical tools,big data handling,biomedical instrumentation,clinical decisions,data categorization,data preprocessing,data-driven analytics,data-driven knowledge extraction,data-driven methodology,data-driven methods,data-rich environment,database,feature extrac
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Autonomous penetration detection for bone cutting tool using demonstration-based learning
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| date accessioned | 2020-03-12T20:47:23Z | |
| date available | 2020-03-12T20:47:23Z | |
| date issued | 2014 | |
| identifier other | 6906624.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1025547 | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | Autonomous penetration detection for bone cutting tool using demonstration-based learning | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8150550 | |
| subject keywords | Big Data | |
| subject keywords | biomedical equipment | |
| subject keywords | data analysis | |
| subject keywords | decision support systems | |
| subject keywords | feature extraction | |
| subject keywords | feature selection | |
| subject keywords | health care | |
| subject keywords | knowledge acquisition | |
| subject keywords | medical computing | |
| subject keywords | patient care | |
| subject keywords | patient monitoring | |
| subject keywords | surgery | |
| subject keywords | ICU datasets | |
| subject keywords | analytical tools | |
| subject keywords | big data handling | |
| subject keywords | biomedical instrumentation | |
| subject keywords | clinical decisions | |
| subject keywords | data categorization | |
| subject keywords | data preprocessing | |
| subject keywords | data-driven analytics | |
| subject keywords | data-driven knowledge extraction | |
| subject keywords | data-driven methodology | |
| subject keywords | data-driven methods | |
| subject keywords | data-rich environment | |
| subject keywords | database | |
| subject keywords | feature extrac | |
| identifier doi | 10.1109/EMBC.2014.6944578 | |
| journal title | obotics and Automation (ICRA), 2014 IEEE International Conference on | |
| filesize | 1642836 | |
| citations | 0 | |
| contributor rawauthor | Osa, T. , Abawi, C.F. , Sugita, N. , Chikuda, H. , Sugita, S. , Ito, H. , Moro, T. , Takatori, Y. , Tanaka, S. , Mitsuishi, M. |