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Kết quả 71 - 80 của 3.168  trong Tất cả tài nguyên

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71
Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier
Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier
Material Type:
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Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier

Remote sensing (Basel, Switzerland), 2020-01, Vol.12 (2), p.266 [Tạp chí có phản biện]

2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs12020266

Tài liệu số/Tài liệu điện tử

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72
Detection of Intrinsically Resistant Candida in Mixed Samples by MALDI TOF-MS and a Modified Naïve Bayesian Classifier
Detection of Intrinsically Resistant Candida in Mixed Samples by MALDI TOF-MS and a Modified Naïve Bayesian Classifier
Material Type:
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Detection of Intrinsically Resistant Candida in Mixed Samples by MALDI TOF-MS and a Modified Naïve Bayesian Classifier

Molecules (Basel, Switzerland), 2021-07, Vol.26 (15), p.4470 [Tạp chí có phản biện]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2021 by the authors. 2021 ;ISSN: 1420-3049 ;EISSN: 1420-3049 ;DOI: 10.3390/molecules26154470 ;PMID: 34361627

Tài liệu số/Tài liệu điện tử

Trích dẫn Trích dẫn bởi
73
Exploratory study on classification of diabetes mellitus through a combined Random Forest Classifier
Exploratory study on classification of diabetes mellitus through a combined Random Forest Classifier
Material Type:
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Exploratory study on classification of diabetes mellitus through a combined Random Forest Classifier

BMC medical informatics and decision making, 2021-03, Vol.21 (1), p.105-105, Article 105 [Tạp chí có phản biện]

COPYRIGHT 2021 BioMed Central Ltd. ;COPYRIGHT 2021 BioMed Central Ltd. ;2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2021 ;ISSN: 1472-6947 ;EISSN: 1472-6947 ;DOI: 10.1186/s12911-021-01471-4 ;PMID: 33743696

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74
Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging
Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging
Material Type:
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Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging

European radiology, 2019-08, Vol.29 (8), p.4068-4076 [Tạp chí có phản biện]

European Society of Radiology 2018 ;European Radiology is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-018-5830-3 ;PMID: 30443758

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75
Analysis and prediction of human acetylation using a cascade classifier based on support vector machine
Analysis and prediction of human acetylation using a cascade classifier based on support vector machine
Material Type:
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Analysis and prediction of human acetylation using a cascade classifier based on support vector machine

BMC bioinformatics, 2019-06, Vol.20 (1), p.346-346, Article 346 [Tạp chí có phản biện]

COPYRIGHT 2019 BioMed Central Ltd. ;2019. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s). 2019 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-019-2938-7 ;PMID: 31208321

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Trích dẫn Trích dẫn bởi
76
Enhancing ontology-driven diagnostic reasoning with a symptom-dependency-aware Naïve Bayes classifier
Enhancing ontology-driven diagnostic reasoning with a symptom-dependency-aware Naïve Bayes classifier
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Enhancing ontology-driven diagnostic reasoning with a symptom-dependency-aware Naïve Bayes classifier

BMC bioinformatics, 2019-06, Vol.20 (1), p.330-330, Article 330 [Tạp chí có phản biện]

2019. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s). 2019 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-019-2924-0 ;PMID: 31196129

Tài liệu số/Tài liệu điện tử

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77
CarSite-II: an integrated classification algorithm for identifying carbonylated sites based on K-means similarity-based undersampling and synthetic minority oversampling techniques
CarSite-II: an integrated classification algorithm for identifying carbonylated sites based on K-means similarity-based undersampling and synthetic minority oversampling techniques
Material Type:
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CarSite-II: an integrated classification algorithm for identifying carbonylated sites based on K-means similarity-based undersampling and synthetic minority oversampling techniques

BMC bioinformatics, 2021-04, Vol.22 (1), p.216-216, Article 216 [Tạp chí có phản biện]

COPYRIGHT 2021 BioMed Central Ltd. ;2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2021 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-021-04134-3 ;PMID: 33902446

Tài liệu số/Tài liệu điện tử

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78
Predicting rifampicin resistance mutations in bacterial RNA polymerase subunit beta based on majority consensus
Predicting rifampicin resistance mutations in bacterial RNA polymerase subunit beta based on majority consensus
Material Type:
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Predicting rifampicin resistance mutations in bacterial RNA polymerase subunit beta based on majority consensus

BMC bioinformatics, 2021-04, Vol.22 (1), p.210-16, Article 210 [Tạp chí có phản biện]

2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2021 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-021-04137-0 ;PMID: 33888055

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79
Towards Benthic Habitat 3D Mapping Using Machine Learning Algorithms and Structures from Motion Photogrammetry
Towards Benthic Habitat 3D Mapping Using Machine Learning Algorithms and Structures from Motion Photogrammetry
Material Type:
Bài báo 		Thêm vào Góc nghiên cứu

Towards Benthic Habitat 3D Mapping Using Machine Learning Algorithms and Structures from Motion Photogrammetry

Remote sensing (Basel, Switzerland), 2020-01, Vol.12 (1), p.127 [Tạp chí có phản biện]

2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs12010127

Tài liệu số/Tài liệu điện tử

Trích dẫn Trích dẫn bởi
80
Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma
Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma
Material Type:
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Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma

European radiology, 2018-04, Vol.28 (4), p.1625-1633 [Tạp chí có phản biện]

European Society of Radiology 2017 ;European Radiology is a copyright of Springer, (2017). All Rights Reserved. ;ISSN: 0938-7994 ;EISSN: 1432-1084 ;DOI: 10.1007/s00330-017-5118-z ;PMID: 29134348

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Kết quả 71 - 80 của 3.168  trong Tất cả tài nguyên

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