High-performance medical data processing technology based on distributed parallel machine learning algorithm
Abstract The aim is to improve the efficiency of medical data processing and establish a sound medical data management system. To apply distributed parallel classification algorithms in the field of hospital intelligent guidance, a Parallel Random Forest (PRF) classification algorithm is proposed ba...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Ji [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
Adaptive density peak clustering algorithm |
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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: The journal of supercomputing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 78(2021), 4 vom: 07. Okt., Seite 5933-5956 |
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| Übergeordnetes Werk: |
volume:78 ; year:2021 ; number:4 ; day:07 ; month:10 ; pages:5933-5956 |
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| DOI / URN: |
10.1007/s11227-021-04060-4 |
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| Katalog-ID: |
SPR046492135 |
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| 520 | |a Abstract The aim is to improve the efficiency of medical data processing and establish a sound medical data management system. To apply distributed parallel classification algorithms in the field of hospital intelligent guidance, a Parallel Random Forest (PRF) classification algorithm is proposed based on the Apache Spark cloud computing platform. Given sparse cluster loss in variable density distribution data sets, an Adaptive Domain Density Peak Clustering (ADDPC) method is proposed. Here, a Bilayer Parallel Training-Convolutional Neural Network (BPT-CNN) model based on distributed computing is proposed to detect and classify colon cancer nuclei more accurately through the large-scale parallel deep learning (DL) algorithm. Then, the performance of the proposed model is evaluated through case analysis. The results show that the PRF algorithm based on distributed cloud computing platform can independently design data-parallel tasks, thereby optimizing the data communication cost and efficiency. ADDPC algorithm can adaptively measure domain density and merge sparse clusters to prevent data loss and fragmentation. The BPT-CNN model improves the performance of the algorithm and balances the workload of each task in the algorithm. The results have a significant reference value for solving problems in medical data processing. | ||
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| 700 | 1 | |a Liang, Xiao |4 aut | |
| 700 | 1 | |a Ruan, Wenxi |4 aut | |
| 700 | 1 | |a Zhang, Bo |4 aut | |
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10.1007/s11227-021-04060-4 doi (DE-627)SPR046492135 (SPR)s11227-021-04060-4-e DE-627 ger DE-627 rakwb eng Liu, Ji verfasserin aut High-performance medical data processing technology based on distributed parallel machine learning algorithm 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 The aim is to improve the efficiency of medical data processing and establish a sound medical data management system. To apply distributed parallel classification algorithms in the field of hospital intelligent guidance, a Parallel Random Forest (PRF) classification algorithm is proposed based on the Apache Spark cloud computing platform. Given sparse cluster loss in variable density distribution data sets, an Adaptive Domain Density Peak Clustering (ADDPC) method is proposed. Here, a Bilayer Parallel Training-Convolutional Neural Network (BPT-CNN) model based on distributed computing is proposed to detect and classify colon cancer nuclei more accurately through the large-scale parallel deep learning (DL) algorithm. Then, the performance of the proposed model is evaluated through case analysis. The results show that the PRF algorithm based on distributed cloud computing platform can independently design data-parallel tasks, thereby optimizing the data communication cost and efficiency. ADDPC algorithm can adaptively measure domain density and merge sparse clusters to prevent data loss and fragmentation. The BPT-CNN model improves the performance of the algorithm and balances the workload of each task in the algorithm. The results have a significant reference value for solving problems in medical data processing. Adaptive density peak clustering algorithm (dpeaa)DE-He213 Random forest algorithm (dpeaa)DE-He213 Distributed parallel classification algorithm (dpeaa)DE-He213 Cloud computing (dpeaa)DE-He213 Liang, Xiao aut Ruan, Wenxi aut Zhang, Bo aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 78(2021), 4 vom: 07. Okt., Seite 5933-5956 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:78 year:2021 number:4 day:07 month:10 pages:5933-5956 https://dx.doi.org/10.1007/s11227-021-04060-4 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_101 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_206 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_2119 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 78 2021 4 07 10 5933-5956 |
| spelling |
10.1007/s11227-021-04060-4 doi (DE-627)SPR046492135 (SPR)s11227-021-04060-4-e DE-627 ger DE-627 rakwb eng Liu, Ji verfasserin aut High-performance medical data processing technology based on distributed parallel machine learning algorithm 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 The aim is to improve the efficiency of medical data processing and establish a sound medical data management system. To apply distributed parallel classification algorithms in the field of hospital intelligent guidance, a Parallel Random Forest (PRF) classification algorithm is proposed based on the Apache Spark cloud computing platform. Given sparse cluster loss in variable density distribution data sets, an Adaptive Domain Density Peak Clustering (ADDPC) method is proposed. Here, a Bilayer Parallel Training-Convolutional Neural Network (BPT-CNN) model based on distributed computing is proposed to detect and classify colon cancer nuclei more accurately through the large-scale parallel deep learning (DL) algorithm. Then, the performance of the proposed model is evaluated through case analysis. The results show that the PRF algorithm based on distributed cloud computing platform can independently design data-parallel tasks, thereby optimizing the data communication cost and efficiency. ADDPC algorithm can adaptively measure domain density and merge sparse clusters to prevent data loss and fragmentation. The BPT-CNN model improves the performance of the algorithm and balances the workload of each task in the algorithm. The results have a significant reference value for solving problems in medical data processing. Adaptive density peak clustering algorithm (dpeaa)DE-He213 Random forest algorithm (dpeaa)DE-He213 Distributed parallel classification algorithm (dpeaa)DE-He213 Cloud computing (dpeaa)DE-He213 Liang, Xiao aut Ruan, Wenxi aut Zhang, Bo aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 78(2021), 4 vom: 07. Okt., Seite 5933-5956 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:78 year:2021 number:4 day:07 month:10 pages:5933-5956 https://dx.doi.org/10.1007/s11227-021-04060-4 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_101 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_206 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_2119 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 78 2021 4 07 10 5933-5956 |
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10.1007/s11227-021-04060-4 doi (DE-627)SPR046492135 (SPR)s11227-021-04060-4-e DE-627 ger DE-627 rakwb eng Liu, Ji verfasserin aut High-performance medical data processing technology based on distributed parallel machine learning algorithm 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 The aim is to improve the efficiency of medical data processing and establish a sound medical data management system. To apply distributed parallel classification algorithms in the field of hospital intelligent guidance, a Parallel Random Forest (PRF) classification algorithm is proposed based on the Apache Spark cloud computing platform. Given sparse cluster loss in variable density distribution data sets, an Adaptive Domain Density Peak Clustering (ADDPC) method is proposed. Here, a Bilayer Parallel Training-Convolutional Neural Network (BPT-CNN) model based on distributed computing is proposed to detect and classify colon cancer nuclei more accurately through the large-scale parallel deep learning (DL) algorithm. Then, the performance of the proposed model is evaluated through case analysis. The results show that the PRF algorithm based on distributed cloud computing platform can independently design data-parallel tasks, thereby optimizing the data communication cost and efficiency. ADDPC algorithm can adaptively measure domain density and merge sparse clusters to prevent data loss and fragmentation. The BPT-CNN model improves the performance of the algorithm and balances the workload of each task in the algorithm. The results have a significant reference value for solving problems in medical data processing. Adaptive density peak clustering algorithm (dpeaa)DE-He213 Random forest algorithm (dpeaa)DE-He213 Distributed parallel classification algorithm (dpeaa)DE-He213 Cloud computing (dpeaa)DE-He213 Liang, Xiao aut Ruan, Wenxi aut Zhang, Bo aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 78(2021), 4 vom: 07. Okt., Seite 5933-5956 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:78 year:2021 number:4 day:07 month:10 pages:5933-5956 https://dx.doi.org/10.1007/s11227-021-04060-4 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_101 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_206 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_2119 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 78 2021 4 07 10 5933-5956 |
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10.1007/s11227-021-04060-4 doi (DE-627)SPR046492135 (SPR)s11227-021-04060-4-e DE-627 ger DE-627 rakwb eng Liu, Ji verfasserin aut High-performance medical data processing technology based on distributed parallel machine learning algorithm 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 The aim is to improve the efficiency of medical data processing and establish a sound medical data management system. To apply distributed parallel classification algorithms in the field of hospital intelligent guidance, a Parallel Random Forest (PRF) classification algorithm is proposed based on the Apache Spark cloud computing platform. Given sparse cluster loss in variable density distribution data sets, an Adaptive Domain Density Peak Clustering (ADDPC) method is proposed. Here, a Bilayer Parallel Training-Convolutional Neural Network (BPT-CNN) model based on distributed computing is proposed to detect and classify colon cancer nuclei more accurately through the large-scale parallel deep learning (DL) algorithm. Then, the performance of the proposed model is evaluated through case analysis. The results show that the PRF algorithm based on distributed cloud computing platform can independently design data-parallel tasks, thereby optimizing the data communication cost and efficiency. ADDPC algorithm can adaptively measure domain density and merge sparse clusters to prevent data loss and fragmentation. The BPT-CNN model improves the performance of the algorithm and balances the workload of each task in the algorithm. The results have a significant reference value for solving problems in medical data processing. Adaptive density peak clustering algorithm (dpeaa)DE-He213 Random forest algorithm (dpeaa)DE-He213 Distributed parallel classification algorithm (dpeaa)DE-He213 Cloud computing (dpeaa)DE-He213 Liang, Xiao aut Ruan, Wenxi aut Zhang, Bo aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 78(2021), 4 vom: 07. Okt., Seite 5933-5956 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:78 year:2021 number:4 day:07 month:10 pages:5933-5956 https://dx.doi.org/10.1007/s11227-021-04060-4 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_101 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_206 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_2119 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 78 2021 4 07 10 5933-5956 |
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10.1007/s11227-021-04060-4 doi (DE-627)SPR046492135 (SPR)s11227-021-04060-4-e DE-627 ger DE-627 rakwb eng Liu, Ji verfasserin aut High-performance medical data processing technology based on distributed parallel machine learning algorithm 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 The aim is to improve the efficiency of medical data processing and establish a sound medical data management system. To apply distributed parallel classification algorithms in the field of hospital intelligent guidance, a Parallel Random Forest (PRF) classification algorithm is proposed based on the Apache Spark cloud computing platform. Given sparse cluster loss in variable density distribution data sets, an Adaptive Domain Density Peak Clustering (ADDPC) method is proposed. Here, a Bilayer Parallel Training-Convolutional Neural Network (BPT-CNN) model based on distributed computing is proposed to detect and classify colon cancer nuclei more accurately through the large-scale parallel deep learning (DL) algorithm. Then, the performance of the proposed model is evaluated through case analysis. The results show that the PRF algorithm based on distributed cloud computing platform can independently design data-parallel tasks, thereby optimizing the data communication cost and efficiency. ADDPC algorithm can adaptively measure domain density and merge sparse clusters to prevent data loss and fragmentation. The BPT-CNN model improves the performance of the algorithm and balances the workload of each task in the algorithm. The results have a significant reference value for solving problems in medical data processing. Adaptive density peak clustering algorithm (dpeaa)DE-He213 Random forest algorithm (dpeaa)DE-He213 Distributed parallel classification algorithm (dpeaa)DE-He213 Cloud computing (dpeaa)DE-He213 Liang, Xiao aut Ruan, Wenxi aut Zhang, Bo aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 78(2021), 4 vom: 07. Okt., Seite 5933-5956 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:78 year:2021 number:4 day:07 month:10 pages:5933-5956 https://dx.doi.org/10.1007/s11227-021-04060-4 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_101 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_206 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_2119 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 78 2021 4 07 10 5933-5956 |
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high-performance medical data processing technology based on distributed parallel machine learning algorithm |
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High-performance medical data processing technology based on distributed parallel machine learning algorithm |
| abstract |
Abstract The aim is to improve the efficiency of medical data processing and establish a sound medical data management system. To apply distributed parallel classification algorithms in the field of hospital intelligent guidance, a Parallel Random Forest (PRF) classification algorithm is proposed based on the Apache Spark cloud computing platform. Given sparse cluster loss in variable density distribution data sets, an Adaptive Domain Density Peak Clustering (ADDPC) method is proposed. Here, a Bilayer Parallel Training-Convolutional Neural Network (BPT-CNN) model based on distributed computing is proposed to detect and classify colon cancer nuclei more accurately through the large-scale parallel deep learning (DL) algorithm. Then, the performance of the proposed model is evaluated through case analysis. The results show that the PRF algorithm based on distributed cloud computing platform can independently design data-parallel tasks, thereby optimizing the data communication cost and efficiency. ADDPC algorithm can adaptively measure domain density and merge sparse clusters to prevent data loss and fragmentation. The BPT-CNN model improves the performance of the algorithm and balances the workload of each task in the algorithm. The results have a significant reference value for solving problems in medical data processing. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract The aim is to improve the efficiency of medical data processing and establish a sound medical data management system. To apply distributed parallel classification algorithms in the field of hospital intelligent guidance, a Parallel Random Forest (PRF) classification algorithm is proposed based on the Apache Spark cloud computing platform. Given sparse cluster loss in variable density distribution data sets, an Adaptive Domain Density Peak Clustering (ADDPC) method is proposed. Here, a Bilayer Parallel Training-Convolutional Neural Network (BPT-CNN) model based on distributed computing is proposed to detect and classify colon cancer nuclei more accurately through the large-scale parallel deep learning (DL) algorithm. Then, the performance of the proposed model is evaluated through case analysis. The results show that the PRF algorithm based on distributed cloud computing platform can independently design data-parallel tasks, thereby optimizing the data communication cost and efficiency. ADDPC algorithm can adaptively measure domain density and merge sparse clusters to prevent data loss and fragmentation. The BPT-CNN model improves the performance of the algorithm and balances the workload of each task in the algorithm. The results have a significant reference value for solving problems in medical data processing. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract The aim is to improve the efficiency of medical data processing and establish a sound medical data management system. To apply distributed parallel classification algorithms in the field of hospital intelligent guidance, a Parallel Random Forest (PRF) classification algorithm is proposed based on the Apache Spark cloud computing platform. Given sparse cluster loss in variable density distribution data sets, an Adaptive Domain Density Peak Clustering (ADDPC) method is proposed. Here, a Bilayer Parallel Training-Convolutional Neural Network (BPT-CNN) model based on distributed computing is proposed to detect and classify colon cancer nuclei more accurately through the large-scale parallel deep learning (DL) algorithm. Then, the performance of the proposed model is evaluated through case analysis. The results show that the PRF algorithm based on distributed cloud computing platform can independently design data-parallel tasks, thereby optimizing the data communication cost and efficiency. ADDPC algorithm can adaptively measure domain density and merge sparse clusters to prevent data loss and fragmentation. The BPT-CNN model improves the performance of the algorithm and balances the workload of each task in the algorithm. The results have a significant reference value for solving problems in medical data processing. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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High-performance medical data processing technology based on distributed parallel machine learning algorithm |
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https://dx.doi.org/10.1007/s11227-021-04060-4 |
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Liang, Xiao Ruan, Wenxi Zhang, Bo |
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10.1007/s11227-021-04060-4 |
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2024-07-03T22:52:12.591Z |
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| score |
7.3995323 |