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Advanced Computational Methods for Oncological Image Analysis
| Advanced Computational Methods for Oncological Image Analysis |
| Autore | Rundo Leonardo |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (262 p.) |
| Soggetto topico | Medicine |
| Soggetto non controllato |
melanoma detection
deep learning transfer learning ensemble classification 3D-CNN immunotherapy radiomics self-attention breast imaging microwave imaging image reconstruction segmentation unsupervised machine learning k-means clustering Kolmogorov-Smirnov hypothesis test statistical inference performance metrics contrast source inversion brain tumor segmentation magnetic resonance imaging survey brain MRI image tumor region skull stripping region growing U-Net BRATS dataset incoherent imaging clutter rejection breast cancer detection MRgFUS proton resonance frequency shift temperature variations referenceless thermometry RBF neural networks interferometric optical fibers breast cancer risk assessment machine learning texture mammography medical imaging imaging biomarkers bone scintigraphy prostate cancer semisupervised classification false positives reduction computer-aided detection breast mass mass detection mass segmentation Mask R-CNN dataset partition brain tumor classification shallow machine learning breast cancer diagnosis Wisconsin Breast Cancer Dataset feature selection dimensionality reduction principal component analysis ensemble method |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557353503321 |
Rundo Leonardo
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming
| Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming |
| Autore | Qiao Yongliang |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (228 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
pig weight
body size estimation deep learning convolutional neural network pig identification mask scoring R-CNN soft-NMS group-housed pigs audio dairy cow mastication jaw movement forage management precision livestock management equine behavior wearable sensor intermodality interaction class-balanced focal loss absorbing Markov chain cow behavior analysis prediction of calving time cow identification EfficientDet YOLACT++ cascaded model instance segmentation generative adversarial network machine learning automated medical image processing deep neural network animal science CT scans computer vision cow extensive livestock sensorized wearable device monitoring parturition prediction radar sensors radar signal processing animal farming computational ethology signal classification wavelet analysis dairy welfare hierarchical clustering mutual information precision livestock farming time budgets unsupervised machine learning wearables design animal-centered design animal telemetry modularity smart collar design contributions additive manufacturing low-frequency tracking commercial aviary laying hens false registrations tree-based classifier animal behaviour |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910576879803321 |
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Qiao Yongliang
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics
| Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics |
| Autore | Fritzen Felix |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (254 p.) |
| Soggetto non controllato |
supervised machine learning
proper orthogonal decomposition (POD) PGD compression stabilization nonlinear reduced order model gappy POD symplectic model order reduction neural network snapshot proper orthogonal decomposition 3D reconstruction microstructure property linkage nonlinear material behaviour proper orthogonal decomposition reduced basis ECSW geometric nonlinearity POD model order reduction elasto-viscoplasticity sampling surrogate modeling model reduction enhanced POD archive modal analysis low-rank approximation computational homogenization artificial neural networks unsupervised machine learning large strain reduced-order model proper generalised decomposition (PGD) a priori enrichment elastoviscoplastic behavior error indicator computational homogenisation empirical cubature method nonlinear structural mechanics reduced integration domain model order reduction (MOR) structure preservation of symplecticity heterogeneous data reduced order modeling (ROM) parameter-dependent model data science Hencky strain dynamic extrapolation tensor-train decomposition hyper-reduction empirical cubature randomised SVD machine learning inverse problem plasticity proper symplectic decomposition (PSD) finite deformation Hamiltonian system DEIM GNAT |
| ISBN | 3-03921-410-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910367759403321 |
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Fritzen Felix
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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