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1. |
Record Nr. |
UNISA996397213003316 |
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Autore |
Titus Silius <1623?-1704.> |
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Titolo |
A seasonable speech, made by a worthy member of Parliament in the House of Commons. Concerning the other House. March 1659 [[electronic resource]] |
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Pubbl/distr/stampa |
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Descrizione fisica |
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Altri autori (Persone) |
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ShaftesburyAnthony Ashley Cooper, Earl of, <1621-1683.> |
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Soggetti |
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Great Britain Politics and government 1649-1660 Early works to 1800 |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Attributed to Silius Titus by ESTC. Wing attributes to Anthony Ashley Cooper, Earl of Shaftesbury. |
Caption title. |
Place and date of publication suggested by Wing (2nd ed.). |
In this edition line 5 of caption title reads: "Commons."; signatures: A⁴. |
Imperfect: stained; print show-through with some loss of text. |
Reproduction of original in: Henry E. Huntington Library and Art Gallery. |
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Sommario/riassunto |
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2. |
Record Nr. |
UNINA9910983482503321 |
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Titolo |
Predictive Intelligence in Medicine : 7th International Workshop, PRIME 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings / / edited by Islem Rekik, Ehsan Adeli, Sang Hyun Park, Celia Cintas |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (XII, 208 p. 73 illus., 67 illus. in color.) |
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Collana |
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Lecture Notes in Computer Science, , 1611-3349 ; ; 15155 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Modelling the Neonatal Brain Development Using Implicit Neural Representations -- Attention Based Features Fusion Emotion Guided fNIRS Classification Network for Prenatal Depression Recognition -- Spectral Graph Sample Weighting for Interpretable Sub cohort Analysis in Predictive Models for Neuroimaging -- RCT Relational Connectivity Transformer for Enhanced Prediction of Absolute and Residual Intelligence -- Gene to Image Decoding Brain Images from Genetics via Latent Diffusion Models -- Physics Guided Multi View Graph Neural Network for Schizophrenia Classification via Structural Functional Coupling -- Automated Patient Specific Pneumoperitoneum Model Reconstruction for Surgical Navigation Systems in Distal Gastrectomy -- MNA net Multimodal Neuroimaging Attention based Architecture for Cognitive Decline Prediction -- Improving Brain MRI Segmentation with Multi Stage Deep Domain Unlearning -- DynGNN Dynamic Memory enhanced Generative GNNs for Predicting Temporal Brain Connectivity -- Strongly Topology preserving GNNs for Brain Graph Super resolution -- Generative Hypergraph Neural Network for Multiview Brain Connectivity Fusion -- Identifying brain ageing trajectories using variational autoencoders with regression model in neuroimaging data stratified by sex and validated against dementia related risk factors -- |
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Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction -- Self-Supervised Contrastive Learning for Consistent Few Shot Image Representations -- Neurocognitive Latent Space Regularization for Multi Label Diagnosis from MRI -- Segmentation of Brain Metastases in MRI A Two Stage Deep Learning Approach with Modality Impact Study. |
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Sommario/riassunto |
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This volume constitutes the refereed proceedings of the 7th International Workshop on Predictive Intelligence in Medicine, PRIME 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco in October 2024. The 17 full papers presented here were carefully reviewed and selected from 22 submissions. These papers focus on the current, cutting-edge Predictive models and methods with applications in the field of Medical data analysis, for early disease prediction and prevention. . |
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