| Nome |
# |
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1-D Convolutional Neural Network for ECG Arrhythmia Classification, file e384c431-1b62-d4b2-e053-9f05fe0a1d67
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318
|
|
Novel neural approaches to data topology analysis and telemedicine, file e384c432-d1d9-d4b2-e053-9f05fe0a1d67
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262
|
|
A wearable smart device to monitor multiple vital parameters—VITAL ECG, file e384c431-b4d9-d4b2-e053-9f05fe0a1d67
|
121
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Neural Recurrent Approches to Noninvasive Blood Pressure Estimation, file e384c432-b995-d4b2-e053-9f05fe0a1d67
|
116
|
|
Unsupervised Multi-Omic Data Fusion: the Neural Graph Learning Network, file e384c432-e5be-d4b2-e053-9f05fe0a1d67
|
80
|
|
A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction, file e384c433-a84c-d4b2-e053-9f05fe0a1d67
|
80
|
|
Noninvasive Arterial Blood Pressure Estimation using ABPNet and VITAL-ECG, file e384c432-423c-d4b2-e053-9f05fe0a1d67
|
72
|
|
Towards Uncovering Feature Extraction from Temporal Signals in Deep CNN: The ECG Case Study, file e384c432-e3fc-d4b2-e053-9f05fe0a1d67
|
64
|
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Growing Curvilinear Component Analysis (GCCA) for Dimensionality Reduction of Nonstationary Data, file e384c432-3ae8-d4b2-e053-9f05fe0a1d67
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63
|
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Novel neural approaches to data topology analysis and telemedicine, file e384c432-e56c-d4b2-e053-9f05fe0a1d67
|
55
|
|
A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients, file e384c433-57f7-d4b2-e053-9f05fe0a1d67
|
54
|
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The Growing Curvilinear Component Analysis (GCCA) neural network, file e384c430-1fbf-d4b2-e053-9f05fe0a1d67
|
48
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Induction Machine Stator Fault Tracking using the Growing Curvilinear Component Analysis, file e384c432-c67c-d4b2-e053-9f05fe0a1d67
|
46
|
|
Metodo di processo digitale di un segnale audio e relativo sistema per uso in un impianto produttivo con macchinari, file e384c432-fc04-d4b2-e053-9f05fe0a1d67
|
41
|
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Audio signal digital processing method and system thereof, file e384c433-1261-d4b2-e053-9f05fe0a1d67
|
39
|
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Anytime ecg monitoring through the use of a low-cost, user-friendly, wearable device, file e384c433-e1da-d4b2-e053-9f05fe0a1d67
|
38
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The Growing Curvilinear Component Analysis (GCCA) neural network, file e384c433-2292-d4b2-e053-9f05fe0a1d67
|
26
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Topological Gradient-based Competitive Learning, file e384c434-3dae-d4b2-e053-9f05fe0a1d67
|
17
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Double Channel Neural Non Invasive Blood Pressure Prediction, file e384c432-b695-d4b2-e053-9f05fe0a1d67
|
15
|
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Growing Curvilinear Component Analysis (GCCA) for Stator Fault Detection in Induction Machines, file e384c431-89be-d4b2-e053-9f05fe0a1d67
|
11
|
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A Neural Based Comparative Analysis for Feature Extraction from ECG Signals, file e384c431-ad5b-d4b2-e053-9f05fe0a1d67
|
11
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The GH-EXIN neural network for hierarchical clustering, file e384c431-462f-d4b2-e053-9f05fe0a1d67
|
8
|
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1-D Convolutional Neural Network for ECG Arrhythmia Classification, file e384c432-892d-d4b2-e053-9f05fe0a1d67
|
8
|
|
External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients, file e384c434-9166-d4b2-e053-9f05fe0a1d67
|
8
|
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Development and Validation of an Algorithm for the Digitization of ECG Paper Images, file 8543952e-f7b3-4b04-94a4-7eb73d07f188
|
7
|
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Shallow Neural Network for Biometrics from the ECG-WATCH, file e384c432-e3fa-d4b2-e053-9f05fe0a1d67
|
7
|
|
A survey on data integration for multi-omics sample clustering, file e384c434-c5af-d4b2-e053-9f05fe0a1d67
|
7
|
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Neural feature extraction for the analysis of Parkinsonian patient handwriting, file e384c432-336b-d4b2-e053-9f05fe0a1d67
|
6
|
|
Tracking Evolution of Stator-based Fault in Induction Machines using the Growing Curvilinear Component Analysis Neural Network, file 7abaf219-854c-4ecc-b5d6-09d1cb893efb
|
4
|
|
Tracking Evolution of Stator-based Fault in Induction Machines using the Growing Curvilinear Component Analysis Neural Network, file 69d0a328-02ef-4821-9bc6-1777a031ec4b
|
3
|
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Non-Invasive Arterial Blood Pressure Estimation from Electrocardiogram and Photoplethysmography Signals Using a Conv1D-BiLSTM Neural Network, file 6bc2d575-2647-4d48-a4c0-1b7e63ea6d97
|
3
|
|
Comparative deep learning studies for indirect tunnel monitoring with and without Fourier pre-processing, file 92deeb54-519a-4af8-a6c5-ae4bfcd39ec1
|
3
|
|
Comparison of Genetic and Reinforcement Learning Algorithms for Energy Cogeneration Optimization, file a1a100dc-b34e-4ba8-81e6-10552da0506e
|
3
|
|
Neural feature extraction for the analysis of Parkinsonian patient handwriting, file e384c431-1b61-d4b2-e053-9f05fe0a1d67
|
3
|
|
Ubiquitous fridge with natural language interaction, file e384c431-4368-d4b2-e053-9f05fe0a1d67
|
3
|
|
The GH-EXIN neural network for hierarchical clustering, file e384c431-5a3d-d4b2-e053-9f05fe0a1d67
|
3
|
|
Growing Curvilinear Component Analysis (GCCA) for Dimensionality Reduction of Nonstationary Data, file e384c432-6c3b-d4b2-e053-9f05fe0a1d67
|
3
|
|
A new unsupervised neural approach to stationary and non-stationary data, file e384c432-748a-d4b2-e053-9f05fe0a1d67
|
3
|
|
Unsupervised Multi-Omic Data Fusion: the Neural Graph Learning Network, file e384c432-76a4-d4b2-e053-9f05fe0a1d67
|
3
|
|
Double Channel Neural Non Invasive Blood Pressure Prediction, file e384c432-e3fd-d4b2-e053-9f05fe0a1d67
|
3
|
|
Artificial Intelligence ECG Analysis in Patients with Short QT Syndrome to Predict Life-Threatening Arrhythmic Events, file 90a9d839-882b-478c-ba2b-d8835392e4cf
|
2
|
|
Neural feature extraction for the analysis of Parkinsonian patient handwriting, file e384c432-37e5-d4b2-e053-9f05fe0a1d67
|
2
|
|
Topological Gradient-based Competitive Learning, file e384c434-3f34-d4b2-e053-9f05fe0a1d67
|
2
|
|
Dual Deep Clustering, file 152ef1f8-efed-4ea5-adc5-5556df916987
|
1
|
|
Learning-Based Approach to Predict Fatal Events in Brugada Syndrome, file 2bcacf1e-f74b-4b1d-a455-2867a613fa01
|
1
|
|
Learning-Based Approach to Predict Fatal Events in Brugada Syndrome, file 35d60e0c-5596-45ed-9775-12291587695d
|
1
|
|
Gradient-Based Competitive Learning: Theory, file 4f76a798-1795-440e-95ec-9605784d74c6
|
1
|
|
Dual Deep Clustering, file 674edb52-4b23-4633-9476-98b81d3972c4
|
1
|
|
Comparison of Genetic and Reinforcement Learning Algorithms for Energy Cogeneration Optimization, file 94efe8c4-7e17-4dd7-8eed-dd4ffb578aef
|
1
|
|
1-D Convolutional Neural Network for ECG Arrhythmia Classification, file e384c432-5987-d4b2-e053-9f05fe0a1d67
|
1
|
|
A Neural Based Comparative Analysis for Feature Extraction from ECG Signals, file e384c432-5b03-d4b2-e053-9f05fe0a1d67
|
1
|
|
Growing Curvilinear Component Analysis (GCCA) for Stator Fault Detection in Induction Machines, file e384c432-5b0c-d4b2-e053-9f05fe0a1d67
|
1
|
|
Shallow Neural Network for Biometrics from the ECG-WATCH, file e384c432-9a58-d4b2-e053-9f05fe0a1d67
|
1
|
|
Neural Recurrent Approches to Noninvasive Blood Pressure Estimation, file e384c432-a05a-d4b2-e053-9f05fe0a1d67
|
1
|
|
Unsupervised Multi-Omic Data Fusion: the Neural Graph Learning Network, file e384c432-c82c-d4b2-e053-9f05fe0a1d67
|
1
|
|
Shallow Neural Network for Biometrics from the ECG-WATCH, file e384c432-cacb-d4b2-e053-9f05fe0a1d67
|
1
|
|
Towards Uncovering Feature Extraction from Temporal Signals in Deep CNN: The ECG Case Study, file e384c432-e3fb-d4b2-e053-9f05fe0a1d67
|
1
|
|
The Growing Curvilinear Component Analysis (GCCA) neural network, file e384c433-2296-d4b2-e053-9f05fe0a1d67
|
1
|
| Totale |
1.686 |
Archivio Istituzionale della Ricerca