| Nome |
# |
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Hyperdimensional computing for efficient distributed classification with randomized neural networks, file e383532d-12e1-15e8-e053-a505fe0a3de9
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145
|
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Prediction in Photovoltaic Power by Neural Networks, file e3835317-a6f4-15e8-e053-a505fe0a3de9
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136
|
|
A review of the enabling methodologies for knowledge discovery from smart grids data, file e3835328-b895-15e8-e053-a505fe0a3de9
|
136
|
|
2-D convolutional deep neural network for the multivariate prediction of photovoltaic time series, file e383532c-1f76-15e8-e053-a505fe0a3de9
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123
|
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On effects of compression with hyperdimensional computing in distributed randomized neural networks, file e383532c-af28-15e8-e053-a505fe0a3de9
|
83
|
|
Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series, file e3835328-62cb-15e8-e053-a505fe0a3de9
|
62
|
|
Perceptron theory can predict the accuracy of neural networks, file f5296dce-7325-4564-9061-9e41ae8b0661
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41
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A Fast Deep Learning Technique for Wi-Fi-Based Human Activity Recognition, file c53c8a0c-fda8-48c8-abe9-594ee4db235e
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40
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A Study on structural health monitoring of a large space antenna via distributed sensors and deep learning, file 054b106d-5787-4b69-8ac6-a94ae027751d
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26
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Challenges and perspectives of smart grid systems in islands. A real case study, file a5405e8f-58ae-4358-971c-0f08178368e9
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26
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Deep learning-based structural health monitoring for damage detection on a large space antenna, file e383532e-17c7-15e8-e053-a505fe0a3de9
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14
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|
Systematic review of energy theft practices and autonomous detection through artificial intelligence methods, file 884028ab-704c-40b8-8b52-39b3db0cff08
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13
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Multi-damage detection in composite space structures via deep learning, file a9e2c2a3-3ce1-40aa-ac8b-f62d9308fba3
|
13
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Modular quantum circuits for secure communication, file f9691f32-26bc-4ae7-bd2c-01332a759846
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13
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Detection of Autism Spectrum Disorder by a Fast Deep Neural Network, file 5bab0354-6b76-4f2a-9094-b60bf2eab8e8
|
12
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RETI NEURALI QUANTISTICHE RANDOM VECTOR FUNCTIONAL-LINK, file e3835323-7043-15e8-e053-a505fe0a3de9
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11
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An adaptive embedding procedure for time series forecasting with deep neural networks, file 1df111f0-077e-42a1-a08c-56fa6c568f55
|
9
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Multidimensional feeding of LSTM networks for multivariate prediction of energy time series, file e3835327-cf52-15e8-e053-a505fe0a3de9
|
9
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Nonexclusive classification of household appliances by fuzzy deep neural networks, file 37602c85-927f-4103-8d86-4e5968e3ae7e
|
8
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|
Multivariate time series analysis for electrical power theft detection in the distribution grid, file e20b8deb-7d3e-427b-8071-ba040258b330
|
8
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Time series prediction with autoencoding LSTM networks, file e383532c-fe4b-15e8-e053-a505fe0a3de9
|
8
|
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Analysis of Logic Schemes for the Optical Implementation of Pointwise Operations in Gated Recurrent Unit Cells, file 642d6997-4e19-4693-b0bf-6a879b3f13b0
|
7
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A price-aware dynamic decision system in energy communities, file cca62d2f-c698-4397-a7e9-2c6b83c9119f
|
7
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Multivariate prediction in photovoltaic power plants by a stacked deep neural network, file e3835326-b517-15e8-e053-a505fe0a3de9
|
7
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Design of an LSTM cell on a quantum hardware, file e383532e-9890-15e8-e053-a505fe0a3de9
|
7
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Hybrid Quantum-Classical Recurrent Neural Networks for Time Series Prediction, file a9106adb-a0b6-4647-b04c-2da7d8bded50
|
6
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Recent advances on distributed unsupervised learning, file e3835315-5d37-15e8-e053-a505fe0a3de9
|
6
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Takagi-Sugeno Fuzzy Systems Applied to Voltage Prediction of Photovoltaic Plants, file e3835317-e9a1-15e8-e053-a505fe0a3de9
|
6
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Multivariate prediction of PM10 concentration by LSTM neural networks, file e3835326-b50d-15e8-e053-a505fe0a3de9
|
6
|
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A combined deep learning approach for time series prediction in energy environments, file e3835327-7c7d-15e8-e053-a505fe0a3de9
|
6
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|
Deep Learning for local damage identification in large space structures via sensor-measured time responses, file e383532a-8491-15e8-e053-a505fe0a3de9
|
6
|
|
A parallel hardware implementation for 2-D hierarchical clustering based on fuzzy logic, file e383532b-3662-15e8-e053-a505fe0a3de9
|
6
|
|
Two-stage dynamic management in energy communities using a decision system based on elastic net regularization, file e383532c-0615-15e8-e053-a505fe0a3de9
|
6
|
|
Distributed LSTM-based cloud resource allocation in network function virtualization architectures, file 9ced7cf7-55b4-4f67-9598-6be859b40e95
|
5
|
|
Embedding of Time Series for the Prediction in Photovoltaic Power Plants, file e383531e-30ba-15e8-e053-a505fe0a3de9
|
5
|
|
A neural network based prediction system of distributed generation for the management of microgrids, file e3835323-b24e-15e8-e053-a505fe0a3de9
|
5
|
|
ADMM consensus for deep LSTM networks, file e3835328-8cce-15e8-e053-a505fe0a3de9
|
5
|
|
Multivariate Prediction of Energy Time Series by Autoencoded LSTM Networks, file e383532e-92f4-15e8-e053-a505fe0a3de9
|
5
|
|
All-optical and logic gate based on semiconductor optical amplifiers for implementing deep recurrent neural networks, file 1ed9e1ac-9dfa-471a-8305-c5f30e80be41
|
4
|
|
Distributed learning of random weights fuzzy neural networks, file e3835315-9c65-15e8-e053-a505fe0a3de9
|
4
|
|
Time series prediction using random weights fuzzy neural networks, file e3835328-60e6-15e8-e053-a505fe0a3de9
|
4
|
|
An energy-aware hardware implementation of 2D hierarchical clustering, file e3835328-b984-15e8-e053-a505fe0a3de9
|
4
|
|
A blockwise embedding for multi-day-ahead prediction of energy time series by randomized deep neural networks, file e383532d-7898-15e8-e053-a505fe0a3de9
|
4
|
|
Bimodal Feature Analysis with Deep Learning for Autism Spectrum Disorder Detection, file 0ca017f3-7278-41a7-91cf-8db3b823a3d7
|
3
|
|
A randomized deep neural network for emotion recognition with landmarks detection, file 25edcdd4-b0b3-4e6b-8ecc-2e792651709b
|
3
|
|
Few-shot Federated Learning in Randomized Neural Networks via Hyperdimensional Computing, file 3a12e35c-a3eb-46c9-9eb8-1bd1dc9eaf30
|
3
|
|
A deep neural network for G-quadruplexes binding proteins classification, file d1938473-edcc-4c59-9ac8-45dc1b0175b4
|
3
|
|
MACHINE LEARNING PER L’ANALISI AMBIENTALE, file e3835315-ffdb-15e8-e053-a505fe0a3de9
|
3
|
|
Takagi-Sugeno Fuzzy Systems Applied to Voltage Prediction of Photovoltaic Plants, file e3835317-7531-15e8-e053-a505fe0a3de9
|
3
|
|
Retrieving chlorophyll-a levels, transparency and tss concentration from multispectral satellite data by using artificial neural networks, file e383531d-3645-15e8-e053-a505fe0a3de9
|
3
|
|
Apprendimento sparso di reti neurofuzzy, file e383531d-7a98-15e8-e053-a505fe0a3de9
|
3
|
|
Water quality prediction based on wavelet neural networks and remote sensing, file e3835323-19d3-15e8-e053-a505fe0a3de9
|
3
|
|
Neural network approaches to electricity price forecasting in day-ahead markets, file e3835323-5dba-15e8-e053-a505fe0a3de9
|
3
|
|
A fuzzy neural network approach to quality assessment of water reservoirs, file e3835326-9a1c-15e8-e053-a505fe0a3de9
|
3
|
|
A decentralized algorithm for distributed ensemble clustering, file e383532d-03d3-15e8-e053-a505fe0a3de9
|
3
|
|
Fast convolutional analysis of task-based fMRI data for ADHD detection, file 6d9785d4-f7f6-4bcd-8abc-4961624cf8e0
|
2
|
|
Neural Graphs: An Effective Solution for the Resource Allocation in NFV Sites interconnected by Elastic Optical Networks, file dba25f6e-e1d7-4640-b496-d78932fe214a
|
2
|
|
APPRENDIMENTO DI RETI NEURALI IN CIRCUITI A PRECISIONE NUMERICA FINITA, file e3835317-869a-15e8-e053-a505fe0a3de9
|
2
|
|
A new learning approach for Takagi-Sugeno fuzzy systems applied to time series prediction, file e3835318-8c6f-15e8-e053-a505fe0a3de9
|
2
|
|
Finite precision implementation of random vector functional-link networks, file e383531d-35d0-15e8-e053-a505fe0a3de9
|
2
|
|
A sparse Bayesian model for random weight fuzzy neural networks, file e3835323-09a1-15e8-e053-a505fe0a3de9
|
2
|
|
2-D convolutional deep neural network for multivariate energy time series prediction, file e3835323-0b55-15e8-e053-a505fe0a3de9
|
2
|
|
A distributed algorithm for the cooperative prediction of power production in PV plants, file e3835323-0c37-15e8-e053-a505fe0a3de9
|
2
|
|
A smart grid in Ponza island: battery energy storage management by echo state neural network, file e3835323-5db7-15e8-e053-a505fe0a3de9
|
2
|
|
A training procedure for quantum random vector functional-link networks, file e3835323-66d0-15e8-e053-a505fe0a3de9
|
2
|
|
Predictive analysis of photovoltaic power generation using deep learning, file e3835323-66d2-15e8-e053-a505fe0a3de9
|
2
|
|
Decentralized prediction of electrical time series in smart grids using long short-term memory neural networks, file e3835326-b506-15e8-e053-a505fe0a3de9
|
2
|
|
Prediction of photovoltaic time series by recurrent neural networks and genetic embedding, file e3835328-60e9-15e8-e053-a505fe0a3de9
|
2
|
|
Deep Neural Networks for Electric Energy Theft and Anomaly Detection in the Distribution Grid, file e383532e-92f5-15e8-e053-a505fe0a3de9
|
2
|
|
All-optical logic gates based on semiconductor optical amplifiers for implementing deep recurrent neural networks, file 2f5ddaec-a418-4214-b8ae-d24fb309661f
|
1
|
|
RETI NEURALI E LOGICA FUZZY PER LA PREDIZIONE DI SERIE ENERGETICHE, file e3835317-5eb4-15e8-e053-a505fe0a3de9
|
1
|
|
Prediction in Photovoltaic Power by Neural Networks, file e3835317-ac83-15e8-e053-a505fe0a3de9
|
1
|
|
Apprendimento on-line di reti neurali su architetture a precisione numerica finita, file e383531d-7c41-15e8-e053-a505fe0a3de9
|
1
|
|
A nonuniform quantizer for hardware implementation of neural networks, file e383531d-7d24-15e8-e053-a505fe0a3de9
|
1
|
|
On-line learning of RVFL neural networks on finite precision hardware, file e3835323-0991-15e8-e053-a505fe0a3de9
|
1
|
|
DEEP LEARNING PER IL CONTROLLO PREDITTIVO NELLA GESTIONE DELLE RISORSE ENERGETICHE DISTRIBUITE, file e3835323-0def-15e8-e053-a505fe0a3de9
|
1
|
| Totale |
1.128 |