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Artificial Neural Networks for IoT-Enabled Smart Applications
| Artificial Neural Networks for IoT-Enabled Smart Applications |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2023 |
| Descrizione fisica | 1 online resource (268 p.) |
| Soggetto topico |
Medicine and Nursing
Neurosciences |
| Soggetto non controllato |
"dry" immersion
5G and beyond accelerometry ANN arrhythmia artificial intelligence (AI) bark detection biochemical and hematological biomarkers canine activity recognition cardiac chemical carcinogens child drowning prevention communication technologies convolution neural network convolutional neural network COVID-19 decentralized deep learning deep learning neural network distracted driving edge computing Electrocardiogram (ECG) electroencephalography ensemble learning fast forward neural network feature selection method gait hybrid neural network imaginary speech inertial measurement unit Internet of Medical Things IoT Kara One database LogNNet neural network machine learning machine learning sensors microprocessor n/a network slicing architecture Parkinson's disease point clouds remote sensing routine blood values search and rescue system signal processing smartphone stacking systematic literature review (SLR) TUG test water level prediction wearable computing |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911053081103321 |
| MDPI - Multidisciplinary Digital Publishing Institute, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Flood Forecasting Using Machine Learning Methods
| Flood Forecasting Using Machine Learning Methods |
| Autore | Chang Fi-John |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 online resource (376 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
adaptive neuro-fuzzy inference system (ANFIS)
ANFIS ANN ANN-based models artificial intelligence artificial neural network artificial neural networks backtracking search optimization algorithm (BSA) bat algorithm bees algorithm big data classification and regression trees (CART) convolutional neural networks cultural algorithm data assimilation data forward prediction data scarce basins data science database decision tree deep learning disasters Dongting Lake early flood warning systems empirical wavelet transform ensemble empirical mode decomposition (EEMD) ensemble machine learning ensemble technique extreme event management extreme learning machine (ELM) flash-flood flood events flood forecast flood forecasting flood inundation map flood prediction flood routing flood susceptibility modeling forecasting Google Maps Haraz watershed high-resolution remote-sensing images hybrid & hybrid neural network hydrograph predictions hydroinformatics hydrologic model hydrologic models hydrometeorology improved bat algorithm invasive weed optimization Karahan flood lag analysis Lower Yellow River LSTM LSTM network machine learning machine learning methods method of tracking energy differences (MTED) micro-model monthly streamflow forecasting Muskingum model natural hazards & nonlinear Muskingum model optimization parameters particle filter algorithm particle swarm optimization phase space reconstruction postprocessing precipitation-runoff rainfall-runoff random forest rating curve method real-time recurrent nonlinear autoregressive with exogenous inputs (RNARX) runoff series self-organizing map self-organizing map (SOM) sensitivity soft computing St. Venant equations stopping criteria streamflow predictions superpixel support vector machine survey the Three Gorges Dam the upper Yangtze River time series prediction uncertainty urban water bodies water level forecast Wilson flood wolf pack algorithm |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910346688303321 |
Chang Fi-John
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||