Artificial Intelligence Applications to Smart City and Smart Enterprise |
Autore | Impedovo Donato |
Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
Descrizione fisica | 1 electronic resource (374 p.) |
Soggetto topico | Information technology industries |
Soggetto non controllato |
spatio-temporal
residual networks bus traffic flow prediction advance rate shield performance principal component analysis ANFIS-GA tunnel online learning extreme learning machine cyclic dynamics transfer learning knowledge preservation Feature Adaptive optimization Bacterial Foraging algorithm Swarm Intelligence algorithm Isolated Microgrid traffic surveillance video state analysis Grassmann manifold neural network machine-learning quality of life Better Life Index bagging ensemble learning pedestrian attributes surveillance image semantic attributes recognition multi-label learning large-scale database traffic congestion detection minimizing traffic congestion traffic prediction deep learning urban mobility ITS Vehicle-to-Infrastructure neural networks LSTM embeddings trajectories motion behavior smart tourism driver’s behavior detection texting and driving convolutional neural network smart car smart cities smart infotainment driver distraction cameras convolution detection image recognition DSS diabetes prediction homecare assistance information system muti-attribute analysis artificial training dataset machine learning big data data analysis sensors Internet of Things vehicular networks VDTN routing message scheduling traffic flow prediction wavenet TrafficWave RNN GRU SAEs risk assessment neural architecture search recurrent neural network automated driving vehicle decision support system artificial intelligence disaster management Smart city program management integrated model smart city intelligence transportation system computer vision potential pedestrian safety data mining healthcare Apache Spark disease detection symptoms detection Arabic language Saudi dialect high performance computing (HPC) spatial-temporal dependencies traffic periodicity graph convolutional network traffic speed prediction vehicular traffic surveillance video big data analysis autonomous driving life quality pattern recognition |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910557295703321 |
Impedovo Donato | ||
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
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Artificial Intelligence for Multimedia Signal Processing |
Autore | Kim Byung-Gyu |
Pubbl/distr/stampa | Basel, : MDPI Books, 2022 |
Descrizione fisica | 1 electronic resource (212 p.) |
Soggetto topico |
Technology: general issues
History of engineering & technology |
Soggetto non controllato |
human-height estimation
depth video depth 3D conversion artificial intelligence convolutional neural networks deep neural network convolutional neural network environmental sound recognition feature combination multimodal joint representation content curation social networks different recommend tasks content based recommend systems scene/place classification semantic segmentation deep learning weighting matrix speech enhancement generative adversarial network relativistic GAN lightweight neural network single image super-resolution image enhancement image restoration residual dense networks visual sentiment analysis sentiment classification graph convolutional networks generative adversarial networks traffic surveillance image processing image de-raining fluency evaluation speech recognition data augmentation variational autoencoder speech conversion heartbeat classification convolutional neural network (CNN) canonical correlation analysis (CCA) Indian Sign Language (ISL) natural language processing avatar sign movement context-free grammar object detection logical story unit detection (LSU) object re-ID computer vision image processing single image artifacts reduction dense networks residual networks channel attention networks |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910595077003321 |
Kim Byung-Gyu | ||
Basel, : MDPI Books, 2022 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
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Deep Learning Applications with Practical Measured Results in Electronics Industries |
Autore | Kung Hsu-Yang |
Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
Descrizione fisica | 1 electronic resource (272 p.) |
Soggetto non controllato |
faster region-based CNN
visual tracking intelligent tire manufacturing eye-tracking device neural networks A* information measure oral evaluation GSA-BP tire quality assessment humidity sensor rigid body kinematics intelligent surveillance residual networks imaging confocal microscope update mechanism multiple linear regression geometric errors correction data partition Imaging Confocal Microscope image inpainting lateral stage errors dot grid target K-means clustering unsupervised learning recommender system underground mines digital shearography optimization techniques saliency information gated recurrent unit multivariate time series forecasting multivariate temporal convolutional network foreign object data fusion update occasion generative adversarial network CNN compressed sensing background model image compression supervised learning geometric errors UAV nonlinear optimization reinforcement learning convolutional network neuro-fuzzy systems deep learning image restoration neural audio caption hyperspectral image classification neighborhood noise reduction GA MCM uncertainty evaluation binary classification content reconstruction kinematic modelling long short-term memory transfer learning network layer contribution instance segmentation smart grid unmanned aerial vehicle forecasting trajectory planning discrete wavelet transform machine learning computational intelligence tire bubble defects offshore wind multiple constraints human computer interaction Least Squares method |
ISBN | 3-03928-864-4 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910404080403321 |
Kung Hsu-Yang | ||
MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
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