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Machine Learning in Sensors and Imaging
| Machine Learning in Sensors and Imaging |
| Autore | Nam Hyoungsik |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (302 p.) |
| Soggetto topico |
History of engineering & technology
Technology: general issues |
| Soggetto non controllato |
activity recognition
artificial neural network BLDC BP artificial neural network burr formation capacitive chaotic system color prior model compressive sensing computer vision coniferous plantations convex optimization convolutional neural network cut interruption deep learning display electric machine protection explainable artificial intelligence extrinsic camera calibration fiber laser forest growing stem volume fuzzy image classification image denoising image encryption imbalanced activities intelligent vehicles laser cutting machine learning machine learning-based classification marine maximum likelihood estimation mixed Poisson-Gaussian likelihood modulation transfer function Naïve bayes neural network noisy non-uniform foundation object detection obstacle avoidance on-shelf availability path planning piston error detection plaintext related plankton Q-learning quality monitoring random forest real-world red-edge band reinforcement learning risk assessment robot arm sampling methods segmented telescope semi-supervised semi-supervised learning SNR star image stochastic analysis structure from motion stylus target reaching temperature estimation texture feature touchscreen transmission-line corridors variable selection vehicle-pavement-foundation interaction wearable sensors wildfire YOLO algorithm |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910566484703321 |
Nam Hyoungsik
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Prevention and Management of Frailty
| Prevention and Management of Frailty |
| Autore | Byeon Haewon |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (284 p.) |
| Soggetto topico | Public health & preventive medicine |
| Soggetto non controllato |
brain stimulation
dementia meta-analysis naming primary progressive aphasia qualitative evaluation cognitive function data mining Parkinson’s disease with mild cognitive impairment random forest neuropsychological test motoric cognitive risk syndrome fall gait speed three-item recall older adults mixing ability color-changing chewing gum frailty cross-sectional study spousal concordance aging aged accidental falls pain mild cognitive impairment depressive symptoms frailty profiles latent class analysis quality of life perceived health frailty syndrome physiotherapy exercise mood BDI STAI SWLS muscle strength community-dwelling older adults physical frailty prevalence risk factors non-robust FRAIL scale Tilburg Frailty Indicator determinants community-based sleep quality middle-aged and older adults SUNFRAIL psychometric properties screening tool social isolation social networks social support social participation Parkinson’s disease dementia instrumental activities of daily living clinical dementia rating convergence rate neuropsychological tests neuropsychiatric symptoms explainable artificial intelligence machine learning stacking ensemble Self-Rating Anxiety Scale multiple risk factors fall assessment sheet elderly patients hospitalization risk management driving cessation meaningful activities psychosomatic functions physical functional performance nursing homes physical fitness gait analysis indicators screening artificial intelligence healthcare frail Baduanjin strength training endurance training Explainable Artificial Intelligence |
| ISBN | 3-0365-5372-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910619470503321 |
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Byeon Haewon
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| MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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