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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 electronic resource (302 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
star image
image denoising reinforcement learning maximum likelihood estimation mixed Poisson–Gaussian likelihood machine learning-based classification non-uniform foundation stochastic analysis vehicle–pavement–foundation interaction forest growing stem volume coniferous plantations variable selection texture feature random forest red-edge band on-shelf availability semi-supervised learning deep learning image classification machine learning explainable artificial intelligence wildfire risk assessment Naïve bayes transmission-line corridors image encryption compressive sensing plaintext related chaotic system convolutional neural network color prior model object detection piston error detection segmented telescope BP artificial neural network modulation transfer function computer vision intelligent vehicles extrinsic camera calibration structure from motion convex optimization temperature estimation BLDC electric machine protection touchscreen capacitive display SNR stylus laser cutting quality monitoring artificial neural network burr formation cut interruption fiber laser semi-supervised fuzzy noisy real-world plankton marine activity recognition wearable sensors imbalanced activities sampling methods path planning Q-learning neural network YOLO algorithm robot arm target reaching obstacle avoidance |
| 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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