Artificial Neural Networks for IoT-Enabled Smart Applications
In the age of neural networks and the Internet of Things (IoT), the search for new neural network architectures capable of operating on devices with limited computing power and small memory size is becoming an urgent agenda. This reprint focuses on recent developments in the organization of artifici...
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| Médium: | Online |
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| Jazyk: | angličtina |
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MDPI - Multidisciplinary Digital Publishing Institute
2023
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| On-line přístup: | ONIX_20230911_9783036584287_29 |
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| collection | Directory of Open Access Books |
| description | In the age of neural networks and the Internet of Things (IoT), the search for new neural network architectures capable of operating on devices with limited computing power and small memory size is becoming an urgent agenda. This reprint focuses on recent developments in the organization of artificial intelligence (AI) on edge devices for various IoT-enabled smart applications and starts with the illustration of achievements in smart healthcare services. Digitalization of healthcare driven by the IoT and AI leads to the effective use of sensors, enabling various parameters of the human body to be instantly tracked and processed in daily life. The concept of machine learning sensors is applied to the diagnosis of COVID-19 as an IoT application in healthcare and ambient assisted living. Wearable sensors and IoT-enabled technologies also look promising for monitoring motor activity and gait in Parkinson's disease patients. IoT devices with AI can be effectively used in speech recognition and environmental monitoring, for detecting distracting actions in driver activities and for lifesaving applications such as child drowning prevention systems. Smart disaster rescue is an interesting development of a wearable device for search dogs that recognizes the behavior of a dog when a victim is found, using deep learning models. This reprint illustrates advanced cases of using AI technology for IoT-enabled smart applications. |
| format | Online |
| id | doab-20.500.12854ir-113896 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1138962024-03-31T13:08:33Z Artificial Neural Networks for IoT-Enabled Smart Applications Velichko, Andrei Korzun, Dmitry Meigal, Alexander deep learning canine activity recognition bark detection wearable computing search and rescue system stacking ensemble learning distracted driving imaginary speech convolutional neural network electroencephalography signal processing Kara One database COVID-19 biochemical and hematological biomarkers routine blood values feature selection method LogNNet neural network Internet of Medical Things IoT 5G and beyond child drowning prevention network slicing architecture point clouds remote sensing machine learning sensors inertial measurement unit smartphone accelerometry TUG test gait Parkinson’s disease “dry” immersion arrhythmia artificial intelligence (AI) cardiac communication technologies Electrocardiogram (ECG) systematic literature review (SLR) chemical carcinogens machine learning deep learning neural network hybrid neural network convolution neural network fast forward neural network edge computing ANN microprocessor water level prediction decentralized n/a thema EDItEUR::M Medicine and Nursing thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences In the age of neural networks and the Internet of Things (IoT), the search for new neural network architectures capable of operating on devices with limited computing power and small memory size is becoming an urgent agenda. This reprint focuses on recent developments in the organization of artificial intelligence (AI) on edge devices for various IoT-enabled smart applications and starts with the illustration of achievements in smart healthcare services. Digitalization of healthcare driven by the IoT and AI leads to the effective use of sensors, enabling various parameters of the human body to be instantly tracked and processed in daily life. The concept of machine learning sensors is applied to the diagnosis of COVID-19 as an IoT application in healthcare and ambient assisted living. Wearable sensors and IoT-enabled technologies also look promising for monitoring motor activity and gait in Parkinson's disease patients. IoT devices with AI can be effectively used in speech recognition and environmental monitoring, for detecting distracting actions in driver activities and for lifesaving applications such as child drowning prevention systems. Smart disaster rescue is an interesting development of a wearable device for search dogs that recognizes the behavior of a dog when a victim is found, using deep learning models. This reprint illustrates advanced cases of using AI technology for IoT-enabled smart applications. 2023-09-11T11:55:46Z 2023-09-11T11:55:46Z 2023 book ONIX_20230911_9783036584287_29 9783036584287 9783036584294 https://directory.doabooks.org/handle/20.500.12854/113896 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/7737 https://mdpi.com/books/pdfview/book/7737 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-8429-4 10.3390/books978-3-0365-8429-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036584287 9783036584294 268 open access |
| spellingShingle | deep learning canine activity recognition bark detection wearable computing search and rescue system stacking ensemble learning distracted driving imaginary speech convolutional neural network electroencephalography signal processing Kara One database COVID-19 biochemical and hematological biomarkers routine blood values feature selection method LogNNet neural network Internet of Medical Things IoT 5G and beyond child drowning prevention network slicing architecture point clouds remote sensing machine learning sensors inertial measurement unit smartphone accelerometry TUG test gait Parkinson’s disease “dry” immersion arrhythmia artificial intelligence (AI) cardiac communication technologies Electrocardiogram (ECG) systematic literature review (SLR) chemical carcinogens machine learning deep learning neural network hybrid neural network convolution neural network fast forward neural network edge computing ANN microprocessor water level prediction decentralized n/a thema EDItEUR::M Medicine and Nursing thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences Artificial Neural Networks for IoT-Enabled Smart Applications |
| title | Artificial Neural Networks for IoT-Enabled Smart Applications |
| title_full | Artificial Neural Networks for IoT-Enabled Smart Applications |
| title_fullStr | Artificial Neural Networks for IoT-Enabled Smart Applications |
| title_full_unstemmed | Artificial Neural Networks for IoT-Enabled Smart Applications |
| title_short | Artificial Neural Networks for IoT-Enabled Smart Applications |
| title_sort | artificial neural networks for iot enabled smart applications |
| topic | deep learning canine activity recognition bark detection wearable computing search and rescue system stacking ensemble learning distracted driving imaginary speech convolutional neural network electroencephalography signal processing Kara One database COVID-19 biochemical and hematological biomarkers routine blood values feature selection method LogNNet neural network Internet of Medical Things IoT 5G and beyond child drowning prevention network slicing architecture point clouds remote sensing machine learning sensors inertial measurement unit smartphone accelerometry TUG test gait Parkinson’s disease “dry” immersion arrhythmia artificial intelligence (AI) cardiac communication technologies Electrocardiogram (ECG) systematic literature review (SLR) chemical carcinogens machine learning deep learning neural network hybrid neural network convolution neural network fast forward neural network edge computing ANN microprocessor water level prediction decentralized n/a thema EDItEUR::M Medicine and Nursing thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences |
| topic_facet | deep learning canine activity recognition bark detection wearable computing search and rescue system stacking ensemble learning distracted driving imaginary speech convolutional neural network electroencephalography signal processing Kara One database COVID-19 biochemical and hematological biomarkers routine blood values feature selection method LogNNet neural network Internet of Medical Things IoT 5G and beyond child drowning prevention network slicing architecture point clouds remote sensing machine learning sensors inertial measurement unit smartphone accelerometry TUG test gait Parkinson’s disease “dry” immersion arrhythmia artificial intelligence (AI) cardiac communication technologies Electrocardiogram (ECG) systematic literature review (SLR) chemical carcinogens machine learning deep learning neural network hybrid neural network convolution neural network fast forward neural network edge computing ANN microprocessor water level prediction decentralized n/a thema EDItEUR::M Medicine and Nursing thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAN Neurosciences |
| url | ONIX_20230911_9783036584287_29 |