Artificial Intelligence and Deep Learning in Sensors and Applications
The aim of this reprint is to address increasingly complex human problems by utilizing various sensors to collect data, enabling the formulation of solutions through deep learning and artificial intelligence (AI). This trend creates a high demand for sensors while presenting new challenges in develo...
Sábháilte in:
| Formáid: | Online |
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| Teanga: | Béarla |
| Foilsithe / Cruthaithe: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Ábhair: | |
| Rochtain ar líne: | ONIX_20240906_9783725814510_173 |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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| _version_ | 1869517922224832512 |
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| collection | Directory of Open Access Books |
| description | The aim of this reprint is to address increasingly complex human problems by utilizing various sensors to collect data, enabling the formulation of solutions through deep learning and artificial intelligence (AI). This trend creates a high demand for sensors while presenting new challenges in developing sensor devices and applications across various fields, such as healthcare, manufacturing, agriculture, transportation, construction, and environmental monitoring. For instance, in environmental monitoring, AI-integrated sensors rapidly analyze large datasets to identify real-time patterns and trends, enhancing weather forecasting accuracy by gathering data from multiple sources. In industrial settings, AI-enhanced sensors optimize manufacturing by monitoring equipment health, predicting failures, and proactively scheduling maintenance. This reprint compiles contributions on AI and sensor technology, sharing ideas, designs, applications, and deployment experiences across various fields, including smart manufacturing, construction, autonomous vehicles, traffic monitoring, object recognition, image classification, speech processing, and human behavior analysis. |
| format | Online |
| id | doab-20.500.12854ir-143811 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1438112024-09-06T08:30:53Z Artificial Intelligence and Deep Learning in Sensors and Applications Yuan, Shyan-Ming Hong, Zeng-Wei Cheng, Wai-Khuen traffic flow prediction deep learning convolutional LSTM attention mechanism eXplainable Artificial Intelligence (XAI) XAI recommendation system XAI scoring system medical XAI survey approach anomaly detection anomaly classification industrial control system deep neural network multi-attention block residual block audio super-resolution bone-conduction microphone real-time system convolutional neural network face recognition adversarial attack perturbation adversarial examples adversarial patches Generative Adversarial Network semi-supervised learning semantic segmentation dense prediction one-way consistency scene understanding human activity recognition mmWave radar Kinect V4 sensor point clouds skeleton data multimodal two stream weed detection machine learning systematic literature review multivariate time-series short-time Fourier transform transformer self-attention multi-head attention point cloud down sampling classification network deep reinforcement learning self-supervised learning contrastive learning generalization data augmentation network randomization multimodality feature fusion lung cancer CT scan clinical data n/a thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science The aim of this reprint is to address increasingly complex human problems by utilizing various sensors to collect data, enabling the formulation of solutions through deep learning and artificial intelligence (AI). This trend creates a high demand for sensors while presenting new challenges in developing sensor devices and applications across various fields, such as healthcare, manufacturing, agriculture, transportation, construction, and environmental monitoring. For instance, in environmental monitoring, AI-integrated sensors rapidly analyze large datasets to identify real-time patterns and trends, enhancing weather forecasting accuracy by gathering data from multiple sources. In industrial settings, AI-enhanced sensors optimize manufacturing by monitoring equipment health, predicting failures, and proactively scheduling maintenance. This reprint compiles contributions on AI and sensor technology, sharing ideas, designs, applications, and deployment experiences across various fields, including smart manufacturing, construction, autonomous vehicles, traffic monitoring, object recognition, image classification, speech processing, and human behavior analysis. 2024-09-06T08:30:46Z 2024-09-06T08:30:46Z 2024 book ONIX_20240906_9783725814510_173 9783725814510 9783725814527 https://directory.doabooks.org/handle/20.500.12854/143811 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/9556 https://mdpi.com/books/pdfview/book/9556 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-1452-7 10.3390/books978-3-7258-1452-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725814510 9783725814527 open access |
| spellingShingle | traffic flow prediction deep learning convolutional LSTM attention mechanism eXplainable Artificial Intelligence (XAI) XAI recommendation system XAI scoring system medical XAI survey approach anomaly detection anomaly classification industrial control system deep neural network multi-attention block residual block audio super-resolution bone-conduction microphone real-time system convolutional neural network face recognition adversarial attack perturbation adversarial examples adversarial patches Generative Adversarial Network semi-supervised learning semantic segmentation dense prediction one-way consistency scene understanding human activity recognition mmWave radar Kinect V4 sensor point clouds skeleton data multimodal two stream weed detection machine learning systematic literature review multivariate time-series short-time Fourier transform transformer self-attention multi-head attention point cloud down sampling classification network deep reinforcement learning self-supervised learning contrastive learning generalization data augmentation network randomization multimodality feature fusion lung cancer CT scan clinical data n/a thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science Artificial Intelligence and Deep Learning in Sensors and Applications |
| title | Artificial Intelligence and Deep Learning in Sensors and Applications |
| title_full | Artificial Intelligence and Deep Learning in Sensors and Applications |
| title_fullStr | Artificial Intelligence and Deep Learning in Sensors and Applications |
| title_full_unstemmed | Artificial Intelligence and Deep Learning in Sensors and Applications |
| title_short | Artificial Intelligence and Deep Learning in Sensors and Applications |
| title_sort | artificial intelligence and deep learning in sensors and applications |
| topic | traffic flow prediction deep learning convolutional LSTM attention mechanism eXplainable Artificial Intelligence (XAI) XAI recommendation system XAI scoring system medical XAI survey approach anomaly detection anomaly classification industrial control system deep neural network multi-attention block residual block audio super-resolution bone-conduction microphone real-time system convolutional neural network face recognition adversarial attack perturbation adversarial examples adversarial patches Generative Adversarial Network semi-supervised learning semantic segmentation dense prediction one-way consistency scene understanding human activity recognition mmWave radar Kinect V4 sensor point clouds skeleton data multimodal two stream weed detection machine learning systematic literature review multivariate time-series short-time Fourier transform transformer self-attention multi-head attention point cloud down sampling classification network deep reinforcement learning self-supervised learning contrastive learning generalization data augmentation network randomization multimodality feature fusion lung cancer CT scan clinical data n/a thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | traffic flow prediction deep learning convolutional LSTM attention mechanism eXplainable Artificial Intelligence (XAI) XAI recommendation system XAI scoring system medical XAI survey approach anomaly detection anomaly classification industrial control system deep neural network multi-attention block residual block audio super-resolution bone-conduction microphone real-time system convolutional neural network face recognition adversarial attack perturbation adversarial examples adversarial patches Generative Adversarial Network semi-supervised learning semantic segmentation dense prediction one-way consistency scene understanding human activity recognition mmWave radar Kinect V4 sensor point clouds skeleton data multimodal two stream weed detection machine learning systematic literature review multivariate time-series short-time Fourier transform transformer self-attention multi-head attention point cloud down sampling classification network deep reinforcement learning self-supervised learning contrastive learning generalization data augmentation network randomization multimodality feature fusion lung cancer CT scan clinical data n/a thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | ONIX_20240906_9783725814510_173 |