Sensors Data Processing Using Machine Learning
The main aim of this reprint was to collect research focusing on data processing using machine learning and deep learning. We invited investigators to contribute both original and review articles, covering the research and development in the areas of data processing using machine learning (ML) and d...
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| Format: | Online |
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| Jezik: | engleski |
| Izdano: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Teme: | |
| Online pristup: | ONIX_20240704_9783725811717_97 |
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| _version_ | 1869517867514331136 |
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| collection | Directory of Open Access Books |
| description | The main aim of this reprint was to collect research focusing on data processing using machine learning and deep learning. We invited investigators to contribute both original and review articles, covering the research and development in the areas of data processing using machine learning (ML) and deep learning (DL). These areas include solutions that are designed for smart devices. In this reprint, leading experts in the field share their insights, research findings, and visions for the future. Together, we embark on a journey to unlock the potential of effective data processing that involves transforming data from a given format into a more usable and desirable form, rendering them more meaningful and informative. Machine learning (ML), deep learning (DL), and artificial intelligence (AI) have proven to be effective methods for this purpose. Through the utilization of machine learning algorithms, mathematical modeling, or various statistical techniques, the entire process can be automated. |
| format | Online |
| id | doab-20.500.12854ir-139301 |
| 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-1393012024-07-04T09:39:29Z Sensors Data Processing Using Machine Learning Hockicko, Peter Hudec, Róbert Kamencay, Patrik web mining detection of degrees of toxicity machine learning lexicon approach text data processing cooperative, connected and automated mobility infrastructure readiness assessment connectivity data positioning data convolutional neural network IoT-based system IoT nodes Raspberry Pi Arduino-based module COVID-19 big data pre-trained model BERT DistilBERT BERTimbau DistilBERTimbau transformer-based machine learning rare earth extraction time delay identification grey correlation analysis time-correlation discrete state transition algorithm wavelet neural network deep learning text classification two-stream networks feature fusion sentiment classification sarcasm detection H.264/AVC H.265/HEVC QoE QoS packet loss rate video quality 3DCNN ConvLSTM human activity recognition IoT smart systems indoor navigation mobile application neural processing unit neural processing cores NPU benchmark processor architectures Apple M1 Apple M2 CoreML neural engine teaching evaluation system student learning behavior data augmentation smart classrooms ductile cast iron pipe defect classification self-supervised CutPaste-Mix remote sensing classification sample selection method classification model sample size n/a thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science The main aim of this reprint was to collect research focusing on data processing using machine learning and deep learning. We invited investigators to contribute both original and review articles, covering the research and development in the areas of data processing using machine learning (ML) and deep learning (DL). These areas include solutions that are designed for smart devices. In this reprint, leading experts in the field share their insights, research findings, and visions for the future. Together, we embark on a journey to unlock the potential of effective data processing that involves transforming data from a given format into a more usable and desirable form, rendering them more meaningful and informative. Machine learning (ML), deep learning (DL), and artificial intelligence (AI) have proven to be effective methods for this purpose. Through the utilization of machine learning algorithms, mathematical modeling, or various statistical techniques, the entire process can be automated. 2024-07-04T09:39:25Z 2024-07-04T09:39:25Z 2024 book ONIX_20240704_9783725811717_97 9783725811717 9783725811724 https://directory.doabooks.org/handle/20.500.12854/139301 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/9297 https://mdpi.com/books/pdfview/book/9297 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-1172-4 10.3390/books978-3-7258-1172-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725811717 9783725811724 248 open access |
| spellingShingle | web mining detection of degrees of toxicity machine learning lexicon approach text data processing cooperative, connected and automated mobility infrastructure readiness assessment connectivity data positioning data convolutional neural network IoT-based system IoT nodes Raspberry Pi Arduino-based module COVID-19 big data pre-trained model BERT DistilBERT BERTimbau DistilBERTimbau transformer-based machine learning rare earth extraction time delay identification grey correlation analysis time-correlation discrete state transition algorithm wavelet neural network deep learning text classification two-stream networks feature fusion sentiment classification sarcasm detection H.264/AVC H.265/HEVC QoE QoS packet loss rate video quality 3DCNN ConvLSTM human activity recognition IoT smart systems indoor navigation mobile application neural processing unit neural processing cores NPU benchmark processor architectures Apple M1 Apple M2 CoreML neural engine teaching evaluation system student learning behavior data augmentation smart classrooms ductile cast iron pipe defect classification self-supervised CutPaste-Mix remote sensing classification sample selection method classification model sample size n/a thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science Sensors Data Processing Using Machine Learning |
| title | Sensors Data Processing Using Machine Learning |
| title_full | Sensors Data Processing Using Machine Learning |
| title_fullStr | Sensors Data Processing Using Machine Learning |
| title_full_unstemmed | Sensors Data Processing Using Machine Learning |
| title_short | Sensors Data Processing Using Machine Learning |
| title_sort | sensors data processing using machine learning |
| topic | web mining detection of degrees of toxicity machine learning lexicon approach text data processing cooperative, connected and automated mobility infrastructure readiness assessment connectivity data positioning data convolutional neural network IoT-based system IoT nodes Raspberry Pi Arduino-based module COVID-19 big data pre-trained model BERT DistilBERT BERTimbau DistilBERTimbau transformer-based machine learning rare earth extraction time delay identification grey correlation analysis time-correlation discrete state transition algorithm wavelet neural network deep learning text classification two-stream networks feature fusion sentiment classification sarcasm detection H.264/AVC H.265/HEVC QoE QoS packet loss rate video quality 3DCNN ConvLSTM human activity recognition IoT smart systems indoor navigation mobile application neural processing unit neural processing cores NPU benchmark processor architectures Apple M1 Apple M2 CoreML neural engine teaching evaluation system student learning behavior data augmentation smart classrooms ductile cast iron pipe defect classification self-supervised CutPaste-Mix remote sensing classification sample selection method classification model sample size n/a thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | web mining detection of degrees of toxicity machine learning lexicon approach text data processing cooperative, connected and automated mobility infrastructure readiness assessment connectivity data positioning data convolutional neural network IoT-based system IoT nodes Raspberry Pi Arduino-based module COVID-19 big data pre-trained model BERT DistilBERT BERTimbau DistilBERTimbau transformer-based machine learning rare earth extraction time delay identification grey correlation analysis time-correlation discrete state transition algorithm wavelet neural network deep learning text classification two-stream networks feature fusion sentiment classification sarcasm detection H.264/AVC H.265/HEVC QoE QoS packet loss rate video quality 3DCNN ConvLSTM human activity recognition IoT smart systems indoor navigation mobile application neural processing unit neural processing cores NPU benchmark processor architectures Apple M1 Apple M2 CoreML neural engine teaching evaluation system student learning behavior data augmentation smart classrooms ductile cast iron pipe defect classification self-supervised CutPaste-Mix remote sensing classification sample selection method classification model sample size n/a thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | ONIX_20240704_9783725811717_97 |