Hybrid Data Processing by Combining Machine Learning, Expert, Safety and Security
The goal of this Special Issue is to promote hybrid data processing by combining machine learning with experts’ input, data safety, and security. AI technology and machine learning technology are developing rapidly. Data contain important information that can advance human knowledge and enhance AI c...
Αποθηκεύτηκε σε:
| Μορφή: | Online |
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| Γλώσσα: | Αγγλικά |
| Έκδοση: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Θέματα: | |
| Διαθέσιμο Online: | ONIX_20250812T110751_9783725835454_65 |
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| _version_ | 1869514628801757184 |
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| collection | Directory of Open Access Books |
| description | The goal of this Special Issue is to promote hybrid data processing by combining machine learning with experts’ input, data safety, and security. AI technology and machine learning technology are developing rapidly. Data contain important information that can advance human knowledge and enhance AI capabilities. Meanwhile, requirements for data mining and data processing are expanding. Machine learning and deep learning may achieve excellent results, but in some cases, a balance can be reached by involving experienced experts to save resources and improve outcomes. In mining and analyzing data, the issues of data safety, data security, and data privacy also need to be suitably considered. This Special Issue presents ten rigorously reviewed manuscripts that study how to integrate hybrid data intelligence with experts’ input, expert systems, safety, and security through decentralized reputation systems, blockchain technology, linkable ring signatures, collaborative filtering, contrastive learning, graph neural networks, feature selection, sample imbalance, few-shot learning, contrastive learning, knowledge graphs, transfer learning, dynamic Gaussian Bayesian networks, the Manning formula, surface confluence, federated learning, trusted execution environments, optimal mechanisms, multi-attribute auctions, multi-scale loss, scenario reconfiguration, probabilistic models, topology reconfiguration models, etc., in scenarios of flood prediction, social recommendation, multi-auction, terrorist attack prediction, etc. We believe that these studies are valuable in this field. |
| format | Online |
| id | doab-20.500.12854ir-165309 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1653092025-08-12T09:19:49Z Hybrid Data Processing by Combining Machine Learning, Expert, Safety and Security Cai, Zhiming Du, Wencai Wang, Zhihai Ying, Zuobin machine learning data intelligence data safety data security expert system user information privacy decentralized reputation system blockchain linkable ring signatures collaborative filtering social recommendation contrastive learning graph neural networks terrorist attack prediction feature selection sample imbalance few-shot learning knowledge graph transfer learning dynamic Gaussian Bayesian network Manning formula flood prediction surface confluence federated learning trusted execution environment optimal mechanism multi-attribute auction multi-scale loss scenario reconfiguration probabilistic model topology reconfiguration model thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science The goal of this Special Issue is to promote hybrid data processing by combining machine learning with experts’ input, data safety, and security. AI technology and machine learning technology are developing rapidly. Data contain important information that can advance human knowledge and enhance AI capabilities. Meanwhile, requirements for data mining and data processing are expanding. Machine learning and deep learning may achieve excellent results, but in some cases, a balance can be reached by involving experienced experts to save resources and improve outcomes. In mining and analyzing data, the issues of data safety, data security, and data privacy also need to be suitably considered. This Special Issue presents ten rigorously reviewed manuscripts that study how to integrate hybrid data intelligence with experts’ input, expert systems, safety, and security through decentralized reputation systems, blockchain technology, linkable ring signatures, collaborative filtering, contrastive learning, graph neural networks, feature selection, sample imbalance, few-shot learning, contrastive learning, knowledge graphs, transfer learning, dynamic Gaussian Bayesian networks, the Manning formula, surface confluence, federated learning, trusted execution environments, optimal mechanisms, multi-attribute auctions, multi-scale loss, scenario reconfiguration, probabilistic models, topology reconfiguration models, etc., in scenarios of flood prediction, social recommendation, multi-auction, terrorist attack prediction, etc. We believe that these studies are valuable in this field. 2025-08-12T09:19:47Z 2025-08-12T09:19:47Z 2025 book ONIX_20250812T110751_9783725835454_65 9783725835454 9783725835461 https://directory.doabooks.org/handle/20.500.12854/165309 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/10766 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-3546-1 10.3390/books978-3-7258-3546-1 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725835454 9783725835461 184 open access |
| spellingShingle | machine learning data intelligence data safety data security expert system user information privacy decentralized reputation system blockchain linkable ring signatures collaborative filtering social recommendation contrastive learning graph neural networks terrorist attack prediction feature selection sample imbalance few-shot learning knowledge graph transfer learning dynamic Gaussian Bayesian network Manning formula flood prediction surface confluence federated learning trusted execution environment optimal mechanism multi-attribute auction multi-scale loss scenario reconfiguration probabilistic model topology reconfiguration model thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science Hybrid Data Processing by Combining Machine Learning, Expert, Safety and Security |
| title | Hybrid Data Processing by Combining Machine Learning, Expert, Safety and Security |
| title_full | Hybrid Data Processing by Combining Machine Learning, Expert, Safety and Security |
| title_fullStr | Hybrid Data Processing by Combining Machine Learning, Expert, Safety and Security |
| title_full_unstemmed | Hybrid Data Processing by Combining Machine Learning, Expert, Safety and Security |
| title_short | Hybrid Data Processing by Combining Machine Learning, Expert, Safety and Security |
| title_sort | hybrid data processing by combining machine learning expert safety and security |
| topic | machine learning data intelligence data safety data security expert system user information privacy decentralized reputation system blockchain linkable ring signatures collaborative filtering social recommendation contrastive learning graph neural networks terrorist attack prediction feature selection sample imbalance few-shot learning knowledge graph transfer learning dynamic Gaussian Bayesian network Manning formula flood prediction surface confluence federated learning trusted execution environment optimal mechanism multi-attribute auction multi-scale loss scenario reconfiguration probabilistic model topology reconfiguration model thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science |
| topic_facet | machine learning data intelligence data safety data security expert system user information privacy decentralized reputation system blockchain linkable ring signatures collaborative filtering social recommendation contrastive learning graph neural networks terrorist attack prediction feature selection sample imbalance few-shot learning knowledge graph transfer learning dynamic Gaussian Bayesian network Manning formula flood prediction surface confluence federated learning trusted execution environment optimal mechanism multi-attribute auction multi-scale loss scenario reconfiguration probabilistic model topology reconfiguration model thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science |
| url | ONIX_20250812T110751_9783725835454_65 |