Machine Learning
The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled rea...
保存先:
| フォーマット: | Online |
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| 言語: | 英語 |
| 出版事項: |
IntechOpen
2023
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| 主題: | |
| オンライン･アクセス: | ONIX_20231201_9781789237535_1178 |
| タグ: |
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| _version_ | 1869529754596540416 |
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| collection | Directory of Open Access Books |
| description | The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters enabled the processing of a vast amount of data. There has been a vital need to discover important patterns and infer trends from a large volume of data (so-called Big Data) to empower data-driven decision-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Machine learning algorithms are based on concepts and tools developed in several fields including statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in machine learning have had a broad range of applications in different scientific disciplines. This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses. |
| format | Online |
| id | doab-20.500.12854ir-130069 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1300692024-04-14T10:28:26Z Machine Learning Farhadi, Hamed deep learning, big data, malaria, data mining, cloud computing, fpga thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters enabled the processing of a vast amount of data. There has been a vital need to discover important patterns and infer trends from a large volume of data (so-called Big Data) to empower data-driven decision-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Machine learning algorithms are based on concepts and tools developed in several fields including statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in machine learning have had a broad range of applications in different scientific disciplines. This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses. 2023-12-01T16:43:43Z 2023-12-01T16:43:43Z 2018 book ONIX_20231201_9781789237535_1178 9781789237535 9781789237528 9781838814182 https://directory.doabooks.org/handle/20.500.12854/130069 eng image/jpeg n/a https://www.intechopen.com/books/6346 https://mts.intechopen.com/storage/books/6346/authors_book/authors_book.pdf IntechOpen IntechOpen 10.5772/intechopen.69783 10.5772/intechopen.69783 78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6 9781789237535 9781789237528 9781838814182 IntechOpen 230 open access |
| spellingShingle | deep learning, big data, malaria, data mining, cloud computing, fpga thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning Machine Learning |
| title | Machine Learning |
| title_full | Machine Learning |
| title_fullStr | Machine Learning |
| title_full_unstemmed | Machine Learning |
| title_short | Machine Learning |
| title_sort | machine learning |
| topic | deep learning, big data, malaria, data mining, cloud computing, fpga thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| topic_facet | deep learning, big data, malaria, data mining, cloud computing, fpga thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| url | ONIX_20231201_9781789237535_1178 |