Data Science and Big Data in Biology, Physical Science and Engineering
Big Data analysis is one of the most contemporary areas of development and research in the present day. Tremendous amounts of data are generated every single day from digital technologies and modern information systems, such as cloud computing and Internet of Things (IoT) devices. Analysis of these...
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| Formato: | Online |
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| Idioma: | inglés |
| Publicado: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Subjects: | |
| Acceso en liña: | ONIX_20240514_9783725800360_154 |
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| _version_ | 1869518842968932352 |
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| collection | Directory of Open Access Books |
| description | Big Data analysis is one of the most contemporary areas of development and research in the present day. Tremendous amounts of data are generated every single day from digital technologies and modern information systems, such as cloud computing and Internet of Things (IoT) devices. Analysis of these enormous amounts of data has become a crucial need and requires a lot of effort in order to extract valuable knowledge for decision-making, which in turn will help both academia and industry.Big Data and Data Science have appeared due to the significant need for generating, storing, organizing, and processing immense amounts of data. Data scientists strive to use Artificial Intelligence (AI) and Machine Learning (ML) approaches and models to allow computers to detect and identify what the data represents and be able to detect patterns more quickly, efficiently, and reliably than humans.The goal behind this Special Issue is to explore and discuss various principles, tools, and models in the context of Data Science, as well as diverse and varied concepts and techniques in Big Data in Biology, Chemistry, Biomedical Engineering, Physics, Mathematics, and other areas that work with Big Data. |
| format | Online |
| id | doab-20.500.12854ir-137553 |
| 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-1375532024-05-14T13:29:07Z Data Science and Big Data in Biology, Physical Science and Engineering Mahmoud, Mohammed SuperLearner ensemble machine learning cross-validation generalized low rank model bioarchaeology sex prediction central Italy big data biodiversity data curation data generation cyber infrastructure data access science communication rough set theory genetic algorithm discretization classification data pre-processing business intelligence self-service tools systemic quality model software selection data model data warehouse enterprise system IT governance IT performance monitoring warehouse management logistics dynamic storage location assignment reinforcement learning deep learning artificial intelligence neural network deep neural network decision tree nonlinear data classification back propagation gradient descent machine learning transfer learning deep transfer learning progressive learning self-directed learning self-directed design pedagogy plan-oriented web evaluation criteria design thinking COVID-19 self-awareness system cyber-physical systems CNN Industry 5.0 transformer models web-based attacks program management data analytics agile development churn prediction imbalanced data combined data sampling techniques hyperparameter optimization n/a thema EDItEUR::U Computing and Information Technology::UY Computer science Big Data analysis is one of the most contemporary areas of development and research in the present day. Tremendous amounts of data are generated every single day from digital technologies and modern information systems, such as cloud computing and Internet of Things (IoT) devices. Analysis of these enormous amounts of data has become a crucial need and requires a lot of effort in order to extract valuable knowledge for decision-making, which in turn will help both academia and industry.Big Data and Data Science have appeared due to the significant need for generating, storing, organizing, and processing immense amounts of data. Data scientists strive to use Artificial Intelligence (AI) and Machine Learning (ML) approaches and models to allow computers to detect and identify what the data represents and be able to detect patterns more quickly, efficiently, and reliably than humans.The goal behind this Special Issue is to explore and discuss various principles, tools, and models in the context of Data Science, as well as diverse and varied concepts and techniques in Big Data in Biology, Chemistry, Biomedical Engineering, Physics, Mathematics, and other areas that work with Big Data. 2024-05-14T13:29:02Z 2024-05-14T13:29:02Z 2024 book ONIX_20240514_9783725800360_154 9783725800360 9783725800353 https://directory.doabooks.org/handle/20.500.12854/137553 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/8732 https://mdpi.com/books/pdfview/book/8732 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0035-3 10.3390/books978-3-7258-0035-3 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725800360 9783725800353 238 open access |
| spellingShingle | SuperLearner ensemble machine learning cross-validation generalized low rank model bioarchaeology sex prediction central Italy big data biodiversity data curation data generation cyber infrastructure data access science communication rough set theory genetic algorithm discretization classification data pre-processing business intelligence self-service tools systemic quality model software selection data model data warehouse enterprise system IT governance IT performance monitoring warehouse management logistics dynamic storage location assignment reinforcement learning deep learning artificial intelligence neural network deep neural network decision tree nonlinear data classification back propagation gradient descent machine learning transfer learning deep transfer learning progressive learning self-directed learning self-directed design pedagogy plan-oriented web evaluation criteria design thinking COVID-19 self-awareness system cyber-physical systems CNN Industry 5.0 transformer models web-based attacks program management data analytics agile development churn prediction imbalanced data combined data sampling techniques hyperparameter optimization n/a thema EDItEUR::U Computing and Information Technology::UY Computer science Data Science and Big Data in Biology, Physical Science and Engineering |
| title | Data Science and Big Data in Biology, Physical Science and Engineering |
| title_full | Data Science and Big Data in Biology, Physical Science and Engineering |
| title_fullStr | Data Science and Big Data in Biology, Physical Science and Engineering |
| title_full_unstemmed | Data Science and Big Data in Biology, Physical Science and Engineering |
| title_short | Data Science and Big Data in Biology, Physical Science and Engineering |
| title_sort | data science and big data in biology physical science and engineering |
| topic | SuperLearner ensemble machine learning cross-validation generalized low rank model bioarchaeology sex prediction central Italy big data biodiversity data curation data generation cyber infrastructure data access science communication rough set theory genetic algorithm discretization classification data pre-processing business intelligence self-service tools systemic quality model software selection data model data warehouse enterprise system IT governance IT performance monitoring warehouse management logistics dynamic storage location assignment reinforcement learning deep learning artificial intelligence neural network deep neural network decision tree nonlinear data classification back propagation gradient descent machine learning transfer learning deep transfer learning progressive learning self-directed learning self-directed design pedagogy plan-oriented web evaluation criteria design thinking COVID-19 self-awareness system cyber-physical systems CNN Industry 5.0 transformer models web-based attacks program management data analytics agile development churn prediction imbalanced data combined data sampling techniques hyperparameter optimization n/a thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | SuperLearner ensemble machine learning cross-validation generalized low rank model bioarchaeology sex prediction central Italy big data biodiversity data curation data generation cyber infrastructure data access science communication rough set theory genetic algorithm discretization classification data pre-processing business intelligence self-service tools systemic quality model software selection data model data warehouse enterprise system IT governance IT performance monitoring warehouse management logistics dynamic storage location assignment reinforcement learning deep learning artificial intelligence neural network deep neural network decision tree nonlinear data classification back propagation gradient descent machine learning transfer learning deep transfer learning progressive learning self-directed learning self-directed design pedagogy plan-oriented web evaluation criteria design thinking COVID-19 self-awareness system cyber-physical systems CNN Industry 5.0 transformer models web-based attacks program management data analytics agile development churn prediction imbalanced data combined data sampling techniques hyperparameter optimization n/a thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | ONIX_20240514_9783725800360_154 |