Deep learning techniques and optimization strategies in big data analytics:
Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there&...
Gespeichert in:
| Weitere Verfasser: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) :
IGI Global,
2019.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of. |
| Beschreibung: | Description based upon print version of record. |
| Beschreibung: | 27 PDFs (355 pages) Also available in print. |
| Format: | Mode of access: World Wide Web. |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781799811947 |
| Zugangseinschränkungen: | Restricted to subscribers or individual electronic text purchasers. |
Internformat
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| 245 | 0 | 0 | |a Deep learning techniques and optimization strategies in big data analytics |c J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant, editors. |
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| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Chapter 1. Arrhythmia detection based on hybrid features of T-wave in electrocardiogram -- Chapter 2. A review on deep learning applications -- Chapter 3. A survey of nature-inspired algorithms with application to well placement optimization -- Chapter 4. Artificial intelligence approach for predicting TOC from well logs in shale reservoirs: a review -- Chapter 5. Bidirectional GRU-based attention model for kid-specific URL classification -- Chapter 6. Classification of fundus images using neural network approach -- Chapter 7. Convolutional graph neural networks: a review and applications of graph autoencoder in chemoinformatics -- Chapter 8. Deep learning: a recent computing platform for multimedia information retrieval -- Chapter 9. Deep learning techniques and optimization strategies in big data analytics: automated transfer learning of convolutional neural networks using enas algorithm -- Chapter 10. Dimensionality reduction with multi-fold deep denoising autoencoder -- Chapter 11. Fake news detection using deep learning: supervised fake news detection analysis in social media with semantic similarity method -- Chapter 12. Heuristic optimization algorithms for power system scheduling applications: multi-objective generation scheduling with PSO -- Chapter 13. Multiobjective optimization of a biofuel supply chain using random matrix generators -- Chapter 14. Optimized deep learning system for crop health classification strategically using spatial and temporal data -- Chapter 15. Protein secondary structure prediction approaches: a review with focus on deep learning methods -- Chapter 16. Recent trends in the use of graph neural network models for natural language processing -- Chapter 17. Review on particle swarm optimization approach for optimizing wellbore trajectory. | |
| 506 | |a Restricted to subscribers or individual electronic text purchasers. | ||
| 520 | 3 | |a Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of. | |
| 530 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 588 | |a Description based on title screen (IGI Global, viewed 10/31/2019). | ||
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Quantitative research. | |
| 655 | 0 | |a Electronic books. | |
| 700 | 1 | |a Ahamed, B. Bazeer, |e editor. | |
| 700 | 1 | |a Karagoz, Pinar, |e editor. | |
| 700 | 1 | |a Thomas, J. Joshua, |e editor. | |
| 700 | 1 | |a Vasant, Pandian, |e editor. | |
| 710 | 2 | |a IGI Global, |e publisher. | |
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| 912 | |a ZDB-98-IGB | ||
| 049 | |a DE-863 | ||
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| contents | Chapter 1. Arrhythmia detection based on hybrid features of T-wave in electrocardiogram -- Chapter 2. A review on deep learning applications -- Chapter 3. A survey of nature-inspired algorithms with application to well placement optimization -- Chapter 4. Artificial intelligence approach for predicting TOC from well logs in shale reservoirs: a review -- Chapter 5. Bidirectional GRU-based attention model for kid-specific URL classification -- Chapter 6. Classification of fundus images using neural network approach -- Chapter 7. Convolutional graph neural networks: a review and applications of graph autoencoder in chemoinformatics -- Chapter 8. Deep learning: a recent computing platform for multimedia information retrieval -- Chapter 9. Deep learning techniques and optimization strategies in big data analytics: automated transfer learning of convolutional neural networks using enas algorithm -- Chapter 10. Dimensionality reduction with multi-fold deep denoising autoencoder -- Chapter 11. Fake news detection using deep learning: supervised fake news detection analysis in social media with semantic similarity method -- Chapter 12. Heuristic optimization algorithms for power system scheduling applications: multi-objective generation scheduling with PSO -- Chapter 13. Multiobjective optimization of a biofuel supply chain using random matrix generators -- Chapter 14. Optimized deep learning system for crop health classification strategically using spatial and temporal data -- Chapter 15. Protein secondary structure prediction approaches: a review with focus on deep learning methods -- Chapter 16. Recent trends in the use of graph neural network models for natural language processing -- Chapter 17. Review on particle swarm optimization approach for optimizing wellbore trajectory. |
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| discipline | Informatik |
| format | Electronic eBook |
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| id | ZDB-98-IGB-00231554 |
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| isbn | 9781799811947 |
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| spelling | Deep learning techniques and optimization strategies in big data analytics J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant, editors. Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) : IGI Global, 2019. 27 PDFs (355 pages) text rdacontent electronic isbdmedia online resource rdacarrier Description based upon print version of record. Includes bibliographical references and index. Chapter 1. Arrhythmia detection based on hybrid features of T-wave in electrocardiogram -- Chapter 2. A review on deep learning applications -- Chapter 3. A survey of nature-inspired algorithms with application to well placement optimization -- Chapter 4. Artificial intelligence approach for predicting TOC from well logs in shale reservoirs: a review -- Chapter 5. Bidirectional GRU-based attention model for kid-specific URL classification -- Chapter 6. Classification of fundus images using neural network approach -- Chapter 7. Convolutional graph neural networks: a review and applications of graph autoencoder in chemoinformatics -- Chapter 8. Deep learning: a recent computing platform for multimedia information retrieval -- Chapter 9. Deep learning techniques and optimization strategies in big data analytics: automated transfer learning of convolutional neural networks using enas algorithm -- Chapter 10. Dimensionality reduction with multi-fold deep denoising autoencoder -- Chapter 11. Fake news detection using deep learning: supervised fake news detection analysis in social media with semantic similarity method -- Chapter 12. Heuristic optimization algorithms for power system scheduling applications: multi-objective generation scheduling with PSO -- Chapter 13. Multiobjective optimization of a biofuel supply chain using random matrix generators -- Chapter 14. Optimized deep learning system for crop health classification strategically using spatial and temporal data -- Chapter 15. Protein secondary structure prediction approaches: a review with focus on deep learning methods -- Chapter 16. Recent trends in the use of graph neural network models for natural language processing -- Chapter 17. Review on particle swarm optimization approach for optimizing wellbore trajectory. Restricted to subscribers or individual electronic text purchasers. Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of. Also available in print. Mode of access: World Wide Web. Description based on title screen (IGI Global, viewed 10/31/2019). Big data. Quantitative research. Electronic books. Ahamed, B. Bazeer, editor. Karagoz, Pinar, editor. Thomas, J. Joshua, editor. Vasant, Pandian, editor. IGI Global, publisher. Print version: 1799811921 9781799811923 FWS01 ZDB-98-IGB FWS_PDA_IGB http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-1192-3 Volltext |
| spellingShingle | Deep learning techniques and optimization strategies in big data analytics Chapter 1. Arrhythmia detection based on hybrid features of T-wave in electrocardiogram -- Chapter 2. A review on deep learning applications -- Chapter 3. A survey of nature-inspired algorithms with application to well placement optimization -- Chapter 4. Artificial intelligence approach for predicting TOC from well logs in shale reservoirs: a review -- Chapter 5. Bidirectional GRU-based attention model for kid-specific URL classification -- Chapter 6. Classification of fundus images using neural network approach -- Chapter 7. Convolutional graph neural networks: a review and applications of graph autoencoder in chemoinformatics -- Chapter 8. Deep learning: a recent computing platform for multimedia information retrieval -- Chapter 9. Deep learning techniques and optimization strategies in big data analytics: automated transfer learning of convolutional neural networks using enas algorithm -- Chapter 10. Dimensionality reduction with multi-fold deep denoising autoencoder -- Chapter 11. Fake news detection using deep learning: supervised fake news detection analysis in social media with semantic similarity method -- Chapter 12. Heuristic optimization algorithms for power system scheduling applications: multi-objective generation scheduling with PSO -- Chapter 13. Multiobjective optimization of a biofuel supply chain using random matrix generators -- Chapter 14. Optimized deep learning system for crop health classification strategically using spatial and temporal data -- Chapter 15. Protein secondary structure prediction approaches: a review with focus on deep learning methods -- Chapter 16. Recent trends in the use of graph neural network models for natural language processing -- Chapter 17. Review on particle swarm optimization approach for optimizing wellbore trajectory. Big data. Quantitative research. |
| title | Deep learning techniques and optimization strategies in big data analytics |
| title_auth | Deep learning techniques and optimization strategies in big data analytics |
| title_exact_search | Deep learning techniques and optimization strategies in big data analytics |
| title_full | Deep learning techniques and optimization strategies in big data analytics J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant, editors. |
| title_fullStr | Deep learning techniques and optimization strategies in big data analytics J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant, editors. |
| title_full_unstemmed | Deep learning techniques and optimization strategies in big data analytics J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant, editors. |
| title_short | Deep learning techniques and optimization strategies in big data analytics |
| title_sort | deep learning techniques and optimization strategies in big data analytics |
| topic | Big data. Quantitative research. |
| topic_facet | Big data. Quantitative research. Electronic books. |
| url | http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-1192-3 |
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