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, PA
IGI Global
[2020]
|
| Schlagworte: | |
| Online-Zugang: | DE-1050 DE-573 DE-898 DE-1049 DE-706 DE-83 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: | 1 Online-Ressource |
| ISBN: | 9781799811947 |
| DOI: | 10.4018/978-1-7998-1192-3 |
Internformat
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| 245 | 1 | 0 | |a Deep learning techniques and optimization strategies in big data analytics |c J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant |
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| 505 | 8 | |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 | |
| 520 | |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 | ||
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| 700 | 1 | |a Thomas, J. Joshua |4 edt | |
| 700 | 1 | |a Karagoz, Pinar |4 edt | |
| 700 | 1 | |a Ahamed, B. Bazeer |4 edt | |
| 700 | 1 | |a Vasant, Pandian |d 1961- |0 (DE-588)1045112240 |4 edt | |
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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 |
| ctrlnum | (OCoLC)1128853916 (DE-599)BVBBV046249704 |
| doi_str_mv | 10.4018/978-1-7998-1192-3 |
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| id | DE-604.BV046249704 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-20T06:38:20Z |
| institution | BVB |
| isbn | 9781799811947 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031627948 |
| oclc_num | 1128853916 |
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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 Hershey, PA IGI Global [2020] 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier 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 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 Big data Quantitative research Thomas, J. Joshua edt Karagoz, Pinar edt Ahamed, B. Bazeer edt Vasant, Pandian 1961- (DE-588)1045112240 edt Erscheint auch als Druck-Ausgabe, hardcover 978-1-7998-1192-3 Erscheint auch als Druck-Ausgabe, softcover 978-1-7998-1193-0 https://doi.org/10.4018/978-1-7998-1192-3 Verlag URL des Erstveröffentlichers 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 |
| title_fullStr | Deep learning techniques and optimization strategies in big data analytics J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant |
| title_full_unstemmed | Deep learning techniques and optimization strategies in big data analytics J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant |
| 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 |
| url | https://doi.org/10.4018/978-1-7998-1192-3 |
| work_keys_str_mv | AT thomasjjoshua deeplearningtechniquesandoptimizationstrategiesinbigdataanalytics AT karagozpinar deeplearningtechniquesandoptimizationstrategiesinbigdataanalytics AT ahamedbbazeer deeplearningtechniquesandoptimizationstrategiesinbigdataanalytics AT vasantpandian deeplearningtechniquesandoptimizationstrategiesinbigdataanalytics |