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Cognitive big data intelligence with a metaheuristic approach /
Cognitive Big Data Intelligence with a Metaheuristic Approach presents an exact and compact organization of content relating to the latest metaheuristics methodologies based on new challenging big data application domains and cognitive computing. The combined model of cognitive big data intelligence...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam :
Academic Press,
2021.
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| Colección: | Cognitive data science in sustainable computing.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Cognitive big data intelligence with a metaheuristic approach / |c edited by Sushruta Mishra, Hrudaya Kumar Tripathy, Pradeep Kumar Mallick, Arun Kumar Sangaiah, Gyoo-Soo Chae. |
| 264 | 1 | |a Amsterdam : |b Academic Press, |c 2021. | |
| 300 | |a 1 online resource (356 pages :) |b illustrations (black and white, and colour). | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a unmediated |b n |2 rdamedia | ||
| 337 | |a volume |b nc |2 rdacarrier | ||
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| 490 | 1 | |a Cognitive data science in sustainable computing | |
| 500 | |a A. Foundations and Architectural Models of Cognitive Big Data and Meta heuristics 1. Cognitive Computing fundamentals like perception, memory, reasoning, emotion, and problem solving 2. Cognitive Computing techniques using artificial intelligence, pattern and speech recognition, and natural language processing 3. Cognitive approaches within data mining and machine learning techniques 4. Big Data Infrastructure for Cognition and Distributed Data Centers for Cognition 5. Meta heuristics in classification, clustering and frequent pattern mining problems 6. Nature-inspired computing and Optimization algorithms 7. Meta heuristics and swarm intelligence approach 8. Use of Computational intelligence and Intelligent computing approaches in engineering domains 9. Big Data, Clouds and Internet of Things (IoT) 10. Dimensionality reduction models with Meta heuristics 11. Neuro-evolutionary and fuzzy models in big data and cognitive analytics 12. Innovative methods for cognitive business big data analytics 13. Cognitive techniques for mining unstructured, spatial-temporal, streaming and multimedia data 14. Data-driven large scale optimization architectures 15. Ensemble learning with Meta heuristics optimization B. Application Domains and use of Cognitive Big data with Meta heuristics 16. Applications in Logistics, Transportation and Supply Chain Management 17. Cognitive Sensor-Networks applications 18. Algorithm development for big data analysis in E-health and Telemedicine 19. Biomedical Image Processing and Big Data Applications 20. Data Applications of Cognitive Communication 21. Intelligent distributed applications in e-commerce 22. Applications in Economics and Finance 23. Applications in Aeronautics 24. Applications in financial analysis 25. Applications in Cyber security and Intelligence 26. Applications in Traffic Optimization 27. Applications in routing of energy efficient communication networks 28. Other Miscellaneous applications. | ||
| 520 | |a Cognitive Big Data Intelligence with a Metaheuristic Approach presents an exact and compact organization of content relating to the latest metaheuristics methodologies based on new challenging big data application domains and cognitive computing. The combined model of cognitive big data intelligence with metaheuristics methods can be used to analyze emerging patterns, spot business opportunities, and take care of critical process-centric issues in real-time. Various real-time case studies and implemented works are discussed in this book for better understanding and additional clarity. This book presents an essential platform for the use of cognitive technology in the field of Data Science. It covers metaheuristic methodologies that can be successful in a wide variety of problem settings in big data frameworks. -- |c Provided by publisher. | ||
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Metaheuristics. | |
| 650 | 6 | |a Donn�ees volumineuses. |0 (CaQQLa)000284673 | |
| 650 | 6 | |a M�etaheuristiques. |0 (CaQQLa)201-0313163 | |
| 650 | 7 | |a Big data |2 fast |0 (OCoLC)fst01892965 | |
| 650 | 7 | |a Metaheuristics |2 fast |0 (OCoLC)fst02000551 | |
| 700 | 1 | |a Mishra, Sushruta, |e editor. | |
| 700 | 1 | |a Tripathy, Hrudaya Kumar, |e editor. | |
| 700 | 1 | |a Mallick, Pradeep Kumar, |d 1984- |e editor. | |
| 700 | 1 | |a Sangaiah, Arun Kumar, |d 1981- |e editor. | |
| 700 | 1 | |a Chae, Gyoo-Soo, |d 1968- |e editor. | |
| 776 | 0 | 8 | |i ebook version : |z 9780323851183 |
| 776 | 0 | 8 | |c Original |z 0323851177 |z 9780323851176 |w (OCoLC)1246353654 |
| 830 | 0 | |a Cognitive data science in sustainable computing. | |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780323851176 |z Texto completo |