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Artificial Intelligence and Heuristics for Enhanced Food Security / / by Chandrasekar Vuppalapati
| Artificial Intelligence and Heuristics for Enhanced Food Security / / by Chandrasekar Vuppalapati |
| Autore | Vuppalapati Chandrasekar |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (910 pages) |
| Disciplina | 338.10285 |
| Collana | International Series in Operations Research & Management Science |
| Soggetto topico |
Operations research
Food security Artificial intelligence Mathematical optimization Production management Data mining Operations Research and Decision Theory Food Security Artificial Intelligence Optimization Operations Management Data Mining and Knowledge Discovery |
| ISBN |
9783031087431
9783031087424 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part 1: Introduction to Artificial Intelligence and Heuristics -- 1. Introduction -- 2. Heuristics -- 3. Data Engineering Techniques for Machine Learning and Heuristics -- Part 2: Food Security Machine Learning and Heuristics Models -- 4. Food Security -- 5. Food Security – Quality and Safety Drivers -- 6. ML Models - Food Security and Climate Change -- Part 3: Linkage Models -- 7. Food Security and Advanced Imaging Radiometer ML Models -- 8. Composite Models - Food Security and Natural Resources -- 9. Linkage Models: Economic Key Drivers and Agricultural Production -- 10. Heuristics and Agricultural Production Modeling- Part IV: Conclusion -- 11. Future. |
| Record Nr. | UNINA-9910595049503321 |
Vuppalapati Chandrasekar
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Specialty Crops for Climate Change Adaptation : Strategies for Enhanced Food Security by Using Machine Learning and Artificial Intelligence / / by Chandrasekar Vuppalapati
| Specialty Crops for Climate Change Adaptation : Strategies for Enhanced Food Security by Using Machine Learning and Artificial Intelligence / / by Chandrasekar Vuppalapati |
| Autore | Vuppalapati Chandrasekar |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (836 pages) |
| Disciplina | 338.10285 |
| Collana | STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health |
| Soggetto topico |
Machine learning
Food security Agriculture Machine Learning Food Security |
| ISBN |
9783031383991
9783031383984 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction -- Chapter 2. Specialty Crops -- Chapter 3. Time Series Data -- Chapter 4. Climate Models -- Chapter 5. Supervised Models -- Chapter 6. Unsupervised Models -- Chapter 7. Heuristic & Constraint Optimization -- Chapter 8. Deep Learning -- Chapter 9. Dimensionality Reduction -- Chapter 10. Ethical AI -- Chapter 11. Final Thought. |
| Record Nr. | UNINA-9910751387903321 |
|
Vuppalapati Chandrasekar
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||