Info
Hybrid Intelligent Approaches for Smart Energy : Practical Applications
| Hybrid Intelligent Approaches for Smart Energy : Practical Applications |
| Autore | Mohan Senthil Kumar |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (339 pages) |
| Altri autori (Persone) |
AJohn
PadmanabanSanjeevikumar HamidYasir |
| Collana | Next Generation Computing and Communication Engineering Ser. |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-82187-8
1-119-82186-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910595596403321 |
Mohan Senthil Kumar
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| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Spatiotemporal Data Analytics and Modeling : Techniques and Applications / / edited by John A, Satheesh Abimannan, El-Sayed M. El-Alfy, Yue-Shan Chang
| Spatiotemporal Data Analytics and Modeling : Techniques and Applications / / edited by John A, Satheesh Abimannan, El-Sayed M. El-Alfy, Yue-Shan Chang |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (253 pages) |
| Disciplina | 005.7 |
| Collana | Big Data Management |
| Soggetto topico |
Data mining
Quantitative research Big data Engineering - Data processing Data Mining and Knowledge Discovery Data Analysis and Big Data Big Data Data Engineering |
| ISBN |
9789819996513
9819996511 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | PART I. Spatiotemporal Data Management Techniques. – Chapter 1. Introduction to Spatiotemporal Data -- Chapter 2. Recommendation System using Spatial-Temporal Network for Vehicle Demand Prediction -- Chapter 3. Spatial-based Big Data and Large-Scale Network Management -- Chapter 4. Handling Uncertainty in Spatiotemporal Data -- Chapter 5. Multimodal Spatial-Temporal Prediction and Classification using Deep Learning -- Chapter 6. Spatiotemporal Object Detection and Activity Recognition -- PART II. Applications of Spatiotemporal Data Analytics -- Chapter 7. Spatiotemporal Data Analytics for e-waste Management System using Hybrid Deep Belief Networks -- Chapter 8. Spatiotemporal and Intelligent Transportation Forecasting -- Chapter 9. Spatiotemporal Supply Chains and E-Commerce -- Chapter 10. Spatiotemporal Renewable Energy Techniques and Applications.-Chapter 11. Environmental Spatiotemporal Data Analytics -- Chapter 12. Future and ResearchPerspectives of Spatiotemporal Data Analytics and Modelling. . |
| Record Nr. | UNINA-9910847575403321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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