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Record Nr. |
UNISA996490358303316 |
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Titolo |
World of business with data and analytics / / edited by Neha Sharma, Mandar Bhatavdekar |
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Pubbl/distr/stampa |
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Singapore : , : Springer, , [2022] |
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©2022 |
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ISBN |
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Descrizione fisica |
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1 online resource (211 pages) |
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Collana |
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Studies in Autonomic, Data-Driven and Industrial Computing |
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Disciplina |
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Soggetti |
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Business - Data processing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Intro -- Preface -- Acknowledgements -- Contents -- About the Editors -- 1 Dynamic Demand Planning for Distorted Historical Data Due to Pandemic -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Results -- 5 Conclusion -- References -- 2 Cognitive Models to Predict Pipeline Leaks and Ruptures -- 1 Introduction -- 2 Literature Review -- 3 Material and Methodology -- 3.1 Defining the Solution Using Data and Analytics -- 4 Results -- 5 Conclusion -- References -- 3 Network Optimization of the Electricity Grid to Manage Distributed Energy Resources Using Data and Analytics -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Defining a Network Optimization Solution to Build an Agile Grid -- 3.2 Defining the Problem -- 3.3 Defining a Solution for the Problem -- 4 Results -- 5 Conclusion -- References -- 4 Enhancing Market Agility Through Accurate Price Indicators Using Contextualized Data Analytics -- 1 Introduction -- 2 Literature Review -- 3 Data-Flow in Utility Value Chain -- 4 Handaling Market data Volatility and Coherency -- 5 Leveraging Data Analytics in Improving Accuracy of Price-Prediction Models -- 6 Data-Reliant Congestion Management -- 7 Unlocking Techno Commercial Benefits to Utility -- 8 Conclusion -- References -- 5 Infrastructure for Automated Surface Damage Classification and Detection in Production Industries Using ResUNet-based Deep Learning Architecture -- 1 Introduction -- 2 Literature Review -- 3 Dataset Description -- 4 Methodology -- 4.1 Two-Phase Learning |
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