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Engineering Applications of Neural Networks

20th International Conference, EANN 2019, Xersonisos, Crete, Greece, May 24-26, 2019, Proceedings

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The cover of “Engineering Applications of Neural Networks” - Open Library.

"Engineering Applications of Neural Networks" is published by Springer in May 14, 2019 - Cham and it has 572 pages.


“Engineering Applications of Neural Networks” Metadata:

  • Title: ➤  Engineering Applications of Neural Networks
  • Author:
  • Number of Pages: 572
  • Publisher: Springer
  • Publish Date:
  • Publish Location: Cham

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  • Format: paperback

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"Engineering Applications of Neural Networks" Description:

Open Data:

Intro -- EANN 2019 Preface -- Organization -- Preface PEINT2019 -- Invited Papers -- Learning from Electronic Health Records: From Temporal Abstractions to Time Series Interpretability -- Empirical Approach: How to Get Fast, Interpretable Deep Learning -- "In-memory Computing": Accelerating AI Applications -- Contents -- Invited Paper -- The Power of the ``Pursuit'' Learning Paradigm in the Partitioning of Data -- 1 Introduction -- 2 The Object Migration Automata -- 3 Developing the Pursuit Concept: The Environment -- 3.1 The Design and Results of the POMA -- 4 Enhanced OMA (EOMA) -- 5 Enhancing the EOMA with a Pursuit Paradigm -- 6 Cohesiveness in the EPP: The Transitive PEOMA -- 7 Conclusions -- References -- AI in Energy Management - Industrial Applications -- A Benchmark Framework to Evaluate Energy Disaggregation Solutions -- 1 Introduction -- 2 Related Work -- 3 Purpose -- 4 Taxonomy of Experiments -- 4.1 Category 1: Single Building NILM -- 4.2 Category 2: Single Building Learning and Generalization on Same Dataset -- 4.3 Category 3: Multi Building Learning and Generalization on Same Dataset -- 4.4 Category 4: Generalization to Different Dataset -- 5 Artificial Neural Networks with Stacking -- 5.1 Introduction to Stacking -- 5.2 Implementation -- 6 Results and Discussion -- 7 Conclusions and Future Work -- References -- Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting -- Abstract -- 1 Introduction -- 2 Research Approach -- 3 Data Processing -- 3.1 The Data -- 3.2 Data Pre-processing and Analysis -- 3.3 Clustering -- 3.4 Features Definition -- 4 Experimental Results -- 4.1 Experiment 1-3-Day Ahead Forecast (2/3 Features) -- 4.2 Experiment 2-3-Day Ahead Forecast (3 Features on 3 Remaining Clusters) -- 4.3 Experiment 3-15-Day Ahead Forecast (3 Features) -- 5 Conclusion and Future Work -- References

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