1751996980     

Women in Industrial and Systems Engineering : Key Advances and Perspectives on Emerging Topics

Cham : Springer International Publishing AG, 2019 [©2020]

1 Online-Ressource (609 Seiten)

Englisch

Description based on publisher supplied metadata and other sources.

Erscheint auch als: Women in industrial and systems engineering (Druck-Ausgabe)

978-3-030-11866-2

978-3-030-11865-5 (ISBN der Printausgabe)

Elektronische Ressource: Zugang über Aggregator-Datenbank (Lizenzangabe: Lizenzpflichtig)

305.4362     Ähnliche Literatur

Basisklassifikation: 85.20 (Betriebliche Information und Kommunikation)

*Systemtechnik      Zum Register

Women in engineering ; Electronic books

Intro -- Dedication -- Foreword -- Contents -- Part I Background -- 1 Lillian Moller Gilbreth: An Industrial Engineering Pioneer -- Contents -- 1.1 Introduction -- 1.2 Early Years -- 1.3 The One Best Marriage -- 1.4 On Her Own -- 1.5 Honors and Awards -- References -- Part II Analytics -- 2 Emergence of Statistical Methodologies with the Riseof BIG Data -- Contents -- 2.1 Introduction -- 2.2 A Review of Statistical/Machine Learning Algorithms -- 2.2.1 Random Forests -- Applications -- 2.2.2 Support Vector Machine -- Applications -- 2.2.3 Sparse Modeling -- Applications -- 2.2.4 Dimension Reduction -- Applications -- 2.2.5 Deep Learning -- Applications -- 2.3 Developments in Inferential Analyses of New Algorithms -- 2.3.1 Large-Scale Hypothesis Testing -- 2.3.2 Random Forests -- 2.3.3 Sparse Modeling -- 2.3.4 SVM -- 2.3.5 Dimension Reduction -- 2.3.6 Deep Learning -- 2.4 Discussion -- References -- 3 Specifying and Validating Probabilistic Inputs for Prescriptive Models of Decision Making over Time -- Contents -- 3.1 Introduction -- 3.1.1 Recurring Applications -- 3.2 Stochastic Process Modeling -- 3.3 Discretization -- 3.3.1 Scenario Reduction -- 3.3.2 Comparative Granularity -- 3.4 Solution Methods -- 3.5 Comprehensive Assessment -- 3.5.1 Direct Assessment of Scenario Generation Methods -- 3.5.2 Assessing Solutions by Re-enactment -- 3.6 Conclusions -- References -- 4 Towards a Stable Graph Representation Learning Using Connection Subgraphs -- Contents -- 4.1 Introduction -- 4.1.1 Representation Learning -- 4.1.2 Need and Challenges for Representation Learning -- 4.1.3 Evolution of Network Representation Learning -- 4.1.4 Taxonomy of Network Representation Learning Algorithms -- 4.2 Related Work -- 4.3 Proposed Method: GRCS -- 4.3.1 GRCS-Step 1: Neighborhood Definition -- 4.3.2 GRCS-Step 2: Node Representation Vector Update -- 4.4 Experiments.