Search Results - (((((((kant OR wanting) OR span) OR mantis) OR cantor) OR anne) OR carter) OR anting) algorithms.

  1. 221

    Pediatric incontinence : evaluation and clinical management

    Published 2015
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  2. 222

    Listed Volatility and Variance Derivatives : a Python-based Guide. by Hilpisch, Yves

    Published 2016
    Table of Contents: “…Chapter 2 Introduction to Python2.1 Python Basics; 2.1.1 Data Types; 2.1.2 Data Structures; 2.1.3 Control Structures; 2.1.4 Special Python Idioms; 2.2 NumPy; 2.3 matplotlib; 2.4 pandas; 2.4.1 pandas DataFrame class; 2.4.2 Input-Output Operations; 2.4.3 Financial Analytics Examples; 2.5 Conclusions; Chapter 3 Model-Free Replication of Variance; 3.1 Introduction; 3.2 Spanning with Options; 3.3 Log Contracts; 3.4 Static Replication of Realized Variance and Variance Swaps; 3.5 Constant Dollar Gamma Derivatives and Portfolios; 3.6 Practical Replication of Realized Variance.…”
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  3. 223

    Knowledge mining using intelligent agents

    Published 2011
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  4. 224
  5. 225

    Clinical simulation : operations, engineering and management

    Published 2008
    Table of Contents: “…; 3.5 The Systems Approach to Training; 3.6 Defining the Performance Requirement; 3.7 Cost Versus Value Added; 3.8 Operations Cost; 3.9 Standardization: What is it, and who Wants it?; 3.10 Patients as Training Conditions; 3.11 Equipment as Training Conditions; 3.12 Increase in Training System Cost; 3.13 You as the Leader-Manager; 3.14 Conclusion; Endnotes; Topic II What's In It For Me.…”
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  6. 226

    Digitalization of Society and Socio-Political Issues. 1, Digital, Communication, and Culture

    Published 2019
    Table of Contents: “…The Digitalization of Cultural Policies in France 149; Anne BELLON 14.1.…”
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  7. 227

    Other geographies : the influences of Michael Watts

    Published 2017
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  8. 228

    Oracle SOA Suite 11g Performance Cookbook. by Brasier, Matthew

    Published 2013
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  9. 229

    Discovering knowledge in data : an introduction to data mining by Larose, Daniel T.

    Published 2014
    Table of Contents: “…DISCOVERING KNOWLEDGE IN DATA -- Contents -- Preface -- 1 An Introduction to Data Mining -- 1.1 What is Data Mining? -- 1.2 Wanted: Data Miners -- 1.3 The Need for Human Direction of Data Mining -- 1.4 The Cross-Industry Standard Practice for Data Mining -- 1.4.1 Crisp-DM: The Six Phases -- 1.5 Fallacies of Data Mining -- 1.6 What Tasks Can Data Mining Accomplish? …”
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  10. 230

    Building Machine Learning Systems with Python. by Richert, Willi

    Published 2013
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  11. 231

    Machine Learning in Chemical Safety and Health : Fundamentals with Applications. by Wang, Qingsheng

    Published 2022
    Table of Contents: “…Chapter 3 Flammability Characteristics Prediction Using QSPR Modeling -- 3.1 Introduction -- 3.1.1 Flammability Characteristics -- 3.1.2 QSPR Application -- 3.1.2.1 Concept of QSPR -- 3.1.2.2 Trends and Characteristics of QSPR -- 3.2 Flowchart for Flammability Characteristics Prediction -- 3.2.1 Dataset Preparation -- 3.2.2 Structure Input and Molecular Simulation -- 3.2.3 Calculation of Molecular Descriptors -- 3.2.4 Preliminary Screening of Molecular Descriptors -- 3.2.5 Descriptor Selection and Modeling -- 3.2.6 Model Validation -- 3.2.6.1 Model Fitting Ability Evaluation -- 3.2.6.2 Model Stability Analysis -- 3.2.6.3 Model Predictivity Evaluation -- 3.2.7 Model Mechanism Explanation -- 3.2.8 Summary of QSPR Process -- 3.3 QSPR Review for Flammability Characteristics -- 3.3.1 Flammability Limits -- 3.3.1.1 LFLT and LFL -- 3.3.1.2 UFLT and UFL -- 3.3.2 Flash Point -- 3.3.3 Auto-ignition Temperature -- 3.3.4 Heat of Combustion -- 3.3.5 Minimum Ignition Energy -- 3.3.6 Gas-liquid Critical Temperature -- 3.3.7 Other Properties -- 3.4 Limitations -- 3.5 Conclusions and Future Prospects -- References -- Chapter 4 Consequence Prediction Using Quantitative Property-Consequence Relationship Models -- 4.1 Introduction -- 4.2 Conventional Consequence Prediction Methods -- 4.2.1 Empirical Method -- 4.2.2 Computational Fluid Dynamics (CFD) Method -- 4.2.3 Integral Method -- 4.3 Machine Learning and Deep Learning-Based Consequence Prediction Models -- 4.4 Quantitative Property-Consequence Relationship Models -- 4.4.1 Consequence Database -- 4.4.2 Property Descriptors -- 4.4.3 Machine Learning and Deep Learning Algorithms -- 4.5 Challenges and Future Directions -- References -- Chapter 5 Machine Learning in Process Safety and Asset Integrity Management -- 5.1 Opportunities and Threats -- 5.2 State-of-the-Art Reviews -- 5.2.1 Artificial Neural Networks (ANNs).…”
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  12. 232

    Database technology for life sciences and medicine

    Published 2010
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  15. 235

    Computational models of argument : Proceedings of COMMA 2012

    Published 2012
    Table of Contents: “…Simari -- Automated Deployment of Argumentation Protocols / Michael Rovatsos -- On Preferred Extension Enumeration in Abstract Argumentation / Katie Atkinson -- Towards Experimental Algorithms for Abstract Argumentation / Katie Atkinson.…”
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    Electronic Conference Proceeding eBook
  16. 236

    Practical data analysis by Cuesta, Hector

    Published 2013
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  17. 237
  18. 238

    Stochastic filtering with applications in finance by Bhar, Ramaprasad

    Published 2010
    Table of Contents: “…Background to particle filter for non Gaussian problems. 1.8. Particle filter algorithm. 1.9. Unobserved component models. 1.10. …”
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  19. 239

    Design optimization of fluid machinery : applying computational fluid dynamics and numerical optimization by Kim, Kwang-Yong, 1956-, Samad, Abdus, Benini, Ernesto

    Published 2019
    Table of Contents: “…2.2.5.3 Periodic/Cyclic Boundary Conditions2.2.5.4 Symmetry Boundary Conditions; 2.2.6 Moving Reference Frame (MRF); 2.2.7 Verification and Validation; 2.2.8 Commercial CFD Software; 2.2.9 Open Source Codes; 2.2.9.1 OpenFOAM; References; Chapter 3 Optimization Methodology; 3.1 Introduction; 3.1.1 Engineering Optimization Definition; 3.1.2 Design Space; 3.1.3 Design Variables and Objectives; 3.1.4 Optimization Procedure; 3.1.5 Search Algorithm; 3.2 Multi-Objective Optimization (MOO); 3.2.1 Weighted Sum Approach; 3.2.2 Pareto-Optimal Front…”
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  20. 240