Search Results - empirical algorithm

Energy Psychology : Explorations at the Interface of Energy, Cognition, Behavior, and Health. by Gallo, Fred P.

Table of Contents: “…Manual Muscle Testing and KinesiologyApplied Kinesiology Offshoots; Empirical Research on Manual Muscle Testing; Muscle Testing Proficiency; Integrity and Muscle Testing; Therapy and Diagnosis; Self-Testing; Therapeutic Algorithms vs. …”
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Screenplay and narrative theory : the screenplectics model of complex narrative systems by Varotsis, George

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Biological systematics : principles and applications by Brower, Andrew V. Z. (Andrew Van Zandt), 1962-, Schuh, Randall T.

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Numerical models for submerged breakwaters : coastal hydrodynamics and morphodynamics by Ahmadian, Amir Sharif

Table of Contents: “…3 Literature Review and BackgroundReferences; 4 Theories and Methodologies; 4.1 Introduction; 4.2 Traditional Models for Water Waves; 4.3 New Approaches; 4.3.1 Meshless Methods; 4.3.2 Artificial Intelligence Methods; MLP Networks; Back-Propagation Algorithm; Levenberg-Marquardt Algorithm; RBF Networks; References; 5 Mathematical Modeling and Algorithm Development; 5.1 Navier-Stokes Equations; 5.2 The Turbulent Model; 5.3 Initial and Boundary Conditions; 5.4 Shallow Waters; 5.5 The Extended Mild-Slope Equation; 5.6 Boussinesq Equations; 5.7 Smoothed Particles Hydrodynamics.…”
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Evidencing the Productive Value of the 4IR in the AEC Sector. by Ejohwomu, Obuks A.

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Systemic Risk from Global Financial Derivatives. by Markose, Sheri M.

Table of Contents: “…Empirical (Small World) Core-Periphery Network Algorithm; 4. …”
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Intensity Modulated Radiation Therapy : A Clinical Overview. by Das, Indra J.

Table of Contents: “…9.3 The optimization objectives -- 9.4 The optimization algorithms -- 9.4.1 The deterministic algorithms -- 9.4.2 The stochastic algorithms -- 9.5 The direct aperture optimization -- 9.6 The biological optimization -- 9.6.1 The radiobiological models for TCP, NTCP, EUD -- 9.7 Benefit and deficiencies in biological optimization -- 9.8 Robust optimization -- References -- Chapter 10 Dose calculation -- 10.1 Required accuracy in dose calculation -- 10.2 Dose calculation algorithms and classification -- 10.2.1 The empirical models -- 10.2.2 The semi-empirical, correction-based algorithms…”
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Modelling forest systems

Table of Contents: “…Colour Plates; Principal Contributors; Preface; Part 1 Forest Reality and Modelling Strategies; 1 Suggestions for Choosing an Appropriate Level for Modelling Forest Stands; 2 Mapping Lodgepole Pine Site Index in Alberta; 3 Growth Modelling of Eucalyptus regnans for Carbon Accounting at the Landscape Scale; 4 Spatial Distribution Modelling of Forest Attributes Coupling Remotely Sensed Imagery and GIS Techniques; 5 Algorithmic and Interactive Approaches to Stand Growth Modelling; 6 Linking Process-based and Empirical Forest Models in Eucalyptus Plantations in Brazil.…”
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Intelligent materials, applied mechanics and design science : selected, peer reviewed papers from the 2011 international conference on intelligent materials, applied mechanics and...

Table of Contents: “…Intelligent Materials, Applied Mechanics and Design Science; Preface and Committee; Table of Contents; Chapter 1: Intelligent Materials, Energy Science and Dynamic System; Gear Fault Diagnoise Based on Ensemble Empirical Mode Decomposition and Instantaneous Energy Density Spectrum; Numerical Analysis of Nonlinear Behaviors of a Flexible Rotor Dynamic System with Turbulent Journal Bearings Support; Dynamic Path Planning Algorithm Based on Chaos Genetic Vehicle Navigation; The Hybrid Genetic Algorithm of Single-Machine Materials Manufacturing Process with Periodic Maintenance.…”
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Machine Learning for Financial Engineering. by Gyorfi, Laszlo

Table of Contents: “…Experiments on Heuristic Algorithms3.4. Growth-Optimal Portfolio Selection Algorithms; 3.5. …”
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Missing Data Methods : Cross-Sectional Methods and Applications. by Drukker, David M.

Table of Contents: “…SimulationsEmpirical application to portfolio allocation; Conclusion; Notes; Acknowledgment; References; Efficient estimators of Bx and Bw; Variances of Bx and Bw; The case of observed Y; Nonlinear difference-in-difference treatment effect estimation: A distributional analysis; Introduction; Methodology; Monte Carlo simulation; Empirical application; Conclusion; Notes; Acknowledgment; References; Bayesian analysis of multivariate sample selection models using gaussian copulas; Introduction; Copulas; Model; Estimation; Applications; Concluding remarks; Acknowledgments; References.…”
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Stochastic filtering with applications in finance by Bhar, Ramaprasad

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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Computer-Aided Learning and Analysis for COVID-19 Disease. by Dhiman, Gaurav

Table of Contents: “…Cover -- Special issue (part 1) on computer-aided learning and analysis for COVID-19 disease -- COVID-19: risk prediction through nature inspired algorithm -- E-biomedical: a positive prospect to monitor human healthcare system using blockchain technology -- Pattern analysis: predicting COVID-19 pandemic in India using AutoML -- Predicting future diseases based on existing health status using link prediction -- Detection of COVID-19 cases through X-ray images using hybrid deep neural network -- Time series analysis of COVID-19 cases…”
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Multivariate statistics : proceedings of the 6th Tartu Conference, Tartu, Estonia, 19-22 August 1999

Table of Contents: “…s F-statistic: A simulation studyZero-boundary Voronoi partitions -- On expected values of fourth-degree matrix products of a multinormal matrix variate -- A multivariate Buckley-James estimator -- A new algorithm of the linear discriminant function using integer programming -- Robustification of “approximating approachâ€? …”
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Machine Learning for Asset Management New Developments and Financial Applications

Table of Contents: “…Empirical investigation -- 2.2.1. The data -- 2.2.2. …”
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Linkage Analysis and Gene Mapping. by WANG, Jiankang

Table of Contents: “…Theoretical Frequencies of 4 Homozygotes in Permanent Populations -- Genotypic Frequencies of Two Co-Dominant Loci in Temporary Populations -- Genotypic Frequencies of One Co-Dominant Locus and One Dominant Locus in Temporary Populations -- Genotypic Frequencies of One Co-Dominant Locus and One Recessive Locus in Temporary Populations -- Genotypic Frequencies of Two Dominant Loci in Temporary Populations -- Genotypic Frequencies of One Dominant Locus and One Recessive Locus in Temporary Populations -- Genotypic Frequencies of Two Recessive Loci in Temporary Populations -- Estimation of Two-Point Recombination Frequency -- Maximum Likelihood Estimation of Recombination Frequency in DH Populations -- General Procedure on the Maximum Likelihood Estimation of Recombination Frequency -- Estimation of Recombination Frequency Between One Co-Dominant and One Dominant Marker in F2 Population -- Initial Values in Newton Algorithm -- EM Algorithm in Estimating Recombination Frequency in F2 Populations -- Effects on the Estimation of Recombination Frequency from Segregation Distortion -- Exercises -- Three-Point Analysis and Linkage Map Construction -- Three-Point Analysis and Mapping Function -- Genetic Interference and Coefficient of Interference -- Mapping Function -- Construction of Genetic Linkage Maps -- Marker Grouping Algorithm -- Marker Ordering Algorithm -- Use of the k-Optimal Algorithm in Linkage Map Construction -- Rippling of the Ordered Markers -- Integration of Multiple Maps -- Comparison of the Recombination Frequency Estimation in Different Populations -- LOD Score in Testing the Linkage Relationship in Different Populations -- Accuracy of the Estimated Recombination Frequency -- Least Population Size to Declare the Significant Linkage Relationship and Close Linkage -- Linkage Analysis in Random Mating Populations.…”
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Big data and differential privacy : analysis strategies for railway track engineering by Attoh-Okine, Nii O.

Table of Contents: “…4.3 Taxonomy of Big Data Analytics in Railway Track Engineering4.4 Data Engineering; 4.5 Remarks; References; Chapter 5 Hilbert-Huang Transform, Profile, Signal, and Image Analysis; 5.1 Hilbert-Huang Transform; 5.1.1 Traditional Empirical Mode Decomposition; 5.1.1.1 Side Effect (Boundary Effect); 5.1.1.2 Example; 5.1.1.3 Stopping Criterion; 5.1.2 Ensemble Empirical Mode Decomposition (EEMD); 5.1.2.1 Post-Processing EEMD; 5.1.3 Complex Empirical Mode Decomposition (CEMD); 5.1.4 Spectral Analysis; 5.1.5 Bidimensional Empirical Mode Decomposition (BEMD); 5.1.5.1 Example.…”
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Machine Learning in Chemical Safety and Health : Fundamentals with Applications. by Wang, Qingsheng

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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Quantifying human resources : uses and analyses by Coron, Clotilde

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Intelligent computational systems : a multi-disciplinary perspective

Table of Contents: “…3.2.1. Genetic Algorithm (GA) -- 3.2.2. Particle Swarm Optimization Algorithm (PSO) -- 3.3. …”
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