| Description |
1 online resource (467 p.) |
| Contents |
Cover -- Title Page -- Copyright -- Contents -- Notes on Contributors -- Preface -- Acknowledgments -- Chapter 1 Connectionist Learning Models for Application Problems Involving Differential and Integral Equations -- 1.1 Introduction -- 1.1.1 Artificial Neural Network -- 1.1.2 Types of Neural Networks -- 1.1.3 Learning in Neural Network -- 1.1.4 Activation Function -- 1.1.4.1 Sigmoidal Function -- 1.1.5 Advantages of Neural Network -- 1.1.6 Functional Link Artificial Neural Network (FLANN) -- 1.1.7 Differential Equations (DEs) -- 1.1.8 Integral Equation |
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1.1.8.1 Fredholm Integral Equation of First Kind -- 1.1.8.2 Fredholm Integral Equation of Second Kind -- 1.1.8.3 Volterra Integral Equation of First Kind -- 1.1.8.4 Volterra Integral Equation of Second Kind -- 1.1.8.5 Linear Fredholm Integral Equation System of Second Kind -- 1.2 Methodology for Differential Equations -- 1.2.1 FLANN-Based General Formulation of Differential Equations -- 1.2.1.1 Second-Order Initial Value Problem -- 1.2.1.2 Second-Order Boundary Value Problem -- 1.2.2 Proposed Laguerre Neural Network (LgNN) for Differential Equations |
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1.2.2.1 Architecture of Single-Layer LgNN Model -- 1.2.2.2 Training Algorithm of Laguerre Neural Network (LgNN) -- 1.2.2.3 Gradient Computation of LgNN -- 1.3 Methodology for Solving a System of Fredholm Integral Equations of Second Kind -- 1.3.1 Algorithm -- 1.4 Numerical Examples and Discussion -- 1.4.1 Differential Equations and Applications -- 1.4.2 Integral Equations -- 1.5 Conclusion -- References -- Chapter 2 Deep Learning in Population Genetics: Prediction and Explanation of Selection of a Population -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Dataset Description |
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2.3.1 Selection and Its Importance -- 2.4 Objective -- 2.5 Relevant Theory, Results, and Discussions -- 2.5.1 automl -- 2.5.2 Hypertuning the Best Model -- 2.6 Conclusion -- References -- Chapter 3 A Survey of Classification Techniques in Speech Emotion Recognition -- 3.1 Introduction -- 3.2 Emotional Speech Databases -- 3.3 SER Features -- 3.4 Classification Techniques -- 3.4.1 Hidden Markov Model -- 3.4.1.1 Difficulties in Using HMM for SER -- 3.4.2 Gaussian Mixture Model -- 3.4.2.1 Difficulties in Using GMM for SER -- 3.4.3 Support Vector Machine -- 3.4.3.1 Difficulties with SVM |
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3.4.4 Deep Learning -- 3.4.4.1 Drawbacks of Using Deep Learning for SER -- 3.5 Difficulties in SER Studies -- 3.6 Conclusion -- References -- Chapter 4 Mathematical Methods in Deep Learning -- 4.1 Deep Learning Using Neural Networks -- 4.2 Introduction to Neural Networks -- 4.2.1 Artificial Neural Network (ANN) -- 4.2.1.1 Activation Function -- 4.2.1.2 Logistic Sigmoid Activation Function -- 4.2.1.3 tanh or Hyperbolic Tangent Activation Function -- 4.2.1.4 ReLU (Rectified Linear Unit) Activation Function -- 4.3 Other Activation Functions (Variant Forms of ReLU) -- 4.3.1 Smooth ReLU |
| Summary |
Brings mathematics to bear on your real-world, scientific problems Mathematical Methods in Interdisciplinary Sciences provides a practical and usable framework for bringing a mathematical approach to modelling real-life scientific and technological problems. The collection of chapters Dr. Snehashish Chakraverty has provided describe in detail how to bring mathematics, statistics, and computational methods to the fore to solve even the most stubborn problems involving the intersection of multiple fields of study. Graduate students, postgraduate students, researchers, and professors will all benefit significantly from the author's clear approach to applied mathematics. The book covers a wide range of interdisciplinary topics in which mathematics can be brought to bear on challenging problems requiring creative solutions. Subjects include: Structural static and vibration problems Heat conduction and diffusion problems Fluid dynamics problems The book also covers topics as diverse as soft computing and machine intelligence. It concludes with examinations of various fields of application, like infectious diseases, autonomous car and monotone inclusion problems |
| Notes |
Description based upon print version of record |
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4.3.2 Noisy ReLU |
| Subject |
Mathematical analysis.
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Science -- Mathematics
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Mathematical analysis
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Science -- Mathematics
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| Form |
Electronic book
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| ISBN |
9781119585619 |
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1119585619 |
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