Algorithms for Smart World Technologies : A Comprehensive Guide to Applications in AI, IoT and Automation for Electrical and Computer Engineers.

Format:

Book

Author/Creator:

Saha, Suman.

Language:

English

Physical Description:

1 online resource (352 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2025.

Summary:

Enables readers to learn how to design and implement algorithms for efficient and secure smart technologies Algorithms for Smart World Technologies explains the fundamentals of key algorithms and their application in a variety of use cases, covering the factors, assumptions, and models essential for the design of a real-world algorithm and.

Contents:

Cover

Title Page

Copyright

Contents

Foreword

Preface

Acknowledgments

Acronyms

Introduction

Part I: Complexities of Smart Algorithms

Chapter 1: Introduction to Complexities

1.1 Complex Systems and Algorithms

1.2 Complex Systems

1.2.1 Key Features of Complex Systems

1.2.2 Examples of Complex Systems

1.2.3 The Role of Algorithms in Complex Systems

1.2.4 Modeling and Simulation

1.2.5 Data Analysis

1.2.6 Optimization

1.2.7 Machine Learning

1.2.8 Challenges and Opportunities in Algorithmic Design

1.2.9 Future Directions

1.3 Efficiency Metrics for Complex Systems

1.3.1 Challenges in Measuring Efficiency

1.3.2 Techniques for Measuring Efficiency

1.3.3 Defining Efficiency in Complex Systems: A Holistic Approach

1.3.4 The Path Forward: Toward a Unified Framework

1.4 Applications of Complexity

1.4.1 Complexity in Practice

1.4.2 Complexity Management

1.4.3 Complexity Economics

1.4.4 Complexity and Education

1.4.5 Complexity and Modeling

1.4.6 Complexity and Chaos Theory

1.4.7 Complexity and Network Science

1.4.8 Complexity and Future Research Directions

1.5 Types of Complexities

1.6 Exercises

Chapter 2: Computational Complexity

2.1 Computability

2.2 Computational Models

2.3 Complexity Classes

2.4 Probabilistic Complexity

2.4.1 The BPP Complexity Class

2.4.2 Examples of Probabilistic Complexity

2.4.3 BPP: Efficient Probabilistic Computation

2.4.4 Future Directions in Probabilistic Complexity

2.5 Quantum Complexity

2.5.1 BQP: Power and Intrigue

2.5.2 The P, NP, and BQP

2.5.3 BQP: Efficient Quantum Computation

2.5.4 Future Directions in Quantum Complexity

2.6 Exercises

Chapter 3: Communication Complexity

3.1 Deterministic Communication

3.2 Deterministic Communication Complexity.

3.3 Nondeterministic Communication

3.4 Nondeterministic Communication Complexity

3.5 Randomized Communication Complexity

3.5.1 Approximate Rank

3.6 Exercises

Chapter 4: Data Complexity

4.1 Algorithmic Information Theory

4.1.1 Philosophy of Mathematics: Randomness Within Mathematics

4.1.2 Philosophy of Probability: Understanding Randomness of Individual Sequences

4.2 Occam's Razor and Inductive Inference

4.3 Philosophy of Information

4.4 Lessons for the Philosophy of Information

4.5 Kolmogorov Complexity: Measuring Randomness

4.5.1 Defining Descriptions and Complexity

4.5.2 Compression and Invariance

4.5.3 Randomness and Compressibility

4.5.4 Connection to Gödel's Theorem

4.6 VC Dimension: Measuring Model Complexity

4.6.1 VC Dimension of Set Families

4.6.2 VC Dimension of Classification Models

4.7 Rademacher Complexity

4.7.1 Rademacher Complexity of a Set

4.7.2 Rademacher Complexity of a Function Class

4.7.3 Example

4.7.4 Generalization Bound

4.7.5 Using Rademacher Complexity

4.7.6 Representativeness of a Sample

4.8 Exercises

Chapter 5: Risk Measures

5.1 Addressing Algorithmic Bias

5.2 Risk Measures

5.3 Algorithmic Fairness Measures

5.4 Risks in Algorithmic Monoculture

5.5 Green Efficiency

5.5.1 Green Internet Technologies

5.5.2 Green RFID Tags

5.5.3 Green Wireless Sensor Networks

5.5.4 Green Cloud Computing

5.5.5 Green Data Centers

5.6 Conclusion

5.7 Exercises

Chapter 6: Ethics and Algorithmic Boundaries

6.1 Introduction

6.2 Objectives

6.3 Algorithmic Decision-making

6.3.1 Background

6.3.2 Algorithmic Decision-making in Public Discourse

6.3.3 Ethical Challenges in Algorithmic Decision-making

6.3.4 ML and Autonomous Decision-making

6.4 Algorithmic Morality.

6.4.1 Artificial Life and Emerging Ethical Behavior

6.4.2 Unbiased Learning Machines

6.4.3 Associative Learning and Moral Training

6.4.4 Ethical Risks of Learning Systems

6.5 Ethics as a Service

6.5.1 Service Model Analogies for Ethical Governance

6.5.2 Implementing the Ethics-as-a-Service Model

6.5.3 Case Study: Digital Catapult Pilot

6.5.4 Future Research Directions

6.6 Current Discussions and Future Research Directions

6.7 Conclusion

6.8 Exercises

Part II: Algorithmic Paradigms for Smart World Technologies

Chapter 7: Introduction to Paradigms of Smart Algorithms

7.1 Introduction to Smart Paradigms

7.2 Important Algorithms in Smart Paradigms

7.2.1 ML Algorithms

7.2.2 Optimization Algorithms

7.2.3 IoT and Distributed Algorithms

7.3 Roadmap for Future Advancements

7.3.1 Enhancing Scalability

7.3.2 Data Privacy and Security

7.3.3 Autonomous and Intelligent Decision-making

7.3.4 Green Computing and Energy Efficiency

7.4 Conclusion

Chapter 8: Optimization Algorithms

8.1 Constrained Optimization: Optimization with Limitations

8.2 Convex Optimization: Finding the Global Minimum

8.3 Solving Linear Equations

8.3.1 Steepest Descent: Gradient-based Minimization

8.3.2 Improving Convergence

8.3.3 Preconditioning with Trees

8.4 Linear Programming Duality

8.4.1 Complementary Slackness

8.4.2 Congestion Minimization

8.4.3 Maximum Weight Matching

8.4.4 Games and Strategic Solutions

8.4.5 The Minimax Theorem

8.5 Network Problems

8.5.1 Key Definitions

8.5.2 The Minimum-cost Flow Problem

8.5.3 The Transportation Problem

8.5.4 The Maximum Flow Problem

8.6 Exercises

Chapter 9: Decision-making Algorithms

9.1 Markov Decision Process

9.1.1 Discrete MDPs

9.1.2 Nondiscrete MDPs: General Constructions

9.1.3 Discrete State MDPs.

9.1.4 Classical Borel MDPs

9.1.5 Assumptions for Borel MDPs

9.1.6 Universally Measurable Borel MDPs

9.1.7 Assumptions for Universally Measurable MDPs

9.2 Reinforcement Learning

9.3 Value Iteration

9.4 Q-learning

9.5 TD Learning

9.6 Exercises

Chapter 10: Prediction Algorithms

10.1 Regression

10.1.1 Least Squares and Nearest-neighbor Methods

10.1.2 Prediction Theory

10.1.3 Curse of Dimensionality

10.1.4 Learning as Function Approximation

10.1.5 Key Formulas

10.1.6 Linear Regression and Least Squares

10.1.7 Variable Selection

10.1.8 Best Subset Selection and Forward and Backward Stepwise Selection

10.1.9 Smoothly Clipped Absolute Deviation

10.1.10 Consistency and Oracle Property

10.1.11 Selecting a Group of Variables

10.1.12 Least Squares, Penalized Likelihood, and Bayesian Inference

10.2 Classifications

10.2.1 Issues with Linear Regression Approach

10.2.2 Linear Discriminant Analysis

10.2.3 Reduced-rank LDA

10.2.4 Comparison Between Logistic Regression and LDA

10.2.5 Piecewise Polynomial Functions

10.2.6 Smoothing Splines

10.2.7 Choosing Smoothing Parameters

10.2 8 Hilbert Space

10.2.9 Generalized Additive Models

10.2.10 Fitting GAMs

10.2.11 Illustration: Predicting Email Spam

10.2.12 Tree-based Regression and Classification

10.2.13 Regression Trees

10.2.14 Classification Trees

10.2.15 Challenges in Tree-based Methods

10.2.16 Illustrative Example: Spam Prediction

10.2.17 Hierarchical Mixtures of Experts and Missing Values

10.2.18 One-dimensional Kernel Smoothers

10.2.19 Considerations in Kernel Smoothing

10.2.20 Local Regression and Local Likelihood Method

10.2.21 Selecting the Width of the Kernel

10.2.22 Structured Kernels and Local Likelihood Methods

10.2.23 Kernel Density Estimation.

10.2.24 Application to Classification

10.2.25 Mixture Models

10.3 Model Complexity

10.3.1 Bia-variance Decomposition

10.3.2 Estimate the Errors

10.3.3 Cross-validation

10.3.4 Bootstrap

10.3.5 The EM Algorithm

10.3.6 Two Other Interpretations of EM Algorithm

10.4 Bayesian Algorithms

10.4.1 Variational Bayes

10.4.2 The Key Identity

10.4.3 Variational Inference

10.4.4 Improvements and Variants

10.4.5 Approximate Bayesian Computation

10.4.6 The Discrete Version

10.4.7 The Continuous Version

10.4.8 Issues

10.5 Neural Networks

10.5.1 Fitting Neural Networks

10.5.2 Some Issues with Neural Networks

10.6 Support Vector Machines

10.6.1 Separating Hyperplane

10.6.2 Support Vectors

10.7 Cluster Analysis

10.7.1 Clustering Algorithms: Combinatorial

10.7.2 Clustering Algorithms: k-means

10.7.3 Clustering Algorithms: Hierarchical Clustering

10.7.4 Principal Components, Curves, and Surfaces

10.7.5 Procrustes Transform and Shape Averaging

10.7.6 Factor Model and Independent Component Analysis

10.7.7 Independent Component Analysis

10.7.8 Principal Curve and Multidimensional Scaling

10.8 Graphical Models

10.8.1 False Discovery Rate

10.8.2 Markov Graphs and Gaussian Graphical Models

10.8.3 Undirected Graphs for Discrete Variables

10.8.4 Exponential Random Graphs

10.8.5 Eigen-statistics of Sample Covariance Matrices

10.8.6 Bulk Universality: Marchenko-Pastur Law (or Quartercircle Law)

10.8.7 Edge Universality: Tracy-Widom Law

10.9 Exercises

Chapter 11: Secure Algorithms

11.1 Low-power Cryptography

11.2 Secret-key Cryptography

11.3 Public-key Cryptography

11.3.1 Key Exchange Protocol

11.3.2 Trapdoor Functions

11.3.3 MD5

11.3.4 Secure Sockets Layer

11.3.5 Blockchain

11.3.6 Digital Signature

11.4 Exercises.

Part III: Smart World Applications.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-119-82364-1

1-119-82363-3

1-119-82362-5

9781119823629

OCLC:

1583175528