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Présentation Algorithms For Smart World Technologies de Shailendra Shukla Format Relié
- Livre
Résumé : 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 discussing the importance of advanced algorithms in the use of modern world technologies such as AI, IoT, and Blockchain. Each chapter is written to provide a self-contained treatment of one major topic. Collectively, the chapters have been designed and carefully integrated to be entirely complementary with respect to definitions, terminology, and notation. Chapters are divided into three parts-complexities, paradigms, and recent applications-and at the beginning of each part, a detailed introduction explaining each subject area is provided. The foundational subjects are supported by end-of-chapter exercises and case studies, while the application-focused chapters are supported by projects to give worked experience. Written by two highly qualified authors in academia, sample topics covered in Algorithms for Smart World Technologies include: Written for developers and domain experts who want to explore the opportunities and challenges of designing and developing algorithms and protocols for Smart-world problems, Algorithms for Smart World Technologies is an authoritative resource on the topic that provides both foundational knowledge and guidance on practical applications....
Biographie: Chapter 1 Introduction to complexities Chapter 2 Computational Complexity Chapter 3 Communication Complexity Chapter 4 Data Complexity Chapter 5 Risk measures Chapter 6 Ethics and Algorithmic Boundaries Part II Algorithmic Paradigms for Smart World technologies Chapter 7 Introduction to paradigms of smart algorithms Chapter 8 Optimization algorithms Chapter 9 Decision making algorithms Chapter 10 Prediction Algorithms Chapter 11 Secure Algorithms Part III Smart World Applications Chapter 12 Introduction to Smart World Applications Chapter 13 Smart Education Chapter 14 Smart World Algorithms in Healthcare Chapter 15 Modern Approach Algorithms in Environmentalv Chapter 16 Smart Agriculture Chapter 17 Smart Transportation Chapter 18 IT and Society Chapter 19 Smart Government Chapter 20 Disaster Management Chapter 21 Communication Algorithms Index Bibliography
Part I Complexities of Smart Algorithms
Sommaire: Foreword xv Preface xvii Acknowledgments xix Acronyms xxi Introduction xxv Part I Complexities of Smart Algorithms 1 1 Introduction to Complexities 3 1.1 Complex Systems and Algorithms 4 1.2 Complex Systems 4 1.2.1 Key Features of Complex Systems 4 1.2.2 Examples of Complex Systems 5 1.2.3 The Role of Algorithms in Complex Systems 5 1.2.4 Modeling and Simulation 5 1.2.5 Data Analysis 5 1.2.6 Optimization 5 1.2.7 Machine Learning 5 1.2.8 Challenges and Opportunities in Algorithmic Design 5 1.2.9 Future Directions 5 1.3 Efficiency Metrics for Complex Systems 6 1.3.1 Challenges in Measuring Efficiency 6 1.3.2 Techniques for Measuring Efficiency 7 1.3.3 Defining Efficiency in Complex Systems: A Holistic Approach 7 1.3.4 The Path Forward: Toward a Unified Framework 8 1.4 Applications of Complexity 8 1.4.1 Complexity in Practice 8 1.4.2 Complexity Management 8 1.4.3 Complexity Economics 8 1.4.4 Complexity and Education 9 1.4.5 Complexity and Modeling 9 1.4.6 Complexity and Chaos Theory 9 1.4.7 Complexity and Network Science 9 1.4.8 Complexity and Future Research Directions 10 1.5 Types of Complexities 10 1.6 Exercises 11 2 Computational Complexity 13 2.1 Computability 13 2.2 Computational Models 15 2.3 Complexity Classes 16 2.4 Probabilistic Complexity 17 2.4.1 The BPP Complexity Class 17 2.4.2 Examples of Probabilistic Complexity 17 2.4.3 BPP: Efficient Probabilistic Computation 17 2.4.4 Future Directions in Probabilistic Complexity 18 2.5 Quantum Complexity 18 2.5.1 BQP: Power and Intrigue 18 2.5.2 The P, NP, and BQP 19 2.5.3 BQP: Efficient Quantum Computation 19 2.5.4 Future Directions in Quantum Complexity 19 2.6 Exercises 20 3 Communication Complexity 23 3.1 Deterministic Communication 24 3.2 Deterministic Communication Complexity 24 3.3 Nondeterministic Communication 26 3.4 Nondeterministic Communication Complexity 26 3.5 Randomized Communication Complexity 27 3.5.1 Approximate Rank 28 3.6 Exercises 28 4 Data Complexity 31 4.1 Algorithmic Information Theory 32 4.1.1 Philosophy of Mathematics: Randomness Within Mathematics 32 4.1.2 Philosophy of Probability: Understanding Randomness of Individual Sequences 32 4.2 Occam's Razor and Inductive Inference 33 4.3 Philosophy of Information 33 4.4 Lessons for the Philosophy of Information 34 4.5 Kolmogorov Complexity: Measuring Randomness 35 4.5.1 Defining Descriptions and Complexity 35 4.5.2 Compression and Invariance 35 4.5.3 Randomness and Compressibility 36 4.5.4 Connection to G?del's Theorem 36 4.6 VC Dimension: Measuring Model Complexity 36 4.6.1 VC Dimension of Set Families 36 4.6.2 VC Dimension of Classification Models 36 4.7 Rademacher Complexity 37 4.7.1 Rademacher Complexity of a Set 37 4.7.2 Rademacher Complexity of a Function Class 37 4.7.3 Example 37 4.7.4 Generalization Bound 38 4.7.5 Using Rademacher Complexity 38 4.7.6 Representativeness of a Sample 38 4.8 Exercises 38 5 Risk Measures 41 5.1 Addressing Algorithmic Bias 41 5.2 Risk Measures 42 5.3 Algorithmic Fairness Measures 43 5.4 Risks in Algorithmic Monoculture 44 5.5 Green Efficiency 45 5.5.1 Green Internet Technologies 46 5.5.2 Green RFID Tags 46 5.5.3 Green Wir...
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