Tabla de Contenidos:

“…There are seven new chapters, and each of them focuses on a separate real-world problem to which deep learning is applied…”

“…In some instances, case studies are presented to show you how real-world businesses implement backup procedures, high availability configurations, and disaster recovery plans. …”

Tabla de Contenidos: “…Cover -- Preface -- Contents -- Proof Methods and Induction -- Formal Proofs -- Proofs and the Real World -- Propositional Reasoning Examples -- Direct Proof -- Proof by Contradiction -- Proof by Contradiction -- Structured Proofs and Paragraph Proofs -- Counter Examples -- Proofs -- False Proofs -- Some Actual Proofs -- Inductive Proofs -- More Simple Induction -- Divisibility -- A String Example -- Fibonacci Numbers -- Tiling Problem -- Geometry -- Double Induction -- Strong Induction -- Blocks and Towers -- Fibonacci Examples -- Prime Factorization -- Tournaments -- Cycles -- Locally Fair Rankings -- Locally Fair Rankings and Number of Wins -- Induction, Strong Induction, and Well-ordering -- Induction vs Strong Induction -- Well-ordering -- Structural Induction -- Some Recursive Definitions for Data Types -- Inductive Proofs Based on Recursive Data Type Definitions -- Recursively-defined Functions on Recursively-defined Data Types -- Induction and Recursive Algorithms -- RSA Public Key Cryptosystem -- Fast Exponentiation -- Greatest Common Divisor -- Chapter 1: Symbolic Logic -- 1.1 Introduction to Logic -- 1.2 Boolean Expressions -- 1.3 Construction of Boolean Expressions -- 1.4 Meaning of Boolean Expressions -- 1.4.1 Conjunctions -- 1.4.2 Disjunctions -- 1.4.3 Negations -- 1.5 Construction of Truth Tables -- 1.5.1 Back to Derivations -- 1.5.2 Valuations and Truth Tables -- 1.5.3 How Many Rows -- 1.5.4 Making Truth Tables -- 1.5.5 Truth Tables -- 1.6 Logical Equivalence -- 1.7 DeMorgan's Law -- 1.7.1 Why Use DeMorgan's Law -- 1.7.2 Pushing Negations Inward -- 1.8 Why Use Logical Equivalences -- 1.9 Tautologies and Contradictions -- 1.10 Implication and Biconditionals -- 1.10.1 Mental Imagery: Cause and Effect -- 1.10.2 Relation of Biconditionals to Logical Equivalence -- 1.10.3 Relation of Implication Biconditional…”

Tabla de Contenidos: “…-- 2.3.1 Lessons 1-3: Heed the "warnings" -- 2.3.2 Lessons 4 and 11: Reduce obstacles to action -- 2.3.3 Lessons 5 and 8: Stay in the real world -- 2.3.4 Lessons 6 and 9: Consider wider issues -- 2.3.5 Lesson 7: Evaluate alternative solutions -- 2.3.6 Lesson 10: Maintain regulatory independence -- 2.3.7 Lesson 12: Avoid paralysis by analysis -- 2.4 But have we done enough? …”

“…Using crystal-clear explanations, real-world examples, and around 100 diagrams, Entity Framework Core in Action teaches you how to access and update relational data from .NET applications. …”

“…Selected by Bill Jelen, Microsoft Excel MVP and mastermind behind the leading Excel solutions website MrExcel.com, these books will • Dramatically increase your productivity—saving you 50 hours a year or more • Present proven, creative strategies for solving real-world problems • Show you how to get great results, no matter how much data you have • Help you avoid critical mistakes that even experienced users make…”

“…The guide summarizes all CCNP certification-level Cisco IOS ® Software commands, keywords, command arguments, and associated prompts, providing you with tips and examples of how to apply the commands to real-world scenarios. Configuration examples throughout the book provide you with a better understanding of how these commands are used in simple network designs. …”

“…In addition, Examples and Problems in Mathematical Statistics features: Over 160 practical and interesting real-world examples from a variety of fields including engineering, mathematics, and statistics to help readers become proficient in theoretical problem solving More than 430 unique exercises with select solutions Key statistical inference topics, such as probability theory, statistical distributions, sufficient statistics, information in samples, testing statistical hypotheses, statistical estimation, confidence and tolerance intervals, large sample theory, and Bayesian analysis Recommended for graduate-level courses in probability and statistical inference, Examples and Problems in Mathematical Statistics is also an ideal reference for applied statisticians and researchers…”

Tabla de Contenidos: “…Deep networks in the real world -- 6 Teaching machines to see: Image classification with CNNs -- 6.1 Putting the data under the microscope: Exploratory data analysis -- 6.1.1 The folder/file structure -- 6.1.2 Understanding the classes in the data set -- 6.1.3 Computing simple statistics on the data set -- 6.2 Creating data pipelines using the Keras ImageDataGenerator -- 6.3 Inception net: Implementing a state-of-the-art image classifier -- 6.3.1 Recap on CNNs -- 6.3.2 Inception net v1 -- 6.3.3 Putting everything together -- 6.3.4 Other Inception models -- 6.4 Training the model and evaluating performance -- Summary -- Answers to exercises -- 7 Teaching machines to see better: Improving CNNs and making them confess -- 7.1 Techniques for reducing overfitting -- 7.1.1 Image data augmentation with Keras -- 7.1.2 Dropout: Randomly switching off parts of your network to improve generalizability -- 7.1.3 Early stopping: Halting the training process if the network starts to underperform -- 7.2 Toward minimalism: Minception instead of Inception…”

“…Updated throughout for the third edition, Kinematics and Dynamics of Mechanical Systems: Implementation in MATLAB(tm) and Simscape Multibody(tm) offers step-by-step instruction on the fundamentals of mechanism kinematics, synthesis, statics and dynamics, alongside demonstrating its real-world applications…”

“…Concrete examples and exercises show you FP in the real world and reveal how it can improve your everyday coding practices. …”

Tabla de Contenidos: “…16.5.1 Incorporation of More Physiological Signals -- 16.5.2 Long-Term Monitoring of Fatigue in Real-World Scenarios -- 16.5.3 Integration with Wearable Devices for Continuous Monitoring -- 16.6 Conclusion -- References -- Chapter 17 Machine Learning Approach to Predicting Reliability in Healthcare Using Knowledge Engineering -- 17.1 Introduction -- 17.2 Literature Review -- 17.3 Proposed Methodology -- 17.3.1 Data Analysis (Findings) -- 17.3.2 General Procedures -- 17.3.3 Reviewed Algorithms -- 17.3.4 Benefits of Machine Learning -- 17.3.5 Drawbacks of Machine Learning -- 17.4 Implications -- 17.4.1 Prerequisites and Considerations -- 17.4.2 Implementation Strategy -- 17.4.3 Recommendations -- 17.5 Conclusion -- 17.6 Limitations and Scope of Future Work -- References -- Chapter 18 TPLSTM-Based Deep ANN with Feature Matching Prediction of Lung Cancer -- 18.1 Introduction -- 18.2 Proposed TP-LSTM-Based Neural Network with Feature Matching for Prediction of Lung Cancer -- 18.3 Experimental Work and Comparison Analysis -- 18.4 Conclusion -- References -- Chapter 19 Analysis of Business Intelligence in Healthcare Using Machine Learning -- 19.1 Introduction -- 19.2 Data Gathering -- 19.2.1 Data Integration -- 19.2.2 Data Storage -- 19.2.3 Data Analysis -- 19.2.4 Data Distribution -- 19.2.5 Data-Driven Decisions on Generated Insights -- 19.3 Literature Review -- 19.4 Research Methodology -- 19.5 Implementation -- 19.6 Eligibility Criteria -- 19.7 Results -- 19.8 Conclusion and Future Scope -- References -- Chapter 20 StressDetect: ML for Mental Stress Prediction -- 20.1 Introduction -- 20.2 Related Work -- 20.3 Materials and Methods -- 20.4 Results -- 20.5 Discussion &amp -- Conclusions -- References -- Index…”

“…The book demonstrates various real-world, global engineering problems while touching on evolving design strategies like green technology. …”

Tabla de Contenidos: “…3.3.1 Attacks On Computer-Vision-Based Applications -- 3.3.2 Attacks On Natural Language Processing Applications -- 3.3.3 Attacks On Data Poisoning Applications -- 3.4 Specific Attacks In The Real World -- 3.4.1 Attacks On Natural Language Processing -- 3.4.2 Attacks Using Data Poisoning -- 3.5 Discussions and Open Issues -- 3.6 Conclusions -- References -- 4 Attack Models for Collaborative Deep Learning -- 4.1 Introduction -- 4.2 Background -- 4.2.1 Deep Learning (DL) -- Convolution Neural Network -- 4.2.2 Collaborative Deep Learning (CDL) -- Architecture -- Collaborative Deep Learning Workflow -- 4.2.3 Deep Learning Security and Collaborative Deep Learning Security -- 4.3 Auror: An Automated Defense -- 4.3.1 Problem Setting -- 4.3.2 Threat Model -- Targeted Poisoning Attacks -- 4.3.3 AUROR Defense -- 4.3.4 Evaluation -- 4.4 A New CDL Attack: Gan Attack -- 4.4.1 Generative Adversarial Network (GAN) -- 4.4.2 GAN Attack -- Main Protocol -- 4.4.3 Experiment Setups -- Dataset -- System Architecture -- Hyperparameter Setup -- 4.4.4 Evaluation -- 4.5 Defend Against Gan Attack In IoT -- 4.5.1 Threat Model -- 4.5.2 Defense System -- 4.5.3 Main Protocols -- 4.5.4 Evaluation -- 4.6 Conclusions -- Acknowledgment -- References -- 5 Attacks On Deep Reinforcement Learning Systems: A Tutorial -- 5.1 Introduction -- 5.2 Characterizing Attacks on DRL Systems -- 5.3 Adversarial Attacks -- 5.4 Policy Induction Attacks -- 5.5 Conclusions and Future Directions -- References -- 6 Trust and Security of Deep Reinforcement Learning -- 6.1 Introduction -- 6.2 Deep Reinforcement Learning Overview -- 6.2.1 Markov Decision Process -- 6.2.2 Value-Based Methods -- V-value Function -- Q-value Function -- Advantage Function -- Bellman Equation -- 6.2.3 Policy-Based Methods -- 6.2.4 Actor-Critic Methods -- 6.2.5 Deep Reinforcement Learning -- 6.3 The Most Recent Reviews…”

“…In this regard, this book brings together contemporary and computationally efficient methods for investigating real-world fractional order systems in one volume. Fractional calculus has gained increasing popularity and relevance over the last few decades, due to its well-established applications in various fields of science and engineering. …”

Tabla de Contenidos: “…5.8 Future of AI Adoption in the Financial Industry -- 5.8.1 Emerging Trends and Technologies in AI Adoption in Finance -- 5.8.2 Opportunities for Innovation and Growth in the Industry -- 5.8.3 Challenges and Limitations to Widespread Adoption of AI -- 5.9 Case Studies on AI Adoption in the Financial Industry -- 5.9.1 ICICI Bank -- 5.9.2 HDFC Bank -- 5.9.3 Bank of America -- 5.9.4 Real-World Examples of Successful AI Adoption in Finance -- 5.9.5 Impact of AI on Business Operations and Customer Experience -- 5.9.6 Lessons Learned From AI Implementation in the Financial Industry -- 5.10 Conclusion and Future Directions -- 5.10.1 Summary of the Key Findings and Insights from the Chapter -- 5.10.2 Recommendations for Future Research and Development in AI Adoption in Finance -- 5.10.3 The Role of Policymakers, Regulators, and Industry Leaders in Shaping the Future of AI in Finance -- 5.11 Conclusion -- References -- Chapter 6 Artificial Intelligence Applications in the Indian Financial Ecosystem -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Evolution: From Operations to Risk Management -- 6.4 Banking Services -- 6.5 Payment Systems -- 6.6 Digital Lending -- 6.7 Credit Scoring/Creditworthiness/Direct Lending -- 6.8 Stockbrokers and Wealth Management -- 6.9 Mutual Funds and Asset Management -- 6.10 Insurance Services -- 6.11 Indian Financial Regulators -- 6.12 Challenges in Adoption -- 6.13 Conclusion -- References -- Chapter 7 The Extraction of Features That Characterize Financial Fraud Behavior by Machine Learning Algorithms -- 7.1 Introduction -- 7.2 The Framework of Gibbs Sampling Algorithm -- 7.2.1 The Summary of Gibbs Sampling Algorithm -- 7.2.2 The Framework of Associative Feature Extraction Method -- 7.3 The Framework in Screening Features for Corporate Financial Fraud Behaviors…”

Tabla de Contenidos: “…7.5.3 Construction Challenges and Solutions -- 7.6 Case Studies from Multiple Domains -- 7.6.1 Intelligent Urban Infrastructure and Smart Cities -- 7.6.2 Industrial IoT and Production -- 7.6.3 Telemedicine and Healthcare -- 7.7 Prospects for Future Directions and Research -- 7.7.1 Evolving Trends in 6G Networks -- 7.7.2 Research Gaps and Opportunities for Improvement -- References -- Chapter 8 Blockchain for Security and Privacy in the Smart Healthcare -- 8.1 Brief Overview of Medical Records and Their Confidentiality -- 8.1.1 Medical Records -- 8.1.2 Patient-Centered Network Design -- 8.1.3 Significance of Clinical-Care Data -- 8.1.4 Discretion -- 8.1.5 Overview Regarding the Safety of Medical Records -- 8.2 Basics of Blockchain Technology -- 8.3 Benefits of BC Regarding the Protection of Medical Data -- 8.4 Principles of Using Blockchain for Medical Records -- 8.5 IAM on the Blockchain -- 8.6 Encrypted Medical Information Exchange via Blockchain -- 8.6.1 Protected Information and Better Safety -- 8.6.2 Cooperation and the Efficient Transfer of Data -- 8.7 Insurance User Intelligence and Power in Blockchain-Enabled Services -- 8.8 Governmental and Moral Thoughts -- 8.9 Selected Experiences and Recommended Approaches -- 8.9.1 Exploring Real-World Situations: A Global View -- 8.9.2 Exemplary Methods -- 8.10 Prospects and Hurdles in Advancing Blockchain-Based Health Record Security -- 8.10.1 Forthcoming Steps -- 8.10.2 Difficulties in Implementing BC -- 8.11 Conclusion and Future Prospects -- References -- Chapter 9 Conceptual and Empirical Evidence for the Implementation of Blockchain Technology as a Solution for Healthcare Service Providers in India -- Introduction -- Review of Literature -- Research Objectives -- Scope of the Study -- Research Design -- Sample Size Determination for Unknown Populations -- Tools Applied -- Analysis of data…”

Tabla de Contenidos: “…Save the trash talk for basketball -- Stay in touch with the real world -- On the value of awards shows -- Chapter 5: Concepting for the Hive Mind: Creativity in analog and digital -- Brave New Marketing -- It's less about messaging and more about content -- It's less about ads and more about experiences -- It's less about talking to and more about talking with -- It's less about asking customers to listen and more about inviting them to talk -- It's less about trying to make people want stuff and more about making stuff people want -- The New Creative Person is T-Shaped -- Content is King -- What the New Ideas look like, Besides Cool -- The new ideas might not be "ads as we know them. -- The new ideas come from culture, not commerce -- The new ideas improve people's lives…”

Tabla de Contenidos: “…Front Cover -- Architecting High Performing, Scalable and Available Enterprise Web Applications -- Copyright Page -- Dedication -- Contents -- Preface -- All-around quality -- 360° view: key highlights and differentiators of the book -- Motivation for the book -- Main themes and focus areas -- Organization of chapters -- Target audience -- Declaration -- About the Author -- Acknowledgments -- 1 Architecting Scalable Enterprise Web Applications -- 1.1 Introduction -- 1.2 Scalability layers -- 1.3 Key tenets of enterprise scalability -- 1.3.1 Dimensions of scalability -- 1.4 Challenges for scalability -- 1.5 Scalability patterns and best practices -- 1.5.1 Scalability patterns -- 1.5.1.1 Scalability best practices -- 1.5.2 Deep-dive into scalability -- 1.5.2.1 Scalability through fault tolerance and failover -- 1.5.2.2 Distributed computing -- 1.5.2.2.1 Hyperscale architecture -- 1.5.2.3 Services scalability -- Architecting scalable services infrastructure -- Distributed clustered ESB configuration -- 1.5.2.4 Database scalability -- 1.5.2.5 Storage scalability -- 1.5.2.6 Virtualization -- Impact of virtualization on scalability -- 1.5.2.7 Cloud alternative -- 1.6 Architecting scalable systems -- 1.6.1 Real-world scalability-related metrics of the enterprise application -- 1.6.2 Infrastructure planning -- 1.6.2.1 Infrastructure design and components -- 1.6.2.1.1 Sizing and capacity planning -- 1.6.3 Right routing and workload management -- 1.6.4 Scalable software design -- 1.6.5 Scalability strategy at various layers -- 1.6.6 Scalability monitoring and governance -- 1.7 Scalability testing -- 1.8 Scalability anti-patterns -- 1.9 Case study -- 1.9.1 Problem statement -- 1.9.2 Analysis methodology -- 1.9.2.1 Architecture and code analysis -- 1.9.2.2 Testing methodology -- 1.9.2.3 Scalability problems identified -- 1.9.3 Scalability optimization…”