Tabla de Contenidos: “…7.3.2 How to Implement -- Step 1: Data Preparation -- Step 2: SOM Modeling Operator and Parameters -- Step 3: Execution and Interpretation -- Visual Model -- Location Coordinates -- Conclusion -- References -- 8 Model Evaluation -- 8.1 Confusion Matrix -- 8.2 ROC and AUC -- 8.3 Lift Curves -- 8.4 How to Implement -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Evaluation -- Step 4: Execution and Interpretation -- 8.5 Conclusion -- References -- 9 Text Mining -- 9.1 How It Works -- 9.1.1 Term Frequency-Inverse Document Frequency -- 9.1.2 Terminology -- 9.2 How to Implement -- 9.2.1 Implementation 1: Keyword Clustering -- Step 1: Gather Unstructured Data -- Step 2: Data Preparation -- Step 3: Apply Clustering -- 9.2.2 Implementation 2: Predicting the Gender of Blog Authors -- Step 1: Gather Unstructured Data -- Step 2: Data Preparation -- Step 3.1: Identify Key Features -- Step 3.2: Build Models -- Step 4.1: Prepare Test Data for Model Application -- Step 4.2: Applying the Trained Models to Testing Data -- Bias in Machine Learning -- 9.3 Conclusion -- References -- 10 Deep Learning -- 10.1 The AI Winter -- AI Winter: 1970's -- Mid-Winter Thaw of the 1980s -- The Spring and Summer of Artificial Intelligence: 2006-Today -- 10.2 How it works -- 10.2.1 Regression Models As Neural Networks -- 10.2.2 Gradient Descent -- 10.2.3 Need for Backpropagation -- 10.2.4 Classifying More Than 2 Classes: Softmax -- 10.2.5 Convolutional Neural Networks -- 10.2.6 Dense Layer -- 10.2.7 Dropout Layer -- 10.2.8 Recurrent Neural Networks -- 10.2.9 Autoencoders -- 10.2.10 Related AI Models -- 10.3 How to Implement -- Handwritten Image Recognition -- Step 1: Dataset Preparation -- Step 2: Modeling using the Keras Model -- Step 3: Applying the Keras Model -- Step 4: Results -- 10.4 Conclusion -- References -- 11 Recommendation Engines…”

Tabla de Contenidos: “…3.4.2 Google Analytics Testing -- 3.4.3 Structured Data Testing -- 3.4.4 SEO Implementation -- 3.4.5 Google Analytics Implementation -- 3.4.6 Google Data Studio Implementation -- 3.4.7 Tableau Implementation -- 3.5 Result -- 3.6 Conclusion and Future Work -- References -- Chapter 4 The Need for XAI: Challenges and Its Applications -- 4.1 Introduction -- 4.2 Literature Review -- 4.3 The Need for Exploring XAI -- 4.3.1 Explain to Justify -- 4.3.2 Explain to Control -- 4.3.3 Explain to Improve -- 4.3.4 Explain to Discover -- 4.3.5 Challenges in XAI -- 4.4 Scope of Explanation -- 4.4.1 Local Explanation -- 4.4.2 Global Explanations -- 4.5 Differences in Research Methodology -- 4.5.1 Perturbation Based -- 4.5.2 Backpropagation- or Gradient-Based -- 4.5.3 XAI Applications -- 4.6 Conclusion -- References -- Chapter 5 Why Law Firms Need to Embrace Artificial Intelligence to Transform the Indian Legal Industry -- 5.1 Introduction -- 5.1.1 Research Methodology -- 5.1.2 Research Objectives -- 5.2 What Is Artificial Intelligence? …”

Tabla de Contenidos: “…5.3 Nondimensionalization of Basic Differential Equations -- 5.4 Discussion -- 5.5 Dimensionless Numbers -- 5.5.1 Reynolds Number -- 5.5.2 Peclet Number -- 5.5.3 Prandtl Number -- 5.5.4 Nusselt Number -- 5.5.5 Stanton Number -- 5.5.6 Skin Friction Coefficient -- 5.5.7 Graetz Number -- 5.5.8 Eckert Number -- 5.5.9 Grashof Number -- 5.5.10 Rayleigh Number -- 5.5.11 Brinkman Number -- 5.6 Correlations of Experimental Data -- Problems -- References -- Chapter 6 One‐Dimensional Solutions in Convective Heat Transfer -- 6.1 Introduction -- 6.2 Couette Flow -- 6.3 Poiseuille Flow -- 6.4 Rotating Flows -- Problems -- References -- Chapter 7 Laminar External Boundary Layers: Momentum and Heat Transfer -- 7.1 Introduction -- 7.2 Velocity Boundary Layer over a Semi‐Infinite Flat Plate: Similarity Solution -- 7.3 Momentum Transfer over a Wedge (Falkner-Skan Wedge Flow): Similarity Solution -- 7.4 Application of Integral Methods to Momentum Transfer Problems -- 7.4.1 Laminar Forced Flow over a Flat Plate with Uniform Velocity -- 7.4.2 Two‐Dimensional Laminar Flow over a Surface with Pressure Gradient (Variable Free Stream Velocity) -- 7.4.2.1 The Correlation Method of Thwaites -- 7.4.2.2 A Thwaites Type Correlation for Axisymmetric Body -- 7.5 Viscous Incompressible Constant Property Parallel Flow over a Semi‐Infinite Flat Plate: Similarity Solution for Uniform Wall Temperature Boundary Condition -- 7.6 Low‐Prandtl‐Number Viscous Incompressible Constant Property Parallel Flow over a Semi‐Infinite Flat Plate: Similarity Solutions for Uniform Wall Temperature Boundary Condition -- 7.7 High‐Prandtl‐Number Viscous Incompressible Constant Property Parallel Flow over a Semi‐Infinite Flat Plate: Similarity Solutions for Uniform Wall Temperature Boundary Condition…”

Tabla de Contenidos: “…-- 15.3.2 The Architecture and Components of Digital Twins -- 15.3.3 Advantages of Integrating Digital Twins in Manufacturing -- 15.4 Physical Interaction Dynamics in Production Lines -- 15.4.1 The Nature of Physical Interactions -- 15.4.2 The Role of Dynamics in Production Efficiency -- 15.4.3 Challenges in Traditional Simulation Methods -- 15.5 Building the Advanced Real-Time Simulation Framework -- 15.5.1 Core Principles and Design Objectives -- 15.5.2 Data Integration and Processing -- 15.5.2.1 Role of Sensors and IoT -- 15.5.2.2 Algorithmic Foundations for Feedback -- 15.6 Types of Algorithms -- 15.6.1 Pseudocode for Real-Time Adjustments -- 15.6.1.1 Initialization -- 15.6.1.2 Data Collection and Pre-Processing -- 15.6.1.3 Analysis Using Bayesian Inference -- 15.6.1.4 Anomaly Detection and Root Cause Analysis -- 15.6.1.5 Corrective Action Using Gradient Boosting -- 15.6.1.6 Update and Implement -- 15.6.1.7 Continuous Monitoring -- 15.7 Practical Implementations and Case Studies -- 15.7.1 Implementing the Framework: A Step-by-Step Guide -- 15.7.2 Measurable Benefits and Outcomes -- 15.8 Overcoming Challenges and Limitations…”

Tabla de Contenidos: “…Using random forests to further improve prediction -- 10.1.4. Gradient-boosted trees -- 10.1.5. Tree-based model takeaways -- 10.2. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction of AI in Innovative Engineering -- 1.1 Introduction to Innovation Engineering -- 1.2 Flow for Innovation Engineering -- 1.3 Guiding Principles for Innovation Engineering -- 1.4 Introduction to Artificial Intelligence -- 1.4.1 History of Artificial Intelligence -- 1.4.2 Need for Artificial Intelligence -- 1.4.3 Applications of AI -- 1.4.4 Comprised Elements of Intelligence -- 1.4.5 AI Tools -- 1.4.6 AI Future in 2035 -- 1.4.7 Humanoid Robot and AI -- 1.4.8 The Explosive Growth of AI -- 1.5 Types of Learning -- 1.6 Categories of AI -- 1.7 Branches of Artificial Intelligence -- 1.8 Conclusion -- Bibliography -- Chapter 2 An Analytical Review of Deep Learning Algorithms for Stress Prediction in Teaching Professionals -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Dataset and Pre-Processing -- 2.4 Machine Learning Techniques Used -- 2.5 Performance Parameter -- 2.6 Proposed Methodology -- 2.7 Result and Experiment -- 2.8 Comparison of Six Different Approaches For Stress Detection -- 2.9 Conclusions -- 2.10 Future Scope -- References -- Chapter 3 Deep Learning: Tools and Models -- 3.1 Introduction -- 3.1.1 Definition -- 3.1.2 Elements of Neural Networks -- 3.1.3 Tool: Keras -- 3.2 Deep Learning Models -- 3.2.1 Deep Belief Network [DBN] -- 3.2.1.1 Fundamental Architecture of DBN -- 3.2.1.2 Implementing DBN Using MNIST Dataset -- 3.2.2 Recurrent Neural Network [RNN] -- 3.2.2.1 Fundamental Architecture of RNN -- 3.2.2.2 Implementing RNN Using MNIST Dataset -- 3.2.3 Convolutional Neural Network [CNN] -- 3.2.3.1 Fundamental Architecture of CNN -- 3.2.3.2 Implementing CNN Using MNIST Dataset -- 3.2.4 Gradient Adversarial Network [GAN] -- 3.2.4.1 Fundamental Architecture of GAN -- 3.2.4.2 Implementing GAN Using MNIST Dataset -- 3.3 Research Perspective of Deep Learning…”

Tabla de Contenidos: “…7.3 XAI and Its Categorization -- 7.3.1 Intrinsic or Post-Hoc -- 7.3.2 Model-Specific or Model-Agnostic -- 7.3.3 Local or Global -- 7.3.4 Explanation Output -- 7.4 XAI Framework -- 7.4.1 SHAP (SHAPley Additive Explanations) and SHAPley Values -- 7.4.1.1 Computing SHAPley Values -- 7.4.2 LIME - Local Interpretable Model Agnostic Explanations -- 7.4.2.1 Working of LIME -- 7.4.3 ELI5 -- 7.4.4 Skater -- 7.4.5 DALEX -- 7.5 Applications of XAI in Cybersecurity -- 7.5.1 Smart Healthcare -- 7.5.2 Smart Banking -- 7.5.3 Smart Cities -- 7.5.4 Smart Agriculture -- 7.5.5 Transportation -- 7.5.6 Governance -- 7.5.7 Industry 4.0 -- 7.5.8 5G and Beyond Technologies -- 7.6 Challenges of XAI Applications in Cybersecurity -- 7.6.1 Datasets -- 7.6.2 Evaluation -- 7.6.3 Cyber Threats Faced by XAI Models -- 7.6.4 Privacy and Ethical Issues -- 7.7 Future Research Directions -- 7.8 Conclusion -- References -- Chapter 8 AI-Enabled Threat Detection and Security Analysis -- 8.1 Introduction -- 8.1.1 Phishing -- 8.1.2 Features -- 8.1.3 Optimizer Types -- 8.1.4 Gradient Descent -- 8.1.5 Types of Phishing Attack Detection -- 8.2 Literature Survey -- 8.3 Proposed Work -- 8.3.1 Data Collection and Pre-Processing -- 8.3.2 Dataset Description -- 8.3.3 Performance Metrics -- 8.4 System Evaluation -- 8.5 Conclusion -- References -- Chapter 9 Security Risks and Its Preservation Mechanism Using Dynamic Trusted Scheme -- 9.1 Introduction -- 9.1.1 Need of Trust -- 9.1.2 Need of Trust-Based Mechanism in IoT Devices -- 9.1.3 Contribution -- 9.2 Related Work -- 9.3 Proposed Framework -- 9.3.1 Dynamic Trust Updation Model -- 9.3.2 Blockchain Network -- 9.4 Performance Analysis -- 9.4.1 Dataset Description and Simulation Settings -- 9.4.2 Traditional Method and Evaluation Metrics -- 9.5 Results Discussion -- 9.6 Empirical Analysis -- 9.7 Conclusion -- References…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgements -- Chapter 1 Self-Powered Sensory Transducers: A Way Toward Green Internet of Things -- 1.1 Introduction -- 1.2 Need of the Work -- 1.3 Energy Scavenging Schemes in WSAN -- 1.3.1 Photovoltaic or Solar Cell -- 1.3.2 Temperature Gradient -- 1.3.3 Pressure Variations -- 1.3.4 Plant Microbial Fuel -- 1.3.5 Wind/Liquid Flow -- 1.3.6 Vibrations -- 1.3.7 Friction -- 1.4 Self Powered Systems and Green IoT (G-IoT) -- 1.5 Application Area and Scope of Self-Powered System in G-IoT -- 1.5.1 Terrestrial Applications -- 1.5.1.1 Agriculture -- 1.5.1.2 Smart Home and Cities -- 1.5.1.3 Industry -- 1.5.1.4 Medicines -- 1.5.1.5 Environment Monitoring -- 1.5.1.6 Structural Monitoring -- 1.5.1.7 Indoor Applications -- 1.5.1.8 Arial Vehicles -- 1.5.1.9 Military Applications -- 1.5.1.10 Underwater Applications -- 1.5.1.11 Submarine and Event Localization -- 1.5.1.12 Water Contamination -- 1.5.1.13 Intelligent Water Distribution and Smart Meter -- 1.5.1.14 Underground Applications -- 1.5.1.15 Coal and Petroleum Mining Application -- 1.5.1.16 Underground Structural Monitoring -- 1.6 Challenges and Future Scope of the Self-Powered G-IoT -- 1.6.1 Challenges Pertain to Energy Efficient Design and Protocols -- 1.6.2 Size and Cost of the Harvester -- 1.6.3 Energy-Efficient Routing and Scheduling Protocols -- 1.6.4 Design of Application-Specific Passive Wake-Up Receivers -- 1.6.5 Redefined Protocol with Application-Specific Goals -- 1.6.6 Embedded Operating Systems -- 1.6.7 AI and Cloud-Assisted Lifetime Prediction Techniques -- 1.6.8 Design of Energy-Efficient/Harvested Service-Oriented Architecture -- 1.6.9 Smart Web Interfaces for Monitoring -- 1.6.10 Cross Layer Exploitations with Energy Harvesting -- 1.6.11 Security Aspects and Need of Standardization…”

Tabla de Contenidos: “…Various machine learning algorithms -- 4.1 Linear regression -- 4.2 SVM -- 4.3 Naive Bayes -- 4.4 Logistic regression -- 4.5 k-Nearest neighbors -- 4.6 Decision trees -- 4.7 RF algorithm -- 4.8 Boosted gradient decision trees -- 4.9 Clustering with k-means -- 4.10 Analysis by principal components -- 5. …”

Tabla de Contenidos: “…3.2 Deep learning approaches for digital signal processing -- 3.3 Optical IM/DD systems based on deep learning -- 3.3.1 ANN receiver -- 3.3.1.1 PAM transmission -- 3.3.1.2 Sliding window FFNN processing -- 3.3.2 Auto-encoders -- 3.3.2.1 Auto-encoder design based on a feed-forward neural network -- 3.3.2.2 Auto-encoder design based on a recurrent neural network -- 3.3.3 Performance -- 3.3.4 Distance-agnostic transceiver -- 3.4 Implementation on a transmission link -- 3.4.1 Conventional PAM transmission with ANN-based receiver -- 3.4.2 Auto-encoder implementation -- 3.5 Outlook -- References -- 4 Machine learning techniques for passive optical networks -- 4.1 Background -- 4.2 The validation of NN effectiveness -- 4.3 NN for nonlinear equalization -- 4.4 End to end deep learning for optimal equalization -- 4.5 FPGA implementation of NN equalizer -- 4.6 Conclusions and perspectives -- References -- 5 End-to-end learning for fiber-optic communication systems -- 5.1 Introduction -- 5.2 End-to-end learning -- 5.3 End-to-end learning for fiber-optic communication systems -- 5.3.1 Direct detection -- 5.3.2 Coherent systems -- 5.3.2.1 Nonlinear phase noise channel -- 5.3.2.2 Perturbation models (NLIN and GN) -- 5.3.2.3 Split-step Fourier method (SSFM) -- 5.4 Gradient-free end-to-end learning -- 5.5 Conclusion -- Acknowledgments -- References -- 6 Deep learning techniques for optical monitoring -- 6.1 Introduction -- 6.2 Building blocks of deep learning-based optical monitors -- 6.2.1 Digital coherent reception as a data-acquisition method -- 6.2.2 Deep learning and representation learning -- 6.2.3 Combination of digital coherent reception and deep learning -- 6.3 Deep learning-based optical monitors -- 6.3.1 Training mode of DL-based optical monitors -- 6.3.2 Advanced topics for the training mode of DL-based optical monitors…”

“…The first section is about Biology and Ecology, and includes the following chapters: "Crustacean", "The robber crab Birgus latro (Linnaeus, 1767)", "Scyllarid lobster biology and ecology", "Management of the interaction and cannibalism of postlarvae and adults of the freshwater shrimp Cryphiops caementarius (Molina, 1782)", "Bateman gradients and alternative mating strategies in a marine isopod", and "The habitat types of freshwater prawns (Palaemonidae: Macrobrachium) with abbreviated larval development in Mesoamerica (Mexico, Guatemala)…”

“…You will: Discover convolutional nets and how to use them Build deep feedforward nets using locally connected layers, pooling layers, and softmax outputs Master the various programming algorithms required Carry out multi-threaded gradient computations and memory allocations for this threading Work with CUDA code implementations of all core computations, including layer activations and gradient calculations Make use of the CONVNET program and manual to explore convolutional nets and case studies…”

“…Of course, you will also learn how to draw lines and shapes; utilize patterns and gradients; and even create your own brushes, patterns, and gradients. …”

“…Luminal factors (nutrients or other dietary factors, secretions, and microbes), which occur within the lumen and distribute over a proximal-to-distal gradient, are also crucial for the maintenance of the normal gut mucosal growth and could explain the villous height-crypt depth gradient and variety of adaptations since these factors are diluted, absorbed, and destroyed as they pass down the digestive tract. …”

“…This guide covers: Selectors, specificity, and the cascade, including information on the new cascade layers New and old CSS values and units, including CSS variables and ways to size based on viewports Details on font technology and ways to use any available font variants Text styling, from basic decoration to changing the entire writing mode Padding, borders, outlines, and margins, now discussed in terms of the new block- and inline-direction layout paradigm used by modern browsers Colors, backgrounds, and gradients, including the conic gradients Accessible data tables Flexible box and grid layout systems, including new subgrid capabilities 2D and 3D transforms, transitions, and animation Filters, blending, clipping, and masking Media, feature, and container queries…”

“…Further, the book dives into reinforcement learning theory, explaining the Markov decision process, value function, policy, and policy gradients, with their mathematical formulations and learning algorithms. …”

“…As a result, there is a need to understand and, ideally, predict how bees respond to pollution disturbance, to the changes over landscape gradients, and how their responses can vary in different habitats, which are influenced to different degrees by human activities.Modeling approaches are useful to simulate the behavior of whole popula…”

“…In the last two sections, you will learn about loss functions to understand mean squared error, binary cross entropy, and categorical cross entropy and gradient descent to understand stochastic gradient descent, momentum, variable and adaptive learning rates, and Adam optimization. …”

“…This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. …”

“…Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym…”