Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 A Comprehensive Review on Text Classification and Text Mining Techniques Using Spam Dataset Detection -- 1.1 Introduction -- 1.2 Text Mining Techniques -- 1.2.1 Data Mining -- 1.2.2 Information Retrieval -- 1.2.3 Natural Language Processing (NLP) -- 1.2.4 Information Extraction -- 1.2.5 Text Summarization -- 1.2.6 Text Categorization -- 1.2.7 Clustering -- 1.2.8 Information Visualization -- 1.2.9 Question Answer -- 1.3 Dataset and Preprocessing Steps -- 1.3.1 Text Preprocess -- 1.4 Feature Extraction -- 1.4.1 Term Frequency - Inverse Document Frequency -- 1.4.2 Bag of Words (BoW) -- 1.5 Supervised Machine Learning Classification -- 1.6 Evaluation -- 1.7 Experimentation and Discussion Results for Spam Detection Data -- 1.8 Text Mining Applications -- 1.9 Text Classification Support -- 1.9.1 Health -- 1.9.2 Business and Marketing -- 1.9.3 Law -- 1.10 Conclusions -- References -- Chapter 2 Study of Lidar Signals of the Atmospheric Boundary Layer Using Statistical Technique -- 2.1 Introduction -- 2.2 Methodology -- 2.2.1 A Statistical Approach to Determine the CBLH -- 2.2.2 A Statistical Approach to Determine the Best Fit Distribution to the Backscatter Signals of the Lidar Dataset -- 2.3 Mathematical Background of Method -- 2.4 Example and Result -- 2.5 Conclusion and Future Scope -- Acknowledgement -- References -- Annexure -- Chapter 3 Optimal Personalized Therapies in Colon Cancer Induced Immune Response using a Fokker-Planck Framework -- 3.1 Introduction -- 3.2 The Control Framework Based on Fokker-Planck Equations -- 3.3 Theoretical Results -- 3.4 Numerical Schemes -- 3.5 Results -- 3.6 Conclusion -- Acknowledgments -- References -- Chapter 4 Detection and Classification of Leaf Blast Disease using Decision Tree Algorithm in Rice Crop -- 4.1 Introduction…”

Tabla de Contenidos: “…2 Background -- 2.1 Fairness of the Learned Model -- 2.2 Fairness in the Presence of Dataset Bias -- 3 Overview of Our Method -- 3.1 The KNN Algorithm -- 3.2 Certifying the KNN Algorithm -- 4 Abstracting the KNN Prediction Step -- 4.1 Finding the K-Nearest Neighbors -- 4.2 Checking the Classification Result -- 5 Abstracting the KNN Learning Step -- 5.1 Overapproximating the Classification Error -- 5.2 Underapproximating the Classification Error -- 6 Experiments -- 7 Related Work -- 8 Conclusions -- References -- Monitoring Algorithmic Fairness -- 1 Introduction -- 1.1 Motivating Examples -- 1.2 Related Work -- 2 Preliminaries -- 2.1 Markov Chains as Randomized Generators of Events -- 2.2 Randomized Register Monitors -- 3 Algorithmic Fairness Specifications and Problem Formulation -- 3.1 Probabilistic Specification Expressions -- 3.2 The Monitoring Problem -- 4 Frequentist Monitoring -- 4.1 The Main Principle -- 4.2 Implementation of the Frequentist Monitor -- 5 Bayesian Monitoring -- 5.1 The Main Principle -- 5.2 Implementation of the Bayesian Monitor -- 6 Experiments -- 7 Conclusion -- References -- nl2spec: Interactively Translating Unstructured Natural Language to Temporal Logics with Large Language Models -- 1 Introduction -- 2 Background and Related Work -- 2.1 Natural Language to Linear-Time Temporal Logic -- 2.2 Large Language Models -- 3 The nl2spec Framework -- 3.1 Overview -- 3.2 Interactive Few-Shot Prompting -- 4 Evaluation -- 4.1 Study Setup -- 4.2 Results -- 5 Conclusion -- References -- NNV 2.0: The Neural Network Verification Tool -- 1 Introduction -- 2 Related Work -- 3 Overview and Features -- 3.1 NNV 2.0 vs NNV -- 4 Evaluation -- 4.1 Comparison to MATLAB's Deep Learning Verification Toolbox -- 4.2 Neural Ordinary Differential Equations -- 4.3 Recurrent Neural Networks -- 4.4 Semantic Segmentation -- 5 Conclusions -- References…”

Tabla de Contenidos: “…11.2 Challenges in recognition of hand gestures -- 11.3 Approaches' types -- 11.4 Literature review on vision-based gesture recognition -- 11.5 Data acquisition -- 11.6 Pre-processing of acquired data -- 11.7 Segmentation -- 11.7.1 Segmentation of skin color -- 11.7.2 Other methods for segmentation -- 11.7.3 Tracking -- 11.8 Feature extraction -- 11.8.1 Principal component analysis (PCA) -- 11.8.2 Linear discriminant analysis (LDA) -- 11.8.3 Shift-invariant feature transform (SIFT) -- 11.9 Classification -- 11.9.1 Artificial neural network (ANN) -- 11.9.2 Support vector machine (SVM) -- 11.9.3 Euclidean distance classifier -- 11.9.4 K-nearest neighbor classifier -- 11.10 Literature review on sensor-based gesture recognition -- 11.10.1 Data glove -- 11.10.2 Electromyography (EMG) -- 11.10.3 Radar and WiFi -- 11.11 Discussion -- 11.11.1 Critical review on the previous survey -- 11.11.2 Reviews on considered algorithms and methodologies -- 11.12 Conclusion and future work -- References -- Index…”

Tabla de Contenidos: “…ONE STEP BEYOND Order Out of Chaos -- Part II Problems -- Part III Properties of Matter -- OVERVIEW Beyond the Particle Model -- Chapter 12 Thermal Properties of Matter -- 12.1 The Atomic Model of Matter -- 12.2 The Atomic Model of an Ideal Gas -- 12.3 Ideal-Gases Processes -- 12.4 Thermal Expansion -- 12.5 Specific Heat and Heat of Transformation -- 12.6 Calorimetry -- 12.7 Specific Heats of Gases -- 12.8 Heat Transfer -- Summary -- Questions and Problems -- Chapter 13 Fluids -- 13.1 Fluids and Density -- 13.2 Pressure -- 13.3 Measuring and Using Pressure -- 13.4 Buoyancy -- 13.5 Fluids in Motion -- 13.6 Fluid Dynamics -- 13.7 Viscosity and Poiseuille's Equation -- Summary -- Questions and Problems -- Part III Summary Properties of Matter -- ONE STEP BEYOND Size and Life -- PART III PROBLEMS -- Part IV Oscillations and Waves -- OVERVIEW Motion That Repeats Again and Again -- Chapter 14 Oscillations -- 14.1 Equilibrium and Oscillation -- 14.2 Linear Restoring Forces and Simple Harmonic Motion -- 14.3 Describing Simple Harmonic Motion -- 14.4 Energy in Simple Harmonic Motion -- 14.5 Pendulum Motion -- 14.6 Damped Oscillations -- 14.7 Driven Oscillations and Resonance -- Summary -- Questions and Problems -- Chapter 15 Traveling Waves and Sound -- 15.1 The Wave Model -- 15.2 Traveling Waves -- 15.3 Graphical and Mathematical Descriptions of Waves -- 15.4 Sound and Light Waves -- 15.5 Energy and Intensity -- 15.6 Loudness of Sound -- 15.7 The Doppler Effect and Shock Waves -- Summary -- Questions and Problems -- Chapter 16 Superposition and Standing Waves -- 16.1 The Principle of Superposition -- 16.2 Standing Waves -- 16.3 Standing Waves on a String -- 16.4 Standing Sound Waves -- 16.5 Speech and Hearing -- 16.6 The Interference of Waves from Two Sources -- 16.7 Beats -- Summary -- Questions and Problems -- Part IV Summary Oscillations and Waves…”

Tabla de Contenidos: “…Practice Questions -- Further Questions -- Chapter 18: Binomial Trees in Practice -- 18.1 The Binomial Model for a Non-Dividend-Paying Stock -- 18.2 Using the Binomial Tree for Options on Indices, Currencies, and Futures Contracts -- 18.3 The Binomial Model for a Dividend-Paying Stock -- 18.4 Extensions of the Basic Tree Approach -- 18.5 Alternative Procedure for Constructing Trees -- 18.6 Monte Carlo Simulation -- Summary -- Further Reading -- Quiz -- Practice Questions -- Further Questions -- Chapter 19: Volatility Smiles -- 19.1 Foreign Currency Options -- 19.2 Equity Options -- 19.3 The Volatility Term Structure and Volatility Surfaces -- 19.4 When a Single Large Jump Is Anticipated -- Summary -- Further Reading -- Quiz -- Practice Questions -- Further Questions -- Appendix: Why the Put Volatility Smile Is the Same As the Call Volatility Smile -- Chapter 20: Value at Risk and Expected Shortfall -- 20.1 The VaR and ES Measures -- 20.2 Historical Simulation -- 20.3 Model-Building Approach -- 20.4 Generalization of Linear Model -- 20.5 Quadratic Model -- 20.6 Estimating Volatilities and Correlations -- 20.7 Comparison of Approaches -- 20.8 Back Testing -- Summary -- Further Reading -- Quiz -- Practice Questions -- Further Questions -- Chapter 21: Interest Rate Options -- 21.1 Exchange-Traded Interest Rate Options -- 21.2 Embedded Bond Options -- 21.3 Black's Model -- 21.4 European Bond Options -- 21.5 Interest Rate Caps -- 21.6 European Swap Options -- 21.7 Term Structure Models -- Summary -- Further Reading -- Quiz -- Practice Questions -- Further Questions -- Chapter 22: Exotic Options and Other Nonstandard Products -- 22.1 Exotic Options -- 22.2 Agency Mortgage-Backed Securities -- 22.3 Nonstandard Swaps -- Summary -- Further Reading -- Quiz -- Practice Questions -- Further Questions -- Chapter 23: Credit Derivatives -- 23.1 Credit Default Swaps…”

Tabla de Contenidos: “…Intro -- Notations -- Conventions -- Contents -- 1 Very Personal Introduction -- 2 How to Define Differential Operators on Metric Graphs -- 2.1 Schrödinger Operators on Metric Graphs -- 2.1.1 Metric Graphs -- 2.1.2 Differential Operators -- 2.1.3 Standard Vertex Conditions -- 2.1.4 Definition of the Operator -- 2.2 Elementary Examples -- 3 Vertex Conditions -- 3.1 Preliminary Discussion -- 3.2 Vertex Conditions for the Star Graph -- 3.3 Vertex Conditions Via the Vertex Scattering Matrix -- 3.3.1 The Vertex Scattering Matrix -- 3.3.2 Scattering Matrix as a Parameterin the Vertex Conditions -- 3.3.3 On Properly Connecting Vertex Conditions -- 3.4 Parametrisation Via Hermitian Matrices -- 3.5 Scaling-Invariant and Standard Conditions -- 3.5.1 Energy Dependence of the Vertex S-matrix -- 3.5.2 Scaling-Invariant, or Non-Robin Vertex Conditions -- 3.5.3 Standard Vertex Conditions -- 3.6 Signing Conditions for Degree Two Vertices -- 3.7 Generalised Delta Couplings -- 3.8 Vertex Conditions for Arbitrary Graphs and Definition of the Magnetic Schrödinger Operator -- 3.8.1 Scattering Matrix Parametrisationof Vertex Conditions -- 3.8.2 Quadratic Form Parametrisation of Vertex Conditions -- Appendix 1: Important Classes of Vertex Conditions -- δ and δ'-Couplings -- Circulant Conditions -- `Real' Conditions -- Indistinguishable Edges -- Equi-transmitting Vertices -- Appendix 2: Parametrisation of Vertex Conditions: Historical Remarks -- Parametrisation Via Linear Relations -- Parametrisation Using Hermitian Operators -- Unitary Matrix Parametrisation -- 4 Elementary Spectral Properties of Quantum Graphs -- 4.1 Quantum Graphs as Self-adjoint Operators -- 4.2 The Dirichlet Operator and the Weyl's Law -- 4.3 Spectra of Quantum Graphs -- 4.4 Laplacian Ground State -- 4.5 Bonus Section: Positivity of the Ground Statefor Quantum Graphs…”

Tabla de Contenidos: “…Front Cover -- Handbook of Metaheuristic Algorithms -- Copyright -- Contents -- List of figures -- List of tables -- List of algorithms -- List of listings -- About the authors -- Chun-Wei Tsai (1978-) -- Ming-Chao Chiang (1956-) -- Preface -- Part 1 Fundamentals -- 1 Introduction -- 1.1 Why metaheuristic algorithms -- 1.2 Organization of this book -- 2 Optimization problems -- 2.1 Problem definition -- 2.2 Combinatorial optimization problems -- 2.2.1 The one-max and 0-1 knapsack problems -- 2.2.2 The B2D and deceptive problems -- 2.2.3 The traveling salesman problem (TSP) -- 2.3 Continuous optimization problems -- 2.3.1 The single-objective optimization problem -- 2.3.2 The multi-objective optimization problem -- 2.4 Summary -- 3 Traditional methods -- 3.1 Exhaustive search (ES) -- 3.1.1 The basic idea of ES -- 3.1.2 Implementation of ES for the one-max problem -- 3.1.3 Discussion of ES -- 3.2 Hill climbing (HC) -- 3.2.1 The basic idea of HC -- 3.2.2 Implementation of HC for the one-max problem -- 3.2.2.1 Main function -- 3.2.2.2 Search function of HC -- 3.2.2.2.1 Declaration of parameters and functions -- 3.2.2.2.2 The main loop -- 3.2.2.2.3 Additional functions -- 3.2.2.3 Library function -- 3.2.3 Discussion of HC -- 3.3 Comparisons between ES and HC -- 3.3.1 Simulation results of ES and HC for the one-max problem -- 3.3.2 Simulation results of ES and HC for the deceptive problem -- 3.4 Summary of ES and HC -- Supplementary source code -- 4 Metaheuristic algorithms -- 4.1 What is a metaheuristic algorithm? …”

Tabla de Contenidos:

Tabla de Contenidos: “…Oscillations -- 14.1 Equilibrium and Oscillation -- 14.2 Linear Restoring Forces and SHM -- 14.3 Describing Simple Harmonic Motion -- 14.4 Energy in Simple Harmonic Motion -- 14.5 Pendulum Motion -- 14.6 Damped Oscillations -- 14.7 Driven Oscillations and Resonance -- Summary -- Questions And Problems -- Chapter 15. …”

Tabla de Contenidos: “…Flash Memory -- 6.13.1 Uses of FRAM Devices -- 6.14 Conclusion and Upcoming Trends -- References -- Chapter 7 Applications of AI/ML Algorithms in VLSI Design and Technology -- 7.1 Introduction -- 7.2 Artificial Intelligence and Machine Learning -- 7.3 AI/ML Algorithms -- 7.4 Supervised Machine Learning (SML) -- 7.5 Classification Techniques -- 7.6 K-Nearest Neighbors (KNN) -- 7.7 Support Vector Machine (SVM) -- 7.8 Linearly Separable Classification -- 7.9 Decision Tree Classifier (DTC) -- 7.10 Performance Measures in Classification -- 7.11 Unsupervised Machine Learning (UML) -- 7.12 Hierarchical Clustering -- 7.13 Partitional Clustering -- 7.14 K-Means -- 7.15 Fuzzy (soft) Clustering -- 7.16 Cluster Validation Measures -- 7.17 Internal Clustering Validation Measures -- 7.18 External Clustering Validation Criteria -- 7.19 Limitation and Challenges - VLSI -- References -- Chapter 8 Advancement of Neuromorphic Computing Systems with Memristors -- 8.1 Introduction -- 8.1.1 Evolution in Neural Networks -- 8.1.2 Study Plan and Difficulties in Exhibiting Effective Neuromorphic Computing Systems -- 8.1.3 Hardware for Neuromorphic Systems -- 8.1.4 Device-Level Perspective -- 8.1.5 Electrical Circuits to Realize Neurons -- 8.1.6 Broad Applications of Neuromorphic Computing -- 8.2 Summary -- References -- Chapter 9 Neuromorphic Computing and Its Application -- 9.1 Introduction -- 9.2 Evolution of Neuroinspired Computing Chips -- 9.3 Science Behind Brain Physics -- 9.4 Limitations of Semiconductor Devices -- 9.5 Various Combination of Networks -- 9.5.1 ANN-SNN Hybrid -- 9.5.2 Convolutional Neural Network (CNN)-Recurrent Neural Network (RNN) Hybrid -- 9.5.3 Deep Reinforcement Learning (DRL) Hybrid -- 9.5.4 Ensemble Hybrid -- 9.5.5 Different Types of Neural Networks -- 9.6 Artificial Intelligence…”

Tabla de Contenidos: “…9.1 Pages to Prototypes: A Bookworm's Expedition into the World of STEM -- 9.2 Cultivating Curiosity: How a Microbiology Class Shaped My Approach to Education -- 9.3 From Novels to Code: Exploring the Intersection of Creativity and Technology -- 9.4 From Teacher to Maker Librarian: Embracing STEM in the Library -- 9.5 From Maker Librarian to STEM Curriculum Innovator: Shaping the Educational Landscape -- About the Author -- Chapter 10: Maneuvering the Moving Sidewalk of Your Career -- 10.1 Defining the Stages of Our Careers -- 10.2 Early Career - Learning and Exploring -- 10.3 Mid-Career - Competence and Performance -- 10.3.1 Why Do Many Engineers Leave the Field Mid-Career? …”

“…You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. …”

“…Nonostante l’Open Access, vale a dire la disponibilità libera e gratuita dei contenuti in internet, sia ormai un’iniziativa conosciuta, ci sono ancora idee molto confuse e interpretazioni erronee su alcune delle sue caratteristiche e sul suo funzionamento. …”

“…If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data to training models and leveraging neural networks. …”

“…This IBM® Redbooks® publication follows The IBM System Storage Tape Libraries Guide for Open Systems, SG24-5946, and can help you plan, install, and configure IBM Ultrium LTO tape drives, as well as the TS1120 Tape Drive and libraries in i5/OS® environments. …”

Tabla de Contenidos: “…Front Cover; Contents; Preface; Acknowledgments; About the Author; Chapter 1: Safety, Health, and Environmental (SHE) Pillar: Foundation of Lean Continuous Improvement; Chapter 2: Autonomous SHE; Chapter 3: Preparation Phase: Laying the Foundation for SHE Excellence; Chapter 4: Integrating SHE into the Autonomous Maintenance Pillar; Chapter 5: Integrating SHE, Planned Maintenance, and Early Management; Chapter 6: Focused Improvement, Training and Education, and SHE; Chapter 7: SHE Pillar Activities; Chapter 8: Lean and Green: Applying Lean to the Environment; Bibliography; Back Cover…”

“…It examines the portrayal of libraries and librarians in fiction, often marked by stereotypes, and celebrates the library as a space of cultural and intellectual engagement. …”

“…TensorFlow is the world's most popular library for deep learning, and it is built by Google. …”

“…What you will learn Understand matrix and vector derivatives Master linear and quadratic forms Apply the chain rule in matrix calculus Solve optimization problems using gradient descent and Newton's method Set up the Anaconda environment for machine learning Install and use key libraries like Numpy and TensorFlow Develop effective strategies for learning calculus in data science Audience This course suits students and professionals eager to learn the math behind AI, Data Science, and Machine Learning, ideal for deepening knowledge in these advanced technology fields. …”

“…This is therefore an axiology of the library and even more than that, a defence of the library's value, but also a treatise on the library and the librarians advocacy, of a system and a profession which must continually evolve to reaffirm and consolidate their centrality in the social and cultural context…”