Tabla de Contenidos: “…Foreword by Vera Songwe Foreword by Jutta Urpilainen 1 Digitalisation for Transformation: New Frontiers for Africa-Europe Cooperation 2 The EU-AU Digital Partnership: Between Digital Geopolitics and Digital Sovereignty 3 The Spread of Chinese Surveillance Tools in Africa: A Focus on Ethiopia and Kenya 4 The Artificial Intelligence - Biotech Revolution in Africa 5 African Micro, Small, and Medium Enterprises Need to Digitally Transform to Benefit from the Africa Continental Free Trade Area (AfCFTA) 6 Digitalisation, Globalisation, and COVID-19: Unpacking the Opportunities for African Labour Markets 7 Digitalise to Industrialise: Egypt, Morocco, Tunisia and the Africa-Europe Partnership 8 Digital Water: An Analysis of EU and the US Digital Water Initiatives in Africa 9 Digitalisation and Revenue Mobilisation in Ghana: The Role of Evidence-Based Stakeholder Engagements 10 Where Privacy Meets Politics: EU-Kenya Cooperation in Data Protection 11 Digitalisation in Science and Technology Policy: Engagement, Alignment, and Misalignment Between the European Union and South African Data Protection and Privacy Frameworks 12 Putting People at the Centre of Digital Policy: Mechanisms for Citizen Engagement in Nigeria 13 Digital Skills in Africa: Prospects for AU-EU Collaboration 14 Digitalisation and Transformative Learning for Sustainable Futures in Rural Africa: Leaving No One Behind 15 Feminist Digital Development: The Missing Jigsaw Piece in the European Union's Strategic Partnership with Africa 16 Fostering Prosperity for African Female Entrepreneurs: Opportunities for AU-EU Cooperation in Digital Entrepreneurship Networks…”

Tabla de Contenidos: “…Core competences and core resources as catalysts for the design of circular business models -- 15. Artificial intelligence as a catalyst in the circular economy transition -- 16. …”

Tabla de Contenidos: “…Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- List of Figures and Tables -- Preface -- Acknowledgments -- Editors -- Contributors -- Chapter 1: Introduction to IoT and WSN -- Chapter 2: The Role of Sustainable IoT in Present-Day Life -- Chapter 3: Designing an Integrated IoT-WSN Framework for Smart City Applications -- Chapter 4: Principles of Artificial Intelligence in the Internet of Things -- Chapter 5: Blockchain-Based Communication Frameworks for Smart Vehicles -- Chapter 6: A Sustainable IoT-Based Smart Transportation System for Urban Mobility -- Chapter 7: Optimizing Content Delivery in ICN-Based VANET Using Machine Learning Techniques -- Chapter 8: The Role of IoMT Technologies in Revolutionizing Healthcare: A Comprehensive Overview -- Chapter 9: Deep Learning and Its Applications in Healthcare -- Chapter 10: An Explainable Deep Learning Model for Clinical Decision Support in Healthcare -- Chapter 11: Deep Learning Models for Automated Diagnosis of Brain Tumor Disorder in Smart Healthcare -- Chapter 12: 6G and Distributed Computing for IoT: A Survey -- Chapter 13: Designing a Decentralized IoT-WSN Architecture Using Blockchain Technology -- Chapter 14: Privacy-Preserving Machine Learning on Non-Co-Located Datasets Using Federated Learning: Challenges and Opportunities -- Chapter 15: Deep Learning Method for Hyperspectral Image Classification -- Chapter 16: Toward Smarter Industries: Security Framework Using Attribute-Based Encryption and Systematic Solutions -- Index…”

Tabla de Contenidos: “…Microservices circuit breaker -- ABC-MSA enhancements -- Summary -- Part 2: Overview of Machine Learning Algorithms and Applications -- Chapter 4: Key Machine Learning Algorithms and Concepts -- The differences between artificial intelligence, machine learning, and deep learning -- Common deep learning and machine learning libraries used in Python -- Building regression models -- Building multiclass classification -- Text sentiment analysis and topic modeling -- Pattern analysis and forecasting in machine learning -- Enhancing models using deep learning -- Summary…”

Tabla de Contenidos: “…Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- Acknowledgments -- Author Biography -- Introduction -- Glossary -- Terms -- Distinctions -- Notes -- Chapter 1 Orientation -- Coming Out of Obscurity -- Who This Book Is for -- The Student -- The Writer From Another Field -- The Entertainment Executive -- Game Designer -- Game Producer -- Technologist in Media -- Content Designer for Artificial Intelligence -- Warning for Career Changers -- What This Book Is Not -- Notes -- References -- Chapter 2 Definitions and Distinctions -- What Is Narrative Design? …”

Tabla de Contenidos: “…Chapter 13: Fear of Canned FoodChapter 14: Potatoes and Birth Defects; Chapter 15: Fear of Oleomargarine; Chapter 16: Fear of Tomatoes; Chapter 17: Fear of Watercress; Chapter 18: Fear of Beef; Chapter 19: Coca-Cola and Polio; Chapter 20: Fear of Foreign Cheese; Chapter 21: Fear of Mercury in Seafood; Chapter 22: Java Madness; Chapter 23: Fear of Salmonella; Part Four: Additives in Foods and Beverages; Chapter 24: Fear of Red Dye; Chapter 25: Fear of Artificial Sweeteners; Chapter 26: Fear of Fluoridation and Chlorination; Chapter 27: Fear of MSG…”

“…Abarca desde la gestión de los proyectos y las técnicas de dibujo hasta la organización de los espacios, las características del color, los materiales y las texturas o las propiedades de la iluminación natural y artificial, los diferentes sistemas constructivos y principios generales de sostenibilidad. …”

“…A partir de ahí, empieza un viaje que parte de los filósofos presocráticos y llega a la inteligencia artificial, siguiendo el rastro de «lo humano» y la esencia del cambio. …”

“…Tenemos también otros frentes abiertos: la sociedad de consumo genera una creciente desigualdad, las clases medias tienden a diluirse, los robots y la inteligencia artificial hacen prescindibles muchos puestos de trabajo. …”

Tabla de Contenidos: “…Experimental Results; Acknowledgements; References; JORNADA II VISION ARTIFICIAL Y BIOMETRIA…”

Tabla de Contenidos: “…UTILIZACIÓN DE LOS SONDEOS ELÉCTRICOS VERTICALES COMO TÉCNICA PARA EL SEGUIMIENTO (...)BALANCE HÍDRICO EN LA CONCA D'ÒDENA (BARCELONA): VALORACIÓN Y OPORTUNIDADES DE REUTILIZACIÓN; RECARGA ARTIFICIAL; POSIBILIDADES DE ALMACENAMIENTO DE LAS AGUAS DEL TRASVASE JÚCAR-VINALOPÓ (...); RECARGA ARTIFICIAL EN EL ACUÍFERO COSTERO DE MARBELLA (MÁLAGA); LA RECARGA DE ACUÍFEROS COMO HERRAMIENTA DE GESTIÓN EN LA REUTILIZACIÓN DE AGUAS RESIDUALES: EL CASO DE BEGUR (GIRONA); HIDROGEOLOGÍA URBANA: ASPECTOS GENERALES; CONSIDERACIONES HIDROGEOLÓGICAS DEL DESARROLLO URBANO…”

Tabla de Contenidos: “…BIBLIOGRAFÍA; BLOQUE III - Tratamientos de Reproducción Asistida; INSEMINACIÓN ARTIFICIAL; 1. CONCEPTO. INTRODUCCIÓN HISTÓRICA; Concepto; Introducción histórica; 2. …”

Tabla de Contenidos: “…‘Phantom Fathers’ and ‘Test Tube Babies’: Debates on Marriage, Infertility and Artificial Insemination in the British Media, c.1957-60; Hayley Andrew -- 13. …”

Tabla de Contenidos: “…7.4.3 Future Generation and Extraction -- 7.4.4 Wavelet Energy -- 7.5 Feature Selection and Optimization -- 7.5.1 Genetic Algorithm -- 7.6 Machine Learning-Based Classification of PQ Disturbances -- 7.6.1 Support Vector Machine Classifier -- 7.6.2 Artificial Neural Network Classifier -- 7.6.2.1 Back-Propagation Neural Network -- 7.6.2.2 Probabilistic Neural Network -- 7.6.3 Performance Prediction of the ML Classifiers -- 7.7 Summary and Conclusion -- References -- Chapter 8 Hybridization of Artificial Neural Network with Spotted Hyena Optimization (SHO) Algorithm for Heart Disease Detection -- 8.1 Introduction -- 8.1.1 Objective of the Work -- 8.1.2 Scope of the Project -- 8.2 Literature Survey -- 8.2.1 Problem Identification -- 8.3 Proposed Methodology -- 8.3.1 Different Kinds of Machine Learning Approaches -- 8.3.1.1 Supervised Learning -- 8.3.1.2 Unsupervised Learning -- 8.3.1.3 Semi-Supervised Learning -- 8.3.1.4 Reinforcement Learning -- 8.4 Artificial Neural Network -- 8.4.1 ANN Classification -- 8.4.1.1 Input Layer -- 8.4.1.2 Hidden Layer -- 8.4.1.3 Output Layer -- 8.4.2 Spotted Hyena Optimization -- 8.4.2.1 Searching Behavior -- 8.4.2.2 Encircling Behavior -- 8.4.2.3 Hunting Behavior -- 8.4.2.4 Attacking Behavior -- 8.4.3 SHO-Based ANN -- 8.4.4 Benefits of SHO in ANN -- 8.5 Software Implementation Requirements -- 8.5.1 Results and Discussion -- 8.6 Conclusion -- References -- Chapter 9 The Role of Artificial Intelligence, Machine Learning, and Deep Learning to Combat the Socio-Economic Impact of the Global COVID-19 Pandemic -- 9.1 Introduction -- 9.2 Discussions on the Coronavirus -- 9.2.1 Coronavirus -- 9.2.2 COVID-19 -- 9.2.3 Origin of COVID-19 and Its Symptoms -- 9.2.4 Mode of Spreading -- 9.2.5 Steps Taken by the Government to Prevent the Spread of COVID-19 -- 9.3 Bad Impacts of the Coronavirus -- 9.3.1 Social Impact…”

Tabla de Contenidos: “…3.2.1 Concept and Definition of Smart Cities -- 3.2.1.1 Integration of Smart Cities with New Technologies -- 3.2.1.2 Development of Smart Cities by Integrated Technologies -- 3.2.2 Concept of Blockchain Technology -- 3.2.2.1 Features of Blockchain Technology -- 3.2.2.2 Framework and Working of Blockchain Technology -- 3.2.3 Concept and Definition of Artificial Intelligence -- 3.2.3.1 Classification of Artificial Intelligence-Machine Learning -- 3.3 Smart Cities Integrated with Blockchain Technology -- 3.3.1 Applications of Blockchain Technology in Smart City Development -- 3.3.1.1 Secured Data Transmission -- 3.3.1.2 Digital Transaction-Smart Contracts -- 3.3.1.3 Smart Energy Management -- 3.3.1.4 Modeling of Smart Assets -- 3.3.1.5 Smart Health System -- 3.3.1.6 Smart Citizen -- 3.3.1.7 Improved Safety -- 3.4 Smart Cities Integrated with Artificial Intelligence -- 3.4.1 Importance of AI for Developing Smart Cities -- 3.4.2 Applications of Artificial Intelligence in Smart City Development -- 3.4.2.1 Smart Transportation System -- 3.4.2.2 Smart Surveillance and Monitoring System -- 3.4.2.3 Smart Energy Management System -- 3.4.2.4 Smart Disposal and Waste Management System -- 3.5 Conclusion and Future Work -- References -- 4 Smart City a Change to a New Future World -- 4.1 Introduction -- 4.2 Role in Education -- 4.3 Impact of AI on Smart Cities -- 4.3.1 Botler AI -- 4.3.2 Spot -- 4.3.3 Nimb -- 4.3.4 Sawdhaan Application -- 4.3.5 Basic Use Cases of Traffic AI -- 4.4 AI and IoT Support in Agriculture -- 4.5 Smart Meter Reading -- 4.6 Conclusion -- References -- 5 Registration of Vehicles With Validation and Obvious Manner Through Blockchain: Smart City Approach in Industry 5.0 -- 5.1 Introduction -- 5.1.1 Concept of Smart Cities -- 5.1.2 Problem of Car Registration and Motivation -- 5.1.2.1 Research Objectives -- 5.1.2.2 Scope of the Research Work…”

Tabla de Contenidos: “…5.2.5 Fuzzy Microgrid Wind -- 5.3 Genetic Algorithm -- 5.3.1 Important Aspects of Genetic Algorithm -- 5.3.2 Standard Genetic Algorithm -- 5.3.3 Genetic Algorithm and Its Application -- 5.3.4 Power System and Genetic Algorithm -- 5.3.5 Economic Dispatch Using Genetic Algorithm -- 5.4 Artificial Neural Network -- 5.4.1 The Biological Neuron -- 5.4.2 A Formal Definition of Neural Network -- 5.4.3 Neural Network Models -- 5.4.4 Rosenblatt's Perceptron -- 5.4.5 Feedforward and Recurrent Networks -- 5.4.6 Back Propagation Algorithm -- 5.4.7 Forward Propagation -- 5.4.8 Algorithm -- 5.4.9 Recurrent Network -- 5.4.10 Examples of Neural Networks -- 5.4.10.1 AND Operation -- 5.4.10.2 OR Operation -- 5.4.10.3 XOR Operation -- 5.4.11 Key Components of an Artificial Neuron Network -- 5.4.12 Neural Network Training -- 5.4.13 Training Types -- 5.4.13.1 Supervised Training -- 5.4.13.2 Unsupervised Training -- 5.4.14 Learning Rates -- 5.4.15 Learning Laws -- 5.4.16 Restructured Power System -- 5.4.17 Advantages of Precise Forecasting of the Price -- 5.5 Conclusion -- References -- 6 Recent Advances in Wearable Antennas: A Survey -- 6.1 Introduction -- 6.2 Types of Antennas -- 6.2.1 Description of Wearable Antennas -- 6.2.1.1 Microstrip Patch Antenna -- 6.2.1.2 Substrate Integrated Waveguide Antenna -- 6.2.1.3 Planar Inverted-F Antenna -- 6.2.1.4 Monopole Antenna -- 6.2.1.5 Metasurface Loaded Antenna -- 6.3 Design of Wearable Antennas -- 6.3.1 Effect of Substrate and Ground Geometries on Antenna Design -- 6.3.1.1 Conducting Coating on Substrate -- 6.3.1.2 Ground Plane With Spiral Metamaterial Meandered Structure -- 6.3.1.3 Partial Ground Plane -- 6.3.2 Logo Antennas -- 6.3.3 Embroidered Antenna -- 6.3.4 Wearable Antenna Based on Electromagnetic Band Gap -- 6.3.5 Wearable Reconfigurable Antenna -- 6.4 Textile Antennas -- 6.5 Comparison of Wearable Antenna Designs…”

Tabla de Contenidos: “…3.4.1 Convolutional neural network -- 3.4.2 Long short-term memory recurrent neural network -- 3.4.3 Bidirectional LSTM -- 3.4.4 LSTM fully convolutional networks -- 3.4.5 Residual deep networks -- References -- 4 Parameter estimation for glucose-insulin dynamics -- 4.1 Affine system -- 4.1.1 Affine system of the Dalla Man model -- 4.1.2 Affine system of Sorensen model -- 4.2 Evolutionary optimization algorithms for parameter estimation -- 4.2.1 Methodology for parametric estimation -- 4.3 Parametric estimation results in compartmental models -- 4.3.1 Sorensen parametric estimation results -- 4.3.1.1 Results obtained by Evonorm -- 4.3.1.2 Results obtained by Differential Evolution -- 4.3.1.3 Results obtained by PSO -- 4.3.1.4 Results obtained by ACO -- 4.3.2 Dalla Man parametric estimation results -- 4.3.2.1 Results obtained by Evonorm -- 4.3.2.2 Results obtained by DE -- 4.3.2.3 Results obtained by PSO -- 4.3.2.4 Results obtained by ACO -- References -- 5 Neural model for glucose-insulin dynamics -- 5.1 Identification -- 5.2 Identification with artificial neural networks -- 5.3 System description -- 5.3.1 Identification with artificial neural networks offline -- 5.3.1.1 Neuronal network configuration using CGM -- 5.3.1.2 LSTM identification results -- 5.3.1.3 BiLSTM results -- 5.3.1.4 MLP results -- 5.3.2 Identification with an artificial neural network online -- 5.3.2.1 RHONN identification structure -- 5.3.3 RHONN results -- References -- 6 Multistep predictor applied to T1DM patients -- 6.1 Prediction -- 6.1.1 Multistep ahead prediction strategies -- 6.1.2 Recursive strategy -- 6.1.3 Direct strategy -- 6.1.4 Neuronal network configuration using CGM data -- 6.2 Prediction results evaluation criteria -- 6.3 Results -- 6.3.1 Neural models with offline training -- 6.3.1.1 MLP results -- 6.3.1.2 LSTM results -- 6.3.1.3 BiLSTM results…”

“…Es un medio que permite explorar teorías e informar a las audiencias, y, gracias a su flexibilidad como forma de construcción artificial, se adapta bien a una amplia gama de aplicaciones de comunicación. …”

“…La metodología a seguir incluye una encuesta sobre el conocimiento público y la percepción de la Ciencia de los Datos, cuyo objetivo es medir el nivel de conocimiento y comprensión del público, así como las actitudes hacia el Big Data y la Inteligencia Artificial, con especial atención a las posibles brechas debido al género, la edad y el nivel socioeconómico…”
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“…Sus voceros suelen desentenderse de quienes saldrán perdiendo con el despliegue masivo de la inteligencia artificial, los robots y el Internet de las cosas, y tienden a endilgar sus consecuencias a los gobiernos o al resto de la sociedad. …”