Tabla de Contenidos: “…Notes -- References -- Chapter 5: Unsupervised Learning -- K -means Clustering -- Hierarchical Clustering -- Association Rule Mining -- K -Nearest Neighbors -- Summary -- Exercise -- References -- Chapter 6: Supervised Learning -- Introduction to Artificial Neural Networks -- Forward and Backward Propagation Methods -- Architectural Types in ANN -- Hyperparameters for Tuning the ANN -- An Example of ANN Classification -- Introduction to Ensemble Learning Techniques -- Random Forest Ensemble Learning -- Introduction to AdaBoost Ensemble Learning -- Introduction to Extreme Gradient Boosting (XGB) -- Cross-Validation -- Summary -- Exercise -- References -- Chapter 7: Natural Language Processing for Analyzing Unstructured Data -- Terminology for NLP -- Installing NLTK and Other Libraries -- Tokenization -- Stemming -- Stopwords -- Part of Speech Tagging -- Bag-of-Words (BOW) -- n- grams -- Sentiment and Emotion Classification -- Summary -- Exercise -- References -- Chapter 8: Predictive Analytics Using Deep Neural Networks -- Introduction to Deep Learning -- The Deep Neural Networks and Its Architectural Variants -- Multilayer Perceptron (MLP) -- Convolutional Neural Networks (CNN) -- Recurrent Neural Networks (RNN) -- AlexNet -- VGGNet -- Inception -- ResNet and GoogLeNet -- Hyperparameters of DNN and Strategies for Tuning Them -- Activation Function -- Regularization -- Number of Hidden Layers -- Number of Neurons Per Layer -- Learning Rate -- Optimizer -- Batch Size -- Epoch -- Weight and Biases Initialization -- Grid Search -- Random Search -- Deep Belief Networks (DBN) -- Analyzing the Boston Housing Dataset Using DNN -- Summary -- Exercise -- References -- Chapter 9: Convolutional Neural Networks (CNN) for Predictive Analytics -- Convolution Layer -- Padding and Strides -- ReLU LAYER -- Pooling Layer -- Fully Connected Layer…”

Tabla de Contenidos: “…Ticketing to Concerts and Sports Events Will Never Be the Same -- A Game‐Changer for Cartoons, Film, and Television -- Culture Is Evolving with the Community -- The Phygital Boom -- Music and Art Drive Culture -- Some of Us Still Work at the Office and Your Employees Can Benefit -- Transform Communities with Decentralized Autonomous Organizations (DAOs) -- Mic Drop -- Chapter 7: The Perfect Playbook: How to Make the Right Calls -- The Old Playbook -- Rewriting the Playbook -- Making the Right Calls -- Education -- Developing a Strategic Stance -- Proper Use Cases -- Building a Thriving Community -- Understanding Web3 Communities -- Defining the Community Objectives -- Test and Iterate -- Prepare to Scale -- Conclusion -- Chapter 8: The Rise of the Machines: How AI Will Affect Us All -- The Rise of the Machines -- Transforming the Future of the Internet -- Technology Running Mates -- The Power to Transform Community -- Controlling the Rise of the Machines -- Conclusion -- Chapter 9: Liars, Cheaters, Scammers, and Cash Grabbers -- Crypto and Cartoon Elite -- New Path Forward -- Chapter 10: The Future of Work -- Part 1: The Metaverse -- What the Metaverse Is Not -- Only One Metaverse -- The Next Iteration of the Internet -- Metaverse Goals: A Strategic Stance -- Conclusion -- Part 2: The Industrial Metaverse -- The Future of Work -- Ice, Ice Baby -- Acquiring the Right Talent -- Conclusion -- Chapter 11: The Future of Community -- A New Beginning -- Giving Power Back to the People -- Driving Innovation -- Rome Wasn't Built in a Day -- Artificial Intelligence and the Evolution of Community, Brand Building, and Social Media: A Dive into the Next Wave of Human Connection -- Quantum Computing: Revolutionizing Community Building, Branding, and Social Media Interactions in the Digital Era…”

Tabla de Contenidos: “…1.3.3 Machine Learning and Artificial Intelligence -- 1.3.4 Game Development -- 1.3.5 Automation and Scripting -- 1.3.6 Cybersecurity -- 1.3.7 Internet of Things (IoT) -- 1.3.8 Robotics -- 1.3.9 Bioinformatics and Computational Biology -- 1.3.10 Education -- 1.4 Setting up the Python Environment and Writing Your First Python Program -- 1.4.1 Setting up Python Environment -- 1.4.2 Your First Python Program -- Chapter 1 Conclusion -- Chapter 2: Python Building Blocks -- 2.1 Python Syntax and Semantics -- 2.1.1 Python Syntax -- 2.1.2 Python Semantics -- 2.2 Variables and Data Types -- 2.2.1 Integers…”

Tabla de Contenidos: “…Hill-RBF: Improving IOL Power Selection by Artificial Intelligence -- Chapter 43. Hoffer Formulas -- Chapter 44. …”

Tabla de Contenidos: “…-- CAPÍTULO 27 HERRAMIENTA DE EVALUACIÓN PARA ANALIZAR MANUALES ESPECÍFICOS DE ALEMÁN PARA TURISMO -- CAPÍTULO 28 CONCEPTOS Y APLICACIONES DOCENTES DE LA INTELIGENCIA ARTIFICIAL (IA) Y EL PROCESAMIENTO DEL LENGUAJE NATURAL (PLN) -- CAPÍTULO 29 CÓMO ENSEÑAR LA PRÁCTICA DE TRABAJO SOCIAL: OBJETO Y PRINCIPIOS -- CAPÍTULO 30 INICIACIÓN A LA INVESTIGACIÓN EN CIENCIAS SOCIALES EN ESTUDIANTES DE SECUNDARIA A PARTIR DE UN PROYECTO SOBRE VALORES -- CAPÍTULO 31 PROYECTO DE INNOVACIÓN EDUCATIVA SOBRE LA SOSTENIBILIDAD Y EL CAMBIO CLIMÁTICO: EL PENSAMIENTO CRÍTICO A TRAVÉS DE LA CREACIÓN ARTÍSTICA -- CAPÍTULO 32 DESESTIGMATIZANDO EL TRASTORNO LÍMITE DE PERSONALIDAD: CREACIÓN DE RECURSOS AUDIOVISUALES RESPETUOSOS A TRAVÉS DEL APRENDIZAJE COLABORATIVO Y EXPERIENCIAL ENTRE ESTUDIANTES DE PSICOLOGÍA Y COMUNICACIÓN AUDIOVISUAL -- CAPÍTULO 33 BASE TEÓRICA PARA LA APLICACIÓN DE LA NEUROCIENCIA EN EL ÁREA DE EDUCACIÓN FÍSICA: HERRAMIENTAS BIBLIIOGRÁFICAS PARA EL DOCENTE EN LA ETAPA DE PRIMARIA -- CAPÍTULO 34 AULA INVERTIDA (FLIPPED CLASSROOM): INNOVANDO LAS CLASES DE OBSERVACIÓN EN LA ESCUELA -- CAPÍTULO 35 COMPETENCIAS TRANSVERSALES: CÓMO FOMENTAR SU PRESENCIA EN LA EDUCACIÓN SUPERIOR -- CAPÍTULO 36 DIÁLOGO INTERCULTURAL EN LA FORMACIÓN DE MAESTROS AICLE PARA EDUCACIÓN PRIMARIA: ADAPTACIONES CURRICULARES PARA ESTUDIANTES EXTRANJEROS -- CAPÍTULO 37 LAS TÉMPERAS PLÁSTICAS Y EL APRENDIZAJE EXPERIENCIAL PROTAGONISTAS EN LA CREACIÓN DE JUEGOS DIDÁCTICOS PARA EL AULA: EXPERIENCIAS EN EL GRADO DE EDUCACIÓN INFANTIL -- CAPÍTULO 38 TERRITORIOS DE PAZ, GESTIÓN DEL CONOCIMIENTO Y COMUNIDADES DE APRENDIZAJE COMO FACTOR PROTECTOR EN EL ÁMBITO ESCOLAR -- CAPÍTULO 39 EVALUACIÓN DE LA INCLUSIÓN DE CONTENIDO DE ORGANIZACIÓN DE EVENTOS EN LA ASIGNATURA DE GESTIÓN DE LA COMUNICACIÓN CORPORATIVA MEDIANTE LA APLICACIÓN DE UN CASO PRÁCTICO -- CAPÍTULO 40 METODOLOGÍA DE ENSEÑANZA-APRENDIZAJE DE LA ASIGNATURA DE TRABAJO CON COMUNIDADES BASADA EN PRÁCTICAS DE CAMPO EN CONTEXTOS REALES -- CAPÍTULO 41 IMPACTO DE LA GUERRA EN UCRANIA EN LOS GRADOS DE PERIODISMO Y COMUNICACIÓN AUDIOVISUAL: UN ESTUDIO PRELIMINAR -- CAPÍTULO 42 PENSAR PARA APRENDER MEJOR: UNA METODOLOGÍA PARA ENSEÑAR A PENSAR COMO BASE DEL APRENDIZAJE ACADÉMICO -- CAPÍTULO 43 PERSEGUIR LOS FINES USANDO LOS MEDIOS: PENSAR LA EDUCACIÓN EN LA ERA DIGITAL -- CAPÍTULO 44 REVISIÓN BIBLIOGRÁFICA DE LAS INNOVACIONES EDUCATIVAS LLEVADAS A CABO EN LA FORMACIÓN INICIAL DOCENTE PARA EL DESARROLLO DE LA COMPETENCIA DIGITAL -- CAPÍTULO 45 APLICACIÓN DE LAS REDES SOCIALES EN LA DOCENCIA UNIVERSITARIA: INNOVACIÓN EDUCATIVA PARA EL DESARROLLO DE UN APRENDIZAJE ACTIVO E INCLUSIVO -- CAPÍTULO 46 INNOVACIONES EDUCATIVAS PARA LA COMPETENCIA DIGITAL EN LA ESCUELA PRIMARIA Y SECUNDARIA -- CAPÍTULO 47 UN ESTUDIO DE LAS PERCEPCIONES DEL FUTURO PROFESORADO SOBRE LA IDENTIDAD DE GÉNERO -- CAPÍTULO 48 LA EVALUACIÓN EN LA EDUCACIÓN SUPERIOR COMO RECURSO PARA LA INDIVIDUALIZACIÓN, LA INCLUSIÓN Y LA SOCIALIZACIÓN -- CAPÍTULO 49 ALFABETIZACIÓN MEDIÁTICA E INFORMACIONAL (AMI) EN LOS CENTROS EDUCATIVOS PARA DETECTAR Y COMBATIR RUMORES EN TIEMPOS DE CRISIS EN REDES SOCIALES: VIRALIDAD, INTELIGENCIA ARTIFICIAL Y OTROS RIESGOS -- CAPÍTULO 50 LA INTERRELACIÓN ENTRE EL DISEÑO INSTRUCCIONAL Y LA LITERACIDAD COMO UNA FORMA DE INNOVACIÓN CURRICULAR -- CAPÍTULO 51 LA MIRADA HACIA LA DIVERSIDAD DEL ALUMNADO DE EDUCACIÓN INFANTIL: LA POBREZA COMO INVISIBLIDAD -- CAPÍTULO 52 DIME DE QUÉ TE ACUERDAS Y TE DIRÉ CÓMO TE ENSEÑARON Y CÓMO APRENDISTE -- CAPÍTULO 53 DE LA ENSEÑANZA AL APRENDIZAJE EN INVESTIGACIÓN EN EL GRADO DE TRABAJO SOCIAL -- CAPÍTULO 54 PRIORIDADES EN LA FORMACIÓN DEL PROFESORADO DE MÚSICA DE NIVELES NO UNIVERSITARIOS -- CAPÍTULO 55 INNOVACIÓN DOCENTE EN EL ÁMBITO DE LA CRIMINOLOGÍA: JUSTICIA Y DERECHO A LA REEDUCACIÓN. -- CAPÍTULO 56 CUANDO LOS OJOS TIENEN VOZ: LA PHOTOVOICE COMO MÉTODO DE INVESTIGACIÓN SOCIAL PARTICIPATIVA -- CAPÍTULO 57 IMPLICACIONES COGNITIVAS DEL USO DE LA TECNOLOGÍA EN MENORES DESDE LA NEUROEDUCACIÓN -- CAPÍTULO 59 LUCES LARGAS: LA MIRADA CUALITATIVA DE APRENDICES: EXTRAÑAMIENTO, INVESTIGACIÓN Y ACUERPAMIENTO…”

Tabla de Contenidos: “…Chapter 11: Backend Architecture -- API -- RESTful API -- HTTP request methods -- GET -- POST -- PUT -- DELETE -- Authentication -- Basic authentication -- JSON Web Token -- Header -- Payload -- Signature -- Practical -- RESTful web service -- Java client -- Redis -- Installation -- Redis server -- Redis client -- Working with Redis -- Redis data types and structures -- String -- HashMap -- List -- Set -- Redis Publish/Subscribe -- Common key operations -- Summary -- Chapter 12: Machine Learning -- Machine learning -- Types of algorithms -- Parametric algorithms -- Non-parametric algorithms -- Supervised learning -- The classification model -- Binary classification -- Multi-class classification -- The regression model -- Linear regression -- Polynomial regression -- Unsupervised learning -- Clustering, k-means -- Neural networks -- Feedforward neural network -- Recurrent neural network -- Symmetrically connected neural network -- Deep neural networks -- Decision tree classifiers -- Summary -- Chapter 13: Artificial Intelligence -- Artificial intelligence -- Convolutional neural networks -- Deep learning using TensorFlow -- TensorFlow -- Installation -- TensorFlow program -- Uninstalling TensorFlow -- TensorBoard -- Program -- Launching TensorBoard -- TensorBoard graph -- Object detection using YOLO -- Installation -- Compiling YOLO library -- Trained weights -- Detecting objects in an image -- Summary -- Chapter 14: Elasticsearch -- Installing Elasticsearch -- Starting the Elasticsearch server -- Auto starting the Elasticsearch service -- Stopping the Elasticsearch server -- Uninstalling Elasticsearch -- Kibana -- Installation -- Starting Kibana -- Uninstalling Kibana -- Security -- Securing Elasticsearch -- Securing Kibana -- Understanding queries - CRUD commands -- Creating -- Reading -- Updating -- Deleting -- Summary…”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Packt is Searching for Authors Like You -- Table of Contents -- Preface -- Chapter 1: Giving Computers the Ability to Learn from Data -- Building intelligent machines to transform data into knowledge -- The three different types of machine learning -- Making predictions about the future with supervised learning -- Classification for predicting class labels -- Regression for predicting continuous outcomes -- Solving interactive problems with reinforcement learning -- Discovering hidden structures with unsupervised learning -- Finding subgroups with clustering -- Dimensionality reduction for data compression -- Introduction to the basic terminology and notations -- A roadmap for building machine learning systems -- Preprocessing - getting data into shape -- Training and selecting a predictive model -- Evaluating models and predicting unseen data instances -- Using Python for machine learning -- Installing Python and packages from the Python Package Index -- Using the Anaconda Python distribution and package manager -- Packages for scientific computing, data science, and machine learning -- Summary -- Chapter 2: Training Simple Machine Learning Algorithms for Classification -- Artificial neurons - a brief glimpse into the early history of machine learning -- The formal definition of an artificial neuron -- The perceptron learning rule -- Implementing a perceptron learning algorithm in Python -- An object-oriented perceptron API -- Training a perceptron model on the Iris dataset -- Adaptive linear neurons and the convergence of learning -- Minimizing cost functions with gradient descent -- Implementing Adaline in Python -- Improving gradient descent through feature scaling -- Large-scale machine learning and stochastic gradient descent -- Summary…”

Tabla de Contenidos: “…-- Programming language APIs -- Blockchain-related APIs -- Blockchain APIs -- Wallet APIs -- Smart contract APIs -- Digital payment APIs -- Geolocation APIs -- Uses of geolocation APIs -- The technology behind geolocation APIs -- Geolocation API performance metrics -- Integration of geolocation APIs -- Challenges with geolocation APIs -- Mapping APIs -- Messaging APIs -- Artificial Intelligence (AI) APIs -- Voice and audio APIs -- Vision APIs -- Examples of API uses in the Metaverse -- Social interaction APIs…”

Tabla de Contenidos: “…Internet de las cosas -- El Big Data y la inteligencia artificial -- Nuevas culturas digitales -- El marketing 4.0 -- ¿Estás listo para la era 4.0? …”

“…Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. …”

“…It is useful as a general introduction to artificial intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. …”

“…Previously known as Audio Explained, this latest edition includes new material on: reverberation and its use in recording; principles of digital mixing; digital recording; including MiniDisc and MP3; digital artificial reverberation.Designed with the student in mind, information is organised according to level of difficulty. …”

Tabla de Contenidos: “…Chapter 5: Google Cloud Solutions for Data Management -- Understanding the data journey -- Acme e-commerce - case study -- Exploring types of data -- Elaborating on Google Cloud solutions for data management -- Data pipelines -- Data lakes -- Data warehouses -- Business intelligence tools -- Summary -- Chapter 6: Machine Learning and Artificial Intelligence on Google Cloud -- AI overview and Google's contribution -- Considerations when building AI models -- Defining the problem -- Collecting the datasets -- Preparing the data -- Select the model and train it -- Evaluating the model -- Deploying the model -- Monitoring the model -- Google Cloud solutions for ML and AI -- Vertex AI and its associated services -- ML APIs -- AI business solutions -- Generative AI -- PaLM for text -- PaLM for chat -- Chirp for speech -- Imagen for text to image -- The embeddings API for text to image -- Codey -- Google AI Studio -- Gemini -- Summary -- Part 3: Infrastructure and Platform Modernization -- Chapter 7: Modernizing IT Infrastructure with Google Cloud -- The challenges with legacy infrastructure in the modern world -- Acme Inc. - buying servers -- The value of modernizing infrastructure with cloud technologies -- Agility -- Scalability -- Resilience -- Security -- The differences between hybrid and multi-cloud -- The evolution of computing from VMs to containers to serverless -- Summary -- Chapter 8: Modernizing Applications with Google Cloud -- The evolution of application development -- Acme Inc-development case study -- The benefits of cloud-native application development -- Google Cloud solutions for hosting applications -- Google Compute Engine (GCE) -- Google Cloud VMware Engine (GCVE) -- Sole-tenant nodes -- Kubernetes Engine (GKE) -- Cloud Run -- Artifact Registry -- Cloud Build -- App Engine -- Cloud Functions -- Summary -- Chapter 9: The Value of APIs…”

“…Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence. This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. …”

“…The book starts by building on established theory, and applying cutting edge research to it, such as artificial intelligence models and deep learning. It looks at traditional reliability engineering methods through their relation to Prognostics and Health Management (PHM) requirements and presents the concept of RUL loss rate. …”

Tabla de Contenidos: “…Cover -- Contents -- Syllabus -- Preface -- Chapter 1: Introduction to Computers -- 1.1 Introduction -- 1.2 Characteristics of Computers -- 1.3 Development of computers -- 1.3.1 Some Early Computers -- 1.4 Generations of Computers -- 1.4.1 First Generation (1940-56): Vacuum Tubes -- 1.4.2 Second Generation (1956-63): Transistors -- 1.4.3 Third Generation (1964-Early 1970s): Integrated Circuits -- 1.4.4 Fourth Generation (Early 1970s-Till Date): Microprocessors -- 1.4.5 Fifth Generation (Present and Beyond): Artificial Intelligence -- 1.5 Classification of Computers -- 1.5.1 Classification According to Purpose -- 1.5.2 Classification According to Type of Data-Handled Techniques -- 1.5.3 Classification According to Functionality -- 1.6 The Computer System -- 1.6.1 Basic Components of a Computer System -- 1.7 Types of Input Devices -- 1.7.1 Keyboard -- 1.7.2 P ointing Devices -- 1.7.3 D igital Camera -- 1.7.4 Scanners -- 1.7.5 Optical Character Recognition (OCR) -- 1.7.6 Optical Mark Recognition (OMR ) -- 1.7.7 Magnetic Ink Character Reader (MICR) -- 1.7.8 Bar Code Reader -- 1.8 Types of Output Devices -- 1.8.1 Printers -- 1.8.2 Plotters -- 1.8.3 Computer Monitor -- 1.9 Types of memor y -- 1.9.1 Primary Memory -- 1.9.2 Secondary Memory -- 1.10 Applications of Computer -- Let us Summarise -- Exercises -- Answer -- Chapter 2: Number Systems and Computer Codes -- 2.1 Introduction -- 2.2 Number system -- 2.2.1 Types of Number Systems -- 2.2.2 Conversion Between Number Bases -- 2.3 Computer Arithmetic -- 2.3.1 Binary Arithmetic -- 2.3.2 Octal Arithmetic -- 2.3.3 Hexadecimal Arithmetic -- 2.4 Coding Schemes -- 2.4.1 BCD -- 2.4.2 ASCII -- 2.4.3 EBCDIC -- Let us Summarise -- Exercises -- Answer -- Chapter 3: Microsoft Office Excel 2003 -- 3.1 Introduction -- 3.2 Starting Microsoft Office Excel 2003 -- 3.2.1 Microsoft Excel Environment…”

“…Los tres años de análisis profundo del impacto individual de las tecnologías tan disruptivas como las estudiadas en los Informes previos de la primera fase del Proyecto —Redes Sociales (2016), Internet of Things (2017), Robótica (2018), Inteligencia Artificial (2018), Economía de Plataformas y Blockchain (2019) e Informe General (2019)—, nos ha otorgado una posición de privilegio para poder llevar a cabo un proceso de reflexión general de lo que repetidamente venimos denominando como el impacto de la confluencia o convergencia de las nuevas tecnologías en los modelos de negocio de las organizaciones. …”

“…Tal y como explican en este libro divulgativo y accesible, lejos de la oscuridad académica o cientifista, la muerte será opcional hacia 2045 gracias a los avances exponenciales en inteligencia artificial, regeneración de tejidos, tratamientos con células madre, impresión de órganos, criopreservación, terapias genéticas o inmunológicas que resolverán -resuelven ya- el problema del envejecimiento del cuerpo humano. …”

Tabla de Contenidos: “…5.1.6 MÁS SOBRE CONTENIDO ENRIQUECIDO -- 5.2 NUMPY -- 5.2.1 NUMPY.ARANGE -- 5.2.2 NUMPY.LINSPACE -- 5.2.3 HISTOGRAMAS -- 5.2.4 DIAGRAMA DE DISPERSIÓN -- 5.3 MATPLOTLIB -- 5.3.1 INSTALACIÓN DE MATPLOTLIB -- 5.3.2 PYPLOT -- 5.3.3 SEABORN Y PANDAS -- CAPÍTULO 6. INTELIGENCIA ARTIFICIAL. DATA (...) -- 6.1 MACHINE LEARNING -- 6.1.1 LIBRERÍAS UTILIZADAS -- 6.1.2 ALGORITMOS DE MANCHINE LEARNING -- 6.1.3 APRENDIZAJE SUPERVISADO -- 6.1.4 APRENDIZAJE NO SUPERVISADO -- 6.1.5 APRENDIZAJE POR REFUERZO -- 6.1.6 APRENDIZAJE EVOLUTIVO -- 6.2 MÁQUINA INFERENCIAL -- 6.2.1 WEB SEMÁNTICA -- 6.2.2 RAZONADOR -- 6.2.3 LENGUAJES DE LA WEB SEMÁNTICA -- 6.2.4 IMPLEMENTAR UN RAZONADOR EN LA WEB (...) -- 6.2.5 PELLET -- 6.2.6 JENA -- 6.2.7 WEB SCRAPING -- CAPÍTULO 7. …”

Tabla de Contenidos: “…5.2.2.2 Limit and trend checking -- 5.2.2.3 Partial least squares regression -- 5.2.2.4 Bayesian network -- 5.2.2.5 Artificial neural network -- 5.2.2.6 K-means clustering -- 5.2.2.7 Attribute oriented induction -- 5.2.2.8 Hidden Markov model -- 5.3 Remaining Useful Life Identification of Wearing Components -- 5.3.1 Theoretical Background -- 5.3.2 Techniques Catalogue -- 5.3.3 Physical Modelling -- 5.3.3.1 Industrial automation -- 5.3.3.2 Fleet's maintenance -- 5.3.3.3 Eolic systems -- 5.3.3.4 Medical systems -- 5.3.4 Artificial Neural Networks -- 5.3.4.1 Deep neural networks -- 5.3.5 Life Expectancy Models -- 5.3.5.1 Time series analysis with attribute oriented induction -- 5.3.5.2 Application to a pump -- 5.3.5.3 Application to industrial forklifts -- 5.3.5.4 Application to a gearbox -- 5.3.6 Expert Systems -- 5.4 Alerting and Prediction of Failures -- 5.4.1 Theoretical Background -- 5.4.2 Techniques Catalogue -- 5.4.2.1 Nearest neighbour cold-deck imputation -- 5.4.2.2 Support vector machine -- 5.4.2.3 Linear discriminant analysis -- 5.4.2.4 Pattern mining -- 5.4.2.5 Temporal pattern mining -- 5.4.2.6 Principal component analysis -- 5.4.2.7 Hidden Semi-Markov model with Bayes classification -- 5.4.2.8 Autoencoders -- 5.4.2.9 Convolutional neural network with Gramian angular fields -- 5.4.2.10 Recurrent neural network with long-short-term memory -- 5.4.2.11 Change detection algorithm -- 5.4.2.12 Fisher's exact test -- 5.4.2.13 Bonferroni correction -- 5.4.2.14 Hypothesis testing using univariate parametric statistics -- 5.4.2.15 Hypothesis testing using univariate non-parametricstatistics -- 5.4.2.16 Mean, thresholds, normality tests -- 5.5 Examples -- 5.5.1 Usage Patterns/k-means -- 5.5.1.1 Data analysis -- 5.5.1.2 Results -- 5.5.1.2.1 Plotting -- 5.5.1.2.2 Replicability of results -- 5.5.1.2.3 Summary of results…”