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Blockchain Technology for Industry 4.0 [[electronic resource] ] : Secure, Decentralized, Distributed and Trusted Industry Environment / / edited by Rodrigo da Rosa Righi, Antonio Marcos Alberti, Madhusudan Singh
| Blockchain Technology for Industry 4.0 [[electronic resource] ] : Secure, Decentralized, Distributed and Trusted Industry Environment / / edited by Rodrigo da Rosa Righi, Antonio Marcos Alberti, Madhusudan Singh |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (V, 164 p. 50 illus., 40 illus. in color.) |
| Disciplina | 005.824 |
| Collana | Blockchain Technologies |
| Soggetto topico |
Computer engineering
Internet of things Embedded computer systems Data protection Industrial engineering Production engineering Information technology Business—Data processing Electrical engineering Big data Cyber-physical systems, IoT Security Industrial and Production Engineering IT in Business Communications Engineering, Networks Big Data |
| ISBN | 981-15-1137-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. History of Industrial Revolutions: From Homo Sapiens Hunters to Bitcoin Hunters -- 2. Blockchain and Industry 4.0: Overview, Convergence, and Analysis -- 3. Blockchain Technology for Data Management in Industry 4.0 -- 4. Secure Smart Contract Generation based on Petri Nets -- 5. Blockchain for Machine to Machine Interaction in Industry 4.0 -- 6. Blockchain-based Crowdfunding -- 7. Engineering 4.0: Future with disruptive technologies -- 8. The Opportunities of Blockchain in Health 4.0. |
| Record Nr. | UNINA-9910373897003321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Blockchain Technology for Smart Cities [[electronic resource] /] / edited by Dhananjay Singh, Navin Singh Rajput
| Blockchain Technology for Smart Cities [[electronic resource] /] / edited by Dhananjay Singh, Navin Singh Rajput |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (V, 180 p. 76 illus., 61 illus. in color.) |
| Disciplina | 005.824 |
| Collana | Blockchain Technologies |
| Soggetto topico |
Computer engineering
Internet of things Embedded computer systems Information technology Business—Data processing Urban economics Health informatics Applied mathematics Engineering mathematics Big data Cyber-physical systems, IoT IT in Business Urban Economics Health Informatics Mathematical and Computational Engineering Big Data |
| ISBN | 981-15-2205-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introducing Blockchain for Smart City Technologies and Applications -- 2. Chain of Antichains: An Efficient and Secure Distributed Ledger -- 3. Blockchain for intelligent gas monitoring in smart city scenario -- 4. Commodity Ecology: from Smart Cities to Smart Regions via a Blockchain-Based Virtual Community Platform for Ecological Design in Choosing All Materials and Wastes -- 5. Toward Multiple Layered Blockchain Structure for Tracking of Private Contents and Right to be Forgotten -- 6. Smart City Transportation Technologies: Automatic No-Helmet Penalizing System -- 7. An Overview of Smart City: Observation, Technologies, Challenges and Blockchain Applications -- 8. An Architecture for e-Health Recommender Systems based on Similarity of Patients’ Symptoms. |
| Record Nr. | UNINA-9910377823003321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Building Intelligent Systems [[electronic resource] ] : A Guide to Machine Learning Engineering / / by Geoff Hulten
| Building Intelligent Systems [[electronic resource] ] : A Guide to Machine Learning Engineering / / by Geoff Hulten |
| Autore | Hulten Geoff |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2018 |
| Descrizione fisica | 1 online resource (346 pages) |
| Disciplina | 006.37 |
| Soggetto topico |
Artificial intelligence
Big data Artificial Intelligence Big Data |
| ISBN | 1-4842-3432-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part 1: Approaching an Intelligent System Project -- Chapter 1: Introducing Intelligent Systems -- Chapter 2: Knowing When to Use Intelligent Systems -- Chapter 3: A Brief Refresher on Working with Data -- Chapter 4: Defining the Intelligent System's Goals -- Part 2: Intelligent Experiences -- Chapter 5: The Components of Intelligent Experiences -- Chapter 6: Why Creating Intelligence Experiences Is Hard -- Chapter 7: Balancing Intelligent Experiences -- Chapter 8: Modes of Intelligent Interaction -- Chapter 9: Getting Data from Experience -- Chapter 10: Verifying Intelligent Experiences -- Part 3: Implementing Intelligence -- Chapter 11: The Components of an Intelligence Implementation -- Chapter 12: The Intelligence Runtime -- Chapter 13: Where Intelligence Lives -- Chapter 14: Intelligence Management -- Chapter 15: Intelligent Telemetry -- Part 4: Creating Intelligence -- Chapter 16: Overview of Intelligence -- Chapter 17: Representing Intelligence -- Chapter 18: The Intelligence Creation Process -- Chapter 19: Evaluating Intelligence -- Chapter 20: Machine Learning Intelligence -- Chapter 21: Organizing Intelligence -- Part 5: Orchestrating Intelligent Systems -- Chapter 22: Overview of Intelligence Orchestration -- Chapter 23: The Intelligence Orchestration Environment -- Chapter 24: Dealing with Mistakes -- Chapter 25: Adversaries and Abuse -- Chapter 26: Approaching Your Own Intelligent System -- . |
| Record Nr. | UNINA-9910300361903321 |
Hulten Geoff
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Building Machine Learning and Deep Learning Models on Google Cloud Platform [[electronic resource] ] : A Comprehensive Guide for Beginners / / by Ekaba Bisong
| Building Machine Learning and Deep Learning Models on Google Cloud Platform [[electronic resource] ] : A Comprehensive Guide for Beginners / / by Ekaba Bisong |
| Autore | Bisong Ekaba |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019 |
| Descrizione fisica | 1 online resource (XXIX, 709 p. 348 illus., 344 illus. in color.) |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Big data Artificial Intelligence Big Data |
| ISBN | 1-4842-4470-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part 1: Getting Started with Google Cloud Platform -- Chapter 1: What Is Cloud Computing? -- Chapter 2: An Overview of Google Cloud Platform Services -- Chapter 3: The Google Cloud SDK and Web CLI -- Chapter 4: Google Cloud Storage (GCS) -- Chapter 5: Google Compute Engine (GCE) -- Chapter 6: JupyterLab Notebooks -- Chapter 7: Google Colaboratory -- Part 2: Programming Foundations for Data Science -- Chapter 8: What is Data Science? -- Chapter 9: Python -- Chapter 10: Numpy -- Chapter 11: Pandas -- Chapter 12: Matplotlib and Seaborn -- Part 3: Introducing Machine Learning -- Chapter 13: What Is Machine Learning? -- Chapter 14: Principles of Learning -- Chapter 15: Batch vs. Online Learning -- Chapter 16: Optimization for Machine Learning: Gradient Descent -- Chapter 17: Learning Algorithms -- Part 4: Machine Learning in Practice -- Chapter 18: Introduction to Scikit-learn -- Chapter 19: Linear Regression -- Chapter 20: Logistic Regression -- Chapter 21: Regularization for Linear Models -- Chapter 22: Support Vector Machines -- Chapter 23: Ensemble Methods -- Chapter 24: More Supervised Machine Learning Techniques with Scikit-learn -- Chapter 25: Clustering -- Chapter 26: Principal Components Analysis (PCA) -- Part 5: Introducing Deep Learning -- Chapter 27: What is Deep Learning? -- Chapter 28: Neural Network Foundations -- Chapter 29: Training a Neural Network -- Part 6: Deep Learning in Practice -- Chapter 30: TensorFlow 2.0 and Keras -- Chapter 31: The Multilayer Perceptron (MLP) -- Chapter 32: Other Considerations for Training the Network -- Chapter 33: More on Optimization Techniques -- Chapter 34: Regularization for Deep Learning -- Chapter 35: Convolutional Neural Networks (CNN) -- Chapter 36: Recurrent Neural Networks (RNN) -- Chapter 37: Autoencoders -- Part 7: Advanced Analytics/ Machine Learning on Google Cloud Platform -- Chapter 38: Google BigQuery -- Chapter 39: Google Cloud Dataprep -- Chapter 40: Google Cloud Dataflow -- Chapter 41: Google Cloud Machine Learning Engine (Cloud MLE) -- Chapter 42: Google AutoML: Cloud Vision -- Chapter 43: Google AutoML: Cloud Natural Language Processing -- Chapter 44: Model to Predict the Critical Temperature of Superconductors -- Part 8: Productionalizing Machine Learning Solutions on GCP -- Chapter 45: Containers and Google Kubernetes Engine -- Chapter 46: Kubeflow and Kubeflow Pipelines -- Chapter 47: Deploying an End-to-End Machine Learning Solution on Kubeflow Pipelines -- . |
| Record Nr. | UNINA-9910349528303321 |
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Bisong Ekaba
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Building Scalable PHP Web Applications Using the Cloud [[electronic resource] ] : A Simple Guide to Programming and Administering Cloud-Based Applications / / by Jonathan Bartlett
| Building Scalable PHP Web Applications Using the Cloud [[electronic resource] ] : A Simple Guide to Programming and Administering Cloud-Based Applications / / by Jonathan Bartlett |
| Autore | Bartlett Jonathan |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019 |
| Descrizione fisica | 1 online resource (209 pages) |
| Disciplina | 005.276 |
| Soggetto topico |
Computer programming
Programming languages (Electronic computers) Big data Web Development Programming Languages, Compilers, Interpreters Big Data |
| ISBN | 1-4842-5212-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction -- 2. What is the Cloud -- 3. Setting up a Cloud Server -- 4. Creating a Simple Web App -- 5. Setting Up a Basic Cloud Cluster -- 6. Improving Scalability with Caching -- 7. Database Replication -- 8. Using a Content Delivery Network -- 9. Using S3 for Infinite Disk Space -- 10. Hosting with AWS -- 11. Using the Google Cloud Platform -- 12. Server Management Techniques -- 13. Linux Security Basics -- Afterword -- A. Linux Commands -- B. Important Files and Directories -- C. What to Do When It Doesn't Work. |
| Record Nr. | UNINA-9910357836103321 |
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Bartlett Jonathan
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Business Analytics Using R - A Practical Approach [[electronic resource] /] / by Umesh R Hodeghatta, Umesha Nayak
| Business Analytics Using R - A Practical Approach [[electronic resource] /] / by Umesh R Hodeghatta, Umesha Nayak |
| Autore | Hodeghatta Umesh R |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2017 |
| Descrizione fisica | 1 online resource (XVII, 280 p. 278 illus.) |
| Disciplina | 658.054 |
| Soggetto topico |
Big data
Computer programming Programming languages (Electronic computers) Data mining Information storage and retrieval Mathematical statistics R (Computer program language) Big Data Programming Techniques Programming Languages, Compilers, Interpreters Data Mining and Knowledge Discovery Information Storage and Retrieval Probability and Statistics in Computer Science |
| ISBN | 1-4842-2514-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Overview of business analytics -- Introduction to R -- R for data analysis -- Introduction to descriptive analytics -- Business analytics process and data exploration -- Supervised machine learning : classification -- Unsupervised machine learning -- Simple linear regression -- Multiple linear regression -- Logistic regression -- Big data analysis : introduction and future trends. |
| Record Nr. | UNINA-9910157379203321 |
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Hodeghatta Umesh R
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Business Case Analysis with R [[electronic resource] ] : Simulation Tutorials to Support Complex Business Decisions / / by Robert D. Brown III
| Business Case Analysis with R [[electronic resource] ] : Simulation Tutorials to Support Complex Business Decisions / / by Robert D. Brown III |
| Autore | Brown Robert D., III |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2018 |
| Descrizione fisica | 1 online resource (287 pages) |
| Disciplina | 519.502855133 |
| Soggetto topico |
Big data
Big Data Big Data/Analytics |
| ISBN | 1-4842-3495-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part 1: Business Case Analysis with R -- Chapter 1: A Relief from Spreadsheet Misery -- Chapter 2: Setting up the Analysis -- Chapter 3: Include Uncertainty in the Financial Analysis -- Chapter 4: Interpreting and Communicating Insights -- Part 2: It’s Your Move -- Chapter 5: "What Should I Do?" -- Chapter 6: Use a Decision Hierarchy to Categorize Decision Types -- Chapter 7: Tame Decision Complexity by Creating a Strategy Table -- Chapter 8: Clearly Communicate the Intentions of Decision Strategies -- Chapter 9: What Comes Next -- Part 3: Subject Matter Expert Elicitation Guide -- Chapter 10: “What’s Your Number, Pardner?” -- Chapter 11: Conducting SME Elicitations -- Chapter 12: Kinds of Biases -- Part 4: Information Espresso -- Chapter 13: Setting a Budget for Making Decisions Clearly -- Chapter 14: A More Refined Explanation of VOI -- Chapter 15: Building the Simulation in R -- Appendix A: Deterministic Model -- Appendix B: Risk Model -- Appendix C: Simulation and Finance Functions -- Appendix D: Decision Hierarchy and Strategy Table Templates -- Appendix E: VOI Code Samples -- . |
| Record Nr. | UNINA-9910300360903321 |
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Brown Robert D., III
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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CABology: Value of Cloud, Analytics and Big Data Trio Wave [[electronic resource] /] / by Nitin Upadhyay
| CABology: Value of Cloud, Analytics and Big Data Trio Wave [[electronic resource] /] / by Nitin Upadhyay |
| Autore | Upadhyay Nitin |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XIX, 151 p. 44 illus. in color.) |
| Disciplina | 004.6782 |
| Soggetto topico |
Big data
Application software Mathematical statistics Big Data Information Systems Applications (incl. Internet) Big Data/Analytics Probability and Statistics in Computer Science |
| ISBN | 981-10-8675-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. A Triology – Evolution and Now -- Chapter 2. CABology – What is in it? -- Chapter 3. CAB Framework – The Fabric -- Chapter 4. CABonomics - The True Value -- Chapter 5. CABevaluation – What is Right for Me? -- Chapter 6 . CABfication – The Explanation -- Chapter 7. CAB Proposition – The Way Forward -- Chapter 8. CAB Implications – The Affairs -- Chapter 9. CAB Control – The Power -- Chapter 10. CAB Success Stories. . |
| Record Nr. | UNINA-9910299302603321 |
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Upadhyay Nitin
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| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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CDOs in the Public Sector [[electronic resource] ] : Perspectives on Chief Digital Officers and Digital Transformation Strategies / / by Christian Schachtner
| CDOs in the Public Sector [[electronic resource] ] : Perspectives on Chief Digital Officers and Digital Transformation Strategies / / by Christian Schachtner |
| Autore | Schachtner Christian |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (64 pages) |
| Disciplina | 720 |
| Collana | SpringerBriefs in Applied Sciences and Technology |
| Soggetto topico |
Architecture
Transportation engineering Traffic engineering Political science Cooperating objects (Computer systems) Big data Cities, Countries, Regions Transportation Technology and Traffic Engineering Governance and Government Cyber-Physical Systems Big Data |
| ISBN | 3-031-54611-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Theoretical Foundations -- Methodological fields of action for digital transformation -- Derivation of an impact concept for CDOs in the public sector -- Validation of the findings on the basis of a CDO-supported sample strategy -- Conclusions -- Literature. . |
| Record Nr. | UNINA-9910842493303321 |
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Schachtner Christian
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Citation Analysis and Dynamics of Citation Networks [[electronic resource] /] / by Michael Golosovsky
| Citation Analysis and Dynamics of Citation Networks [[electronic resource] /] / by Michael Golosovsky |
| Autore | Golosovsky Michael |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XIV, 121 p. 53 illus., 52 illus. in color.) |
| Disciplina | 621 |
| Collana | Understanding Complex Systems |
| Soggetto topico |
Sociophysics
Econophysics System theory Big data Data-driven Science, Modeling and Theory Building Complex Systems Big Data Big Data/Analytics |
| ISBN | 3-030-28169-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter1: Introduction -- Chapter2: Complex network of scientific papers -- Chapter3: Stochastic modeling of references and citations -- Chapter4: Citation dynamics of individual papers -model calibration -- Chapter5: Model validation -- Chapter6: Comparison of citation dynamics for different disciplines -- Chapter7: Prediction of citation dynamics of individual papers -- Chapter8: Power-law citation distributions are not scale-free -- Chapter9: Comparison to existing models. |
| Record Nr. | UNINA-9910349507503321 |
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Golosovsky Michael
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Soggetto topico
-
Big Data
(422)
- Big data (416)
- Computational Intelligence (130)
- Artificial Intelligence (122)
- Artificial intelligence (122)